# kb

id: spacecraft-electric-propulsion-20260505-1
file: data/en.wikipedia.org/wiki/Spacecraft_electric_propulsion-1.md
category: reference
title: Spacecraft electric propulsion
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spacecraft_electric_propulsion
date_saved: 2026-05-05T06:37:27.109316+00:00
instance: kb-cron
parent: spacecraft-electric-propulsion-20260505-0
summary:
In West Germany, electric-propulsion development also proceeded from 1960 at German Aerospace Center (DLR) institutes in Stuttgart and Braunschweig and at the University of Giessen. At Gießen, Horst Löb's group began development of radio-frequency ion thrusters of the RIT type, which use radio frequency fields rather than physical electrodes to ionize propellant, starting with the conception, laboratory model, and first tests of the RIT-10; the prototype was further improved through the 1960s and transferred to industry for qualification in 1970. A June 1960 decree of the Central Committee and Council of Ministers (No. 715-296), declassified after the Soviet period, directed the development of "space electric rocket engines". This included ion and electroplasma thrusters with target specific impulse of 5,000-10,000 seconds, a measure of propellant efficiency, assigning work to OKB-1, the Kurchatov Institute, and other named bureaus as part of a broader 1960-1967 Soviet Union space development plan. In 1964, Ernst Stuhlinger published Ion Propulsion for Space Flight, characterized by Choueiri as the first comprehensive book on electric rocket technology, marking the field's transition into a serious engineering discipline. On 20 July 1964, two electrostatic ion engines were tested in space in the Space Electric Rocket Test (SERT I), and the


id: spacecraft-electric-propulsion-20260505-2
file: data/en.wikipedia.org/wiki/Spacecraft_electric_propulsion-2.md
category: reference
title: Spacecraft electric propulsion
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spacecraft_electric_propulsion
date_saved: 2026-05-05T06:37:27.109316+00:00
instance: kb-cron
parent: spacecraft-electric-propulsion-20260505-0
summary:
SMART-1, launched in 2003, demonstrated solar electric primary propulsion in flight for ESA and carried the Hall thruster system that had been developed from late-1990s European work on commercial electric-propulsion applications and deep-space mission preparation. While electric-propulsion research and deployment continued, new systems were also launched into space. Hayabusa was launched by the Japan Aerospace Exploration Agency in 2003, propelled by electrodeless plasma thruster technology. By 2012, more than 270 Hall-effect SPT units had operated on over 60 Russian spacecraft. NASA's Dawn became the first spacecraft to orbit an object in the main asteroid belt at Vesta in 2011, and the first to orbit a dwarf planet at Ceres in 2015. Its ion propulsion system made Dawn the only spacecraft ever to orbit two extraterrestrial destinations. ESA's GOCE in 2009 and JAXA's Super Low Altitude Test Satellite "TSUBAME" (2017-2019) marked later electric-propulsion milestones by demonstrating continuous drag compensation and ion-engine-supported super-low-altitude operations in very low Earth orbit. ESA and JAXA's BepiColombo, launched in 2018, marked a later major milestone in solar electric propulsion when its Solar Electric Propulsion System began in-flight commissioning in November 2018, in what ESA described as the first in-flight operation of the most powerful and highest-performance


id: spacecraft-electric-propulsion-20260505-3
file: data/en.wikipedia.org/wiki/Spacecraft_electric_propulsion-3.md
category: reference
title: Spacecraft electric propulsion
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spacecraft_electric_propulsion
date_saved: 2026-05-05T06:37:27.109316+00:00
instance: kb-cron
parent: spacecraft-electric-propulsion-20260505-0
summary:
Three families of electromagnetic thruster, pulsed plasma thrusters (PPTs), magnetoplasmadynamic thrusters (MPD), and pulsed inductive thrusters (PIT), rely on strong fields. The three differ in lifetime, efficiency, and power scaling, but share advantages common to electromagnetic acceleration: high specific impulse, precision suitable for satellite positioning, robustness, high power processing capability, and relatively simple system-level scaling with available spacecraft power. PPTs are the only electromagnetic thrusters used on operational satellites. Solid-propellant PPTs first flew in the Soviet Union in 1964 and in the United States in 1968; they initiate an arc discharge across a solid fluorinated polymer bar, ablating a small amount of propellant and accelerating it by the Lorentz body force. Their compact, low-power, pulsed configurations make them suited to satellite positioning and drag compensation, unlike later concepts that rely on inductive or steady-state operation. MPDs generate thrust through the Lorentz force produced by the interaction of discharge currents with self-induced or externally applied magnetic fields, and have been investigated for both quasi-steady and steady-state spaceflight applications. MPD thrusters have also flown in space in experimental regimes. The PIT concept originated in the late 1960s and evolved through successive experimental designs focused on performance scaling, circuit optimization, and propellant compatibility.


id: sports-science-20260505-0
file: data/en.wikipedia.org/wiki/Sports_science-0.md
category: reference
title: Sports science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sports_science
date_saved: 2026-05-05T06:37:28.413086+00:00
instance: kb-cron
parent: sports-science-20260505-0
summary:
Sports science is a discipline that studies how the healthy human body adapts during acute and long-term exercise, and how sports and physical activity promote health and performance from cellular to whole body perspectives. The study of sports science traditionally incorporates areas of physiology (exercise physiology), psychology (sport psychology), anatomy, biomechanics (sports biomechanics), biochemistry, and kinesiology. == Origins of exercise physiology == Sports science can trace its origins back to Ancient Greece. The noted ancient Greek physician Galen (131–201) wrote 87 detailed essays about improving health (proper nutrition), aerobic fitness, and strengthening muscles. New ideas upon the working and functioning of the human body emerged during the Renaissance as anatomists and physicians challenged the previously known theories. These spread with the implementation of the printed word, the result of Gutenberg's printing press in the 15th century. Allied with this was a large increase in academia in general, universities were forming all around the world. Importantly, these new scholars went beyond the simplistic notions of the early Greek physicians, and shed light upon the complexities of the circulatory, and digestive systems. Furthermore, by the middle of the 19th century, early medical schools (such as the Harvard Medical School, formed 1782) began


id: strategic-urban-planning-20260505-0
file: data/en.wikipedia.org/wiki/Strategic_urban_planning-0.md
category: reference
title: Strategic urban planning
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Strategic_urban_planning
date_saved: 2026-05-05T06:37:29.632192+00:00
instance: kb-cron
parent: strategic-urban-planning-20260505-0
summary:
Strategic Urban Planning (SUP) is a methodical approach aimed at shaping the future of urban areas. It involves setting clear objectives, coordinating public and private efforts, and adapting to new circumstances to enhance the living conditions of the affected citizens. SUP is not a new concept; it has been applied to various aspects of human activity, with notable figures such as Sun Tzu, Arthur Thomson, and Henry Mintzberg contributing to its development. Fifteen years of practice proved to be enough time for the technique to spread and for the first “Meeting of American and European cities for the Exchange of Experiences in Strategic Planning” to be organized. Institutions sponsoring the meeting, held in Barcelona in 1993, included the Inter-American Development Bank, the European Community Commission and the Iberoamerican Cooperation Institute. The cities of Amsterdam, Lisbon, Lille, Barcelona, Toronto and Santiago de Chile participated, among others. At that meeting it was demonstrated, along with other relevant aspects, that if cooperative processes are used in large cities in order to carry out strategic planning processes, and if a reasonable degree of comprehension is reached between the administration, businesses and an ample representation of social agents, organizational synergies will develop that will eventually


id: strategic-urban-planning-20260505-1
file: data/en.wikipedia.org/wiki/Strategic_urban_planning-1.md
category: reference
title: Strategic urban planning
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Strategic_urban_planning
date_saved: 2026-05-05T06:37:29.632192+00:00
instance: kb-cron
parent: strategic-urban-planning-20260505-0
summary:
=== Bottom-Up === There are clear differences between what could be called the traditional approach to Strategic planning and the emerging approach (Fernández Güell. Strategic planning of cities). Before Product predominance, now Process predominance Before Sector specific, now Integrated Before Normative, now Strategic Before Goal-oriented, now Cost-benefit oriented Before Urban-offer oriented, now Urban-demand oriented Before Subject to administrative limitations, now Supersedes administrative limitations and enters in Metropolitan areas Before Open participation, now Focused Participation Of course, in 2006 there was a clear evolution that attempted to adapt to changes, political sensitivities and even trends. In any case, this is a line of thought and action that takes full advantage of the experience of projects that have already been implemented. === Top-Down === Given that: The influence of each agent in the global process under consideration for implementation is yet to be determined; There are no generally accepted criteria when creating instruments for measuring progress or regression on the path toward achieving main goals; Cooperation processes among different agents within the city to carry out strategic planning are usually undertaken using a “framework” organisational structure which highlights differences; Both politics and outside events affect a large city; this line of research


id: superslow-process-20260505-0
file: data/en.wikipedia.org/wiki/Superslow_process-0.md
category: reference
title: Superslow process
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Superslow_process
date_saved: 2026-05-05T06:37:30.856777+00:00
instance: kb-cron
parent: superslow-process-20260505-0
summary:
Superslow processes are processes in which values change so little that their capture is very difficult because of their smallness in comparison with the measurement error. == Applications == Most of the time, the superslow processes lie beyond the scope of investigation due to the reason of their superslowness. Multiple gaps can be easily detected in biology, astronomy, physics, mechanics, economics, linguistics, ecology, gerontology, etc. Biology: Traditional scientific research in this area was focused on the describing some brain reactions. Mathematics: In mathematics, when the fluid flows through thin and long tubes it forms stagnation zones where the flow becomes almost immobile. If the ratio of tube length to its diameter is large, then the potential function and stream function are almost invariable on very extended areas. The situation seems uninteresting, but if we remember that these minor changes occur in the extra-long intervals, we see here a series of first-class tasks that require the development of special mathematical methods. Mathematics: Apriori information regarding the stagnation zones contributes to optimization of the computational process by replacing the unknown functions with the corresponding constants in such zones. Sometimes this makes it possible to significantly reduce the amount of computation, for example


id: formal-science-20260505-0
file: data/en.wikipedia.org/wiki/Formal_science-0.md
category: reference
title: Formal science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Formal_science
date_saved: 2026-05-05T06:37:32.623490+00:00
instance: kb-cron
parent: formal-science-20260505-0
summary:
Formal science is a branch of science studying disciplines concerned with abstract structures described by formal systems. Whereas the natural sciences and social sciences seek to characterize physical systems and social systems, respectively, using theoretical and empirical methods, the formal sciences use language tools concerned with characterizing abstract structures described by formal systems and the deductions that can be made from them. The formal sciences aid the natural and social sciences by providing information about the structures used to describe the physical world, and what inferences may be made about them. == Branches == Logic (also a branch of philosophy) Mathematics Statistics Theoretical computer science Artificial intelligence Game theory Systems theory Theoretical linguistics Decision theory Systems science Data science Information theory Computer science Cryptography == Differences from other sciences == One reason why mathematics enjoys special esteem, above all other sciences, is that its laws are absolutely certain and indisputable, while those of other sciences are to some extent debatable and in constant danger of being overthrown by newly discovered facts. Because of their non-empirical nature, formal sciences are construed by outlining a set of axioms and definitions from which other statements (theorems) are deduced. For this reason, in Rudolf


id: actuarial-science-20260505-0
file: data/en.wikipedia.org/wiki/Actuarial_science-0.md
category: reference
title: Actuarial science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Actuarial_science
date_saved: 2026-05-05T06:37:33.864508+00:00
instance: kb-cron
parent: actuarial-science-20260505-0
summary:
Actuarial science is the discipline that applies mathematical and statistical methods to assess risk in insurance, pension, finance, investment, psychology, medicine, and other industries and professions. Actuaries are professionals trained in this discipline. In many countries, actuaries must demonstrate their competence by passing a series of rigorous professional examinations focused in fields such as probability and predictive analysis. According to the U.S. News & World Report, their job often has to do with using mathematics to identify risk so they can mitigate risk. Actuarial science includes a number of interrelated subjects, including mathematics, probability theory, statistics, finance, economics, financial accounting and computer science. Historically, actuarial science used deterministic models in the construction of tables and premiums. The science has gone through revolutionary changes since the 1980s due to the proliferation of high speed computers and the union of stochastic actuarial models with modern financial theory. Many universities have undergraduate and graduate degree programs in actuarial science. In 2010, a study published by job search website CareerCast ranked actuary as the #1 job in the United States. The study used five key criteria to rank jobs: environment, income, employment outlook, physical demands, and stress. In 2024, U.S. News & World Report


id: actuarial-science-20260505-1
file: data/en.wikipedia.org/wiki/Actuarial_science-1.md
category: reference
title: Actuarial science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Actuarial_science
date_saved: 2026-05-05T06:37:33.864508+00:00
instance: kb-cron
parent: actuarial-science-20260505-0
summary:
=== Applications to other forms of insurance === Actuarial science is also applied to property, casualty, liability, and general insurance. In these forms of insurance, coverage is generally provided on a renewable period (such as a yearly). Coverage can be cancelled at the end of the period by either party. Property and casualty insurance companies tend to specialize because of the complexity and diversity of risks. One division is to organize around personal and commercial lines of insurance. Personal lines of insurance are for individuals and include fire, auto, homeowners, theft, and umbrella coverages. Commercial lines address the insurance needs of businesses and include property, business continuation, product liability, fleet/commercial vehicle, workers compensation, fidelity and surety, and D&O insurance. The insurance industry also provides coverage for exposures such as catastrophe, weather-related risks, earthquakes, patent infringement and other forms of corporate espionage, terrorism, and "one-of-a-kind" (e.g., satellite launch). Actuarial science provides data collection, measurement, estimating, forecasting, and valuation tools to provide financial and underwriting data for management to assess marketing opportunities and the nature of the risks. Actuarial science often helps to assess the overall risk from catastrophic events in relation to its underwriting capacity or surplus. In the reinsurance fields,


id: actuarial-science-20260505-2
file: data/en.wikipedia.org/wiki/Actuarial_science-2.md
category: reference
title: Actuarial science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Actuarial_science
date_saved: 2026-05-05T06:37:33.864508+00:00
instance: kb-cron
parent: actuarial-science-20260505-0
summary:
=== Pre-formalisation === Elementary mutual aid agreements and pensions arose in antiquity. Early in the Roman Empire, associations were formed to meet the expenses of burial, cremation, and monuments—precursors to burial insurance and friendly societies. A small sum was paid into a communal fund on a weekly basis, and upon the death of a member, the fund would cover the expenses of rites and burial. These societies sometimes sold shares in the building of columbāria, or burial vaults, owned by the fund—the precursor to mutual insurance companies. Other early examples of mutual surety and assurance pacts can be traced back to various forms of fellowship within the Saxon clans of England and their Germanic forebears, and to Celtic society. However, many of these earlier forms of surety and aid would often fail due to lack of understanding and knowledge. === Initial development === The 17th century was a period of advances in mathematics in Germany, France and England. At the same time there was a rapidly growing desire and need to place the valuation of personal risk on a more scientific basis. Independently of each other, compound interest was studied and probability theory emerged as a well-understood mathematical discipline. Another


id: analytics-20260505-0
file: data/en.wikipedia.org/wiki/Analytics-0.md
category: reference
title: Analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Analytics
date_saved: 2026-05-05T06:37:35.112766+00:00
instance: kb-cron
parent: analytics-20260505-0
summary:
Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. Analytics also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance. Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics. Analytics may apply to a variety of fields such as marketing, management, finance, online systems, information security, and software services. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics. According to International Data Corporation, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021. As per Gartner, the overall analytic platforms software market grew by $25.5 billion in 2020. == Analytics vs analysis == Data analysis focuses on the process of examining


id: analytics-20260505-1
file: data/en.wikipedia.org/wiki/Analytics-1.md
category: reference
title: Analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Analytics
date_saved: 2026-05-05T06:37:35.112766+00:00
instance: kb-cron
parent: analytics-20260505-0
summary:
=== People analytics === People analytics uses behavioral data to understand how people work and change how companies are managed. It can be referred to by various names, depending on the context, the purpose of the analytics, or the specific focus of the analysis. Some examples include workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and human resources information system (HRIS) analytics. HR analytics is the application of analytics to help companies manage human resources. HR analytics has become a strategic tool in analyzing and forecasting human-related trends in the changing labor markets, using career analytics tools. The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems. For example, inspection of the strategic phenomenon of employee turnover utilizing people analytics tools may serve as an important analysis at times of disruption. It has been suggested that people analytics is a separate discipline to HR analytics, with a greater focus on addressing business issues, while HR Analytics is more concerned with metrics related to HR processes. Additionally, people analytics may now extend beyond the human resources function in organizations. However, experts


id: analytics-20260505-2
file: data/en.wikipedia.org/wiki/Analytics-2.md
category: reference
title: Analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Analytics
date_saved: 2026-05-05T06:37:35.112766+00:00
instance: kb-cron
parent: analytics-20260505-0
summary:
== Challenges == In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly. The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation. Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments, and universities. These challenges are the current inspiration for much of the innovation in modern analytics information systems, giving birth to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation. One such innovation is the


id: artificial-intelligence-20260505-0
file: data/en.wikipedia.org/wiki/Artificial_intelligence-0.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in engineering, mathematics and computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. High-profile applications of AI include advanced web search engines, chatbots, virtual assistants, autonomous vehicles, and play and analysis in strategy games (e.g., chess and Go). Since the 2020s, generative AI has become widely available to generate images, audio, and videos from text prompts. The traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, and perception, as well as support for robotics. To reach these goals, AI researchers have used techniques including state space search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields. Some companies, such as OpenAI, Google DeepMind and Meta, aim to create artificial general intelligence (AGI) – AI that can complete virtually any cognitive task at


id: artificial-intelligence-20260505-1
file: data/en.wikipedia.org/wiki/Artificial_intelligence-1.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
=== Planning and decision-making === An "agent" is any entity (artificial or not) that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning, the agent has a specific goal. In automated decision-making, the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called the "utility") that measures how much the agent prefers it. For each possible action, it can calculate the "expected utility": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility. In classical planning, the agent knows exactly what the effect of any action will be. In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if


id: artificial-intelligence-20260505-2
file: data/en.wikipedia.org/wiki/Artificial_intelligence-2.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
==== State space search ==== State space search searches through a tree of possible states to try to find a goal state. For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. "Heuristics" or "rules of thumb" can help prioritize choices that are more likely to reach a goal. Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and countermoves, looking for a winning position. ==== Local search ==== Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally. Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train neural networks, through the backpropagation algorithm. Another type of local search is evolutionary


id: artificial-intelligence-20260505-3
file: data/en.wikipedia.org/wiki/Artificial_intelligence-3.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
An artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers. Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm. Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function. In feedforward neural networks the signal passes in only one direction. The term perceptron typically refers to a single-layer neural network. In contrast, deep learning uses many layers. Recurrent neural networks (RNNs) feed the output signal back into the input, which allows short-term memories of previous input events. Long short-term


id: artificial-intelligence-20260505-4
file: data/en.wikipedia.org/wiki/Artificial_intelligence-4.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Then, in 2017, it defeated Ke Jie, who was the best Go player in the world. Other programs handle imperfect-information games, such as the poker-playing program Pluribus. DeepMind developed increasingly generalistic reinforcement learning models, such as with MuZero, which could be trained to play chess, Go, or Atari games. In 2019, DeepMind's AlphaStar achieved grandmaster level in StarCraft II, a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map. In 2021, an AI agent competed in a PlayStation Gran Turismo competition, winning against four of the world's best Gran Turismo drivers using deep


id: artificial-intelligence-20260505-5
file: data/en.wikipedia.org/wiki/Artificial_intelligence-5.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright. AI-powered devices and services, such as virtual assistants and IoT products, continuously collect personal information, raising concerns about intrusive data gathering and unauthorized access by third parties. The loss of privacy is further exacerbated by AI's ability to process and combine vast amounts of data, potentially leading to a surveillance society where individual activities are constantly monitored and analyzed without adequate safeguards or transparency. Sensitive user data collected may include online activity records, geolocation data, video, or audio. For example, in order to build speech recognition algorithms, Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy. AI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy. Since 2016, some privacy


id: artificial-intelligence-20260505-6
file: data/en.wikipedia.org/wiki/Artificial_intelligence-6.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
Technology companies have built electricity and artificial intelligence infrastructure to facilitate the AI boom of the 2020s. A 2025 report from the consulting firm McKinsey & Company estimated that by 2030, $2.7 trillion would be invested into AI infrastructure and data centers in the US, surpassing World War II's Manhattan Project every month. In January 2024, the International Energy Agency (IEA) released Electricity 2024, Analysis and Forecast to 2026. This is the first IEA report to make projections for data centers and power consumption by AI and cryptocurrency. The report states that power demand for these uses might double by 2026, with the additional power consumption equaling that of Japan. Power consumption by AI is responsible for an increase in fossil fuel use, and has delayed closings of obsolete, carbon-emitting coal energy facilities. A ChatGPT search involves the use of 10 times the electrical energy as a Google search. A 2024 Goldman Sachs Research Paper, AI Data Centers and the Coming US Power Demand Surge, found "US power demand (is) likely to experience growth not seen in a generation...." and forecasts that, by 2030, US data centers will consume 8% of US power, as opposed to 3% in 2022, presaging


id: artificial-intelligence-20260505-7
file: data/en.wikipedia.org/wiki/Artificial_intelligence-7.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation. This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government. The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took some steps to mitigate the problem. In the early 2020s, generative AI began to create images, audio, and texts that are virtually indistinguishable from real photographs, recordings, or human writing, while realistic AI-generated videos became feasible in the mid-2020s. It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda; one such potential malicious use is deepfakes for


id: artificial-intelligence-20260505-8
file: data/en.wikipedia.org/wiki/Artificial_intelligence-8.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
==== Lack of transparency ==== Many AI systems are so complex that their designers cannot explain how they reach their decisions. Particularly with deep neural networks, in which there are many non-linear relationships between inputs and outputs. But some popular explainability techniques exist. It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as "cancerous", because pictures of malignancies typically include a ruler to show the scale. Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at "low risk" of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but


id: artificial-intelligence-20260505-9
file: data/en.wikipedia.org/wiki/Artificial_intelligence-9.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment. In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed. Risk estimates vary; for example, in the 2010s, Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk". The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies. In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence. Early-career workers showed decreasing employment rates in some AI-exposed occupations. Unlike previous waves of automation, many middle-class jobs may be eliminated by


id: artificial-intelligence-20260505-10
file: data/en.wikipedia.org/wiki/Artificial_intelligence-10.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
Recent public debates in artificial intelligence have increasingly focused on its broader societal and ethical implications. It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist Stephen Hawking stated, "spell the end of the human race". This scenario has been common in science fiction, when a computer or robot suddenly develops a human-like "self-awareness" (or "sentience" or "consciousness") and becomes a malevolent character. These sci-fi scenarios are misleading in several ways. First, AI does not require human-like sentience to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of an automated paperclip factory that destroys the world to get more iron for paperclips). Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that "you can't fetch the coffee if you're dead." In order to be safe for humanity, a superintelligence would have to be


id: artificial-intelligence-20260505-11
file: data/en.wikipedia.org/wiki/Artificial_intelligence-11.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
=== Frameworks === Artificial intelligence projects can be guided by ethical considerations during the design, development, and implementation of an AI system. An AI framework such as the Care and Act Framework, developed by the Alan Turing Institute and based on the SUM values, outlines four main ethical dimensions, defined as follows: Respect the dignity of individual people Connect with other people sincerely, openly, and inclusively Care for the wellbeing of everyone Protect social values, justice, and the public interest Other developments in ethical frameworks include those decided upon during the Asilomar Conference, the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others; however, these principles are not without criticism, especially regarding the people chosen to contribute to these frameworks. Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design, development and implementation, and collaboration between job roles such as data scientists, product managers, data engineers, domain experts, and delivery managers. The UK AI Safety Institute released in 2024 a testing toolset called 'Inspect' for AI safety evaluations available under an MIT open-source licence which is


id: artificial-intelligence-20260505-12
file: data/en.wikipedia.org/wiki/Artificial_intelligence-12.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable form of mathematical reasoning. This, along with concurrent discoveries in cybernetics, information theory and neurobiology, led researchers to consider the possibility of building an "electronic brain". They developed several areas of research that would become part of AI, such as McCulloch and Pitts design for "artificial neurons" in 1943, and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced the Turing test and showed that "machine intelligence" was plausible. The field of AI research was founded at a workshop at Dartmouth College in 1956. The attendees became the leaders of AI research in the 1960s. They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English. Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s. Researchers in the 1960s and the 1970s were convinced that their


id: artificial-intelligence-20260505-13
file: data/en.wikipedia.org/wiki/Artificial_intelligence-13.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
Philosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines. Another major focus has been whether machines can be conscious, and the associated ethical implications. Many other topics in philosophy are relevant to AI, such as epistemology and free will. Rapid advancements have intensified public discussions on the philosophy and ethics of AI. === Defining artificial intelligence === Alan Turing investigated whether machines can show intelligent behaviour and think. In 1950, he proposed the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks." Russell and Norvig agree with Turing that intelligence must be defined in terms of external behavior, not internal structure. However, they are critical that the test requires the machine to imitate humans. "Aeronautical engineering texts", they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool


id: artificial-intelligence-20260505-14
file: data/en.wikipedia.org/wiki/Artificial_intelligence-14.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals. General intelligence is difficult to define and difficult to measure, and modern AI has had more verifiable successes by focusing on specific problems with specific solutions. The sub-field of artificial general intelligence studies this area exclusively. === Machine consciousness, sentience, and mind === There is no settled consensus in philosophy of mind on whether a machine can have a mind, consciousness and mental states in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field: to build machines that can solve problems using intelligence. Russell and Norvig add that "[t]he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on." However, the question has become central to the philosophy of mind. It is also typically the central question


id: artificial-intelligence-20260505-15
file: data/en.wikipedia.org/wiki/Artificial_intelligence-15.md
category: reference
title: Artificial intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence
date_saved: 2026-05-05T06:37:36.472612+00:00
instance: kb-cron
parent: artificial-intelligence-20260505-0
summary:
Thought-capable artificial beings have appeared as storytelling devices since antiquity, and have been a persistent theme in science fiction. A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as Blade Runner (1982), The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture. Isaac Asimov introduced the Three Laws of Robotics in many stories, most notably with the "Multivac" super-intelligent computer. Asimov's laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity. Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to


id: artificial-intelligence-content-detection-20260505-0
file: data/en.wikipedia.org/wiki/Artificial_intelligence_content_detection-0.md
category: reference
title: Artificial intelligence content detection
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence_content_detection
date_saved: 2026-05-05T06:37:37.671214+00:00
instance: kb-cron
parent: artificial-intelligence-content-detection-20260505-0
summary:
Artificial intelligence detection software aims to determine whether some content (text, image, video or audio) was generated using artificial intelligence (AI). This software is often unreliable. == Accuracy issues == Many AI detection tools have been shown to be unreliable in detecting AI-generated text. In a 2023 study conducted by Weber-Wulff et al., researchers evaluated 14 detection tools including Turnitin and GPTZero and found that "all scored below 80% of accuracy and only 5 over 70%." They also found that these tools tend to have a bias for classifying texts more as human than as AI, and that accuracy of these tools worsens upon paraphrasing. === False positives === In AI content detection, a false positive is when human-written work is incorrectly flagged as AI-written. Many AI detection platforms claim to have a minimal level of false positives, with Turnitin claiming a less than 1% false positive rate. However, later research by The Washington Post produced much higher rates of 50%, though they used a smaller sample size. False positives in an academic setting frequently lead to accusations of academic misconduct, which can have serious consequences for a student's academic record. Additionally, studies have shown evidence that many AI detection


id: artificial-intelligence-content-detection-20260505-1
file: data/en.wikipedia.org/wiki/Artificial_intelligence_content_detection-1.md
category: reference
title: Artificial intelligence content detection
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_intelligence_content_detection
date_saved: 2026-05-05T06:37:37.671214+00:00
instance: kb-cron
parent: artificial-intelligence-content-detection-20260505-0
summary:
== Anti text detection == There is software available designed to bypass AI text detection. In practice, evasion may not require specialized bypass tools. Paraphrasing, style editing, and removal of repeated discourse markers can substantially reduce the effectiveness of detectors that rely on recognizable surface patterns. A study published in August 2023 analyzed 20 abstracts from papers published in the Eye Journal, which were then paraphrased using GPT-4.0. The AI-paraphrased abstracts were examined for plagiarism using QueText and for AI-generated content using Originality.AI. The texts were then re-processed through an adversarial software called Undetectable.ai in order to reduce the AI-detection scores. The study found that the AI detection tool, Originality.AI, identified text generated by GPT-4 with a mean accuracy of 91.3%. However, after reprocessing by Undetectable.ai, the detection accuracy of Originality.ai dropped to a mean accuracy of 27.8%. Some experts also believe that techniques like digital watermarking are ineffective because they can be removed or added to trigger false positives. "A Watermark for Large Language Models" paper by Kirchenbauer et al. (2023) also addresses potential vulnerabilities of watermarking techniques. The authors outline a range of adversarial tactics, including text insertion, deletion, and substitution attacks, that could be used to bypass


id: artificial-wisdom-20260505-0
file: data/en.wikipedia.org/wiki/Artificial_wisdom-0.md
category: reference
title: Artificial wisdom
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_wisdom
date_saved: 2026-05-05T06:37:38.994087+00:00
instance: kb-cron
parent: artificial-wisdom-20260505-0
summary:
Artificial wisdom (AW) is an artificial intelligence (AI) system which is able to display the human traits of wisdom and morals while being able to contemplate its own “endpoint”. Artificial wisdom can be described as artificial intelligence reaching the top-level of decision-making when confronted with the most complex challenging situations. The term artificial wisdom is used when the "intelligence" is based on more than by chance collecting and interpreting data, but by design enriched with smart and conscience strategies that wise people would use. == Overview == The goal of artificial wisdom is to create artificial intelligence that can successfully replicate the “uniquely human trait[s]” of having wisdom and morals as closely as possible. Thus, artificial wisdom, must “incorporate [the] ethical and moral considerations” of the data it uses. There are also many significant ethical and legal implications of AW which are compounded by the rapid advances in AI and related technologies alongside the lack of the development of ethics, guidelines, and regulations without the oversight of any kind of overarching advisory board. Additionally, there are challenges in how to develop, test, and implement AW in real world scenarios. Existing tests do not test the internal thought process by which


id: birkhoff-s-theorem-equational-logic-20260505-0
file: data/en.wikipedia.org/wiki/Birkhoff's_theorem_(equational_logic)-0.md
category: reference
title: Birkhoff's theorem (equational logic)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Birkhoff's_theorem_(equational_logic)
date_saved: 2026-05-05T06:37:40.203409+00:00
instance: kb-cron
parent: birkhoff-s-theorem-equational-logic-20260505-0
summary:
In logic, Birkhoff's theorem in equational logic states that an equality t = u is a semantic consequence of a set of equalities E, if and only if t = u can be proven from the set of equalities. It is named after Garrett Birkhoff. == References ==


id: computational-linguistics-20260505-0
file: data/en.wikipedia.org/wiki/Computational_linguistics-0.md
category: reference
title: Computational linguistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Computational_linguistics
date_saved: 2026-05-05T06:37:41.542518+00:00
instance: kb-cron
parent: computational-linguistics-20260505-0
summary:
Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others. Computational linguistics is closely related to mathematical linguistics. == Origins == The field overlapped with artificial intelligence since the efforts in the United States in the 1950s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English. Since rule-based approaches were able to make arithmetic (systematic) calculations much faster and more accurately than humans, it was expected that lexicon, morphology, syntax and semantics can be learned using explicit rules, as well. After the failure of rule-based approaches, David Hays coined the term in order to distinguish the field from AI and co-founded both the Association for Computational Linguistics (ACL) and the International Committee on Computational Linguistics (ICCL) in the 1970s and 1980s. What started as an effort to translate between languages evolved into a much wider field of natural language processing. == Annotated corpora == In order to be able to meticulously study the


id: computer-science-20260505-0
file: data/en.wikipedia.org/wiki/Computer_science-0.md
category: reference
title: Computer science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Computer_science
date_saved: 2026-05-05T06:37:42.731495+00:00
instance: kb-cron
parent: computer-science-20260505-0
summary:
Computer science is the study of computation, information, and automation. Included broadly in the sciences, computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software). An expert in the field is known as a computer scientist. Algorithms and data structures are central to computer science. The theory of computation concerns abstract models of computation and general classes of problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and preventing security vulnerabilities. Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of repositories of data. Human–computer interaction investigates the interfaces through which humans and computers interact, and software engineering focuses on the design and principles behind developing software. Areas such as operating systems, networks and embedded systems investigate the principles and design behind complex systems. Computer architecture describes the construction of computer components and computer-operated equipment. Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found


id: computer-science-20260505-1
file: data/en.wikipedia.org/wiki/Computer_science-1.md
category: reference
title: Computer science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Computer_science
date_saved: 2026-05-05T06:37:42.731495+00:00
instance: kb-cron
parent: computer-science-20260505-0
summary:
Although first proposed in 1956, the term "computer science" appears in a 1959 article in Communications of the ACM, in which Louis Fein argues for the creation of a Graduate School in Computer Sciences analogous to the creation of Harvard Business School in 1921. Louis justifies the name by arguing that, like management science, the subject is applied and interdisciplinary in nature, while having the characteristics typical of an academic discipline. This effort, and those of others such as numerical analyst George Forsythe, were successful, and universities went on to create such departments, starting with Purdue in 1962. Despite its name, a significant amount of computer science does not involve the study of computers themselves. Because of this, several alternative names have been proposed. Certain departments of major universities prefer the term computing science, to emphasize precisely that difference. Danish scientist Peter Naur suggested the term datalogy, to reflect the fact that the scientific discipline revolves around data and data treatment, while not necessarily involving computers. The first scientific institution to use the term was the Department of Datalogy at the University of Copenhagen, founded in 1969, with Peter Naur being the first professor in datalogy. The term is used


id: computer-science-20260505-2
file: data/en.wikipedia.org/wiki/Computer_science-2.md
category: reference
title: Computer science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Computer_science
date_saved: 2026-05-05T06:37:42.731495+00:00
instance: kb-cron
parent: computer-science-20260505-0
summary:
=== Epistemology of computer science === Despite the word science in its name, there is debate over whether or not computer science is a discipline of science, mathematics, or engineering. Allen Newell and Herbert A. Simon argued in 1975, Computer science is an empirical discipline. We would have called it an experimental science, but like astronomy, economics, and geology, some of its unique forms of observation and experience do not fit a narrow stereotype of the experimental method. Nonetheless, they are experiments. Each new machine that is built is an experiment. Actually constructing the machine poses a question to nature; and we listen for the answer by observing the machine in operation and analyzing it by all analytical and measurement means available. It has since been argued that computer science can be classified as an empirical science since it makes use of empirical testing to evaluate the correctness of programs, but a problem remains in defining the laws and theorems of computer science (if any exist) and defining the nature of experiments in computer science. Proponents of classifying computer science as an engineering discipline argue that the reliability of computational systems is investigated in the same way as bridges in


id: computer-science-20260505-3
file: data/en.wikipedia.org/wiki/Computer_science-3.md
category: reference
title: Computer science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Computer_science
date_saved: 2026-05-05T06:37:42.731495+00:00
instance: kb-cron
parent: computer-science-20260505-0
summary:
Programming language theory is a branch of computer science that deals with the design, implementation, analysis, characterization, and classification of programming languages and their individual features. It falls within the discipline of computer science, both depending on and affecting mathematics, software engineering, and linguistics. It is an active research area, with numerous dedicated academic journals. Formal methods are a particular kind of mathematically based technique for the specification, development and verification of software and hardware systems. The use of formal methods for software and hardware design is motivated by the expectation that, as in other engineering disciplines, performing appropriate mathematical analysis can contribute to the reliability and robustness of a design. They form an important theoretical underpinning for software engineering, especially where safety or security is involved. Formal methods are a useful adjunct to software testing since they help avoid errors and can also give a framework for testing. For industrial use, tool support is required. However, the high cost of using formal methods means that they are usually only used in the development of high-integrity and life-critical systems, where safety or security is of utmost importance. Formal methods are best described as the application of a fairly broad variety


id: computer-science-20260505-4
file: data/en.wikipedia.org/wiki/Computer_science-4.md
category: reference
title: Computer science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Computer_science
date_saved: 2026-05-05T06:37:42.731495+00:00
instance: kb-cron
parent: computer-science-20260505-0
summary:
Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other. A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi and the parallel random access machine model. When multiple computers are connected in a network while using concurrency, this is known as a distributed system. Computers within that distributed system have their own private memory, and information can be exchanged to achieve common goals. ==== Computer networks ==== This branch of computer science aims studies the construction and behavior of computer networks. It addresses their performance, resilience, security, scalability, and cost-effectiveness, along with the variety of services they can provide. ==== Computer security and cryptography ==== Computer security is a branch of computer technology with the objective of protecting information from unauthorized access, disruption, or modification while maintaining the accessibility and usability of the system for its intended users. Historical cryptography is the art of writing and deciphering secret messages. Modern cryptography is the scientific study of problems relating to distributed computations that can be attacked. Technologies studied in modern cryptography include symmetric and asymmetric encryption, digital signatures, cryptographic hash functions, key-agreement protocols, blockchain,


id: confrontation-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Confrontation_analysis-0.md
category: reference
title: Confrontation analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Confrontation_analysis
date_saved: 2026-05-05T06:37:44.023896+00:00
instance: kb-cron
parent: confrontation-analysis-20260505-0
summary:
Confrontation analysis (also known as dilemma analysis) is an operational analysis technique used to structure, understand, and analyze multi-party interactions, such as negotiations or conflicts. It serves as the mathematical foundation for drama theory. While based on game theory, confrontation analysis differs in that it focuses on the idea that players may redefine the game during the interaction, often due to the influence of emotions. In traditional game theory, players generally work within a fixed set of rules (represented by a decision matrix). However, confrontation analysis sees the interaction as a sequence of linked decisions, where the rules or perceptions of the game can shift over time, influenced by emotional dilemmas or psychological factors that arise during the interaction. == Derivation and use == Confrontation analysis was devised by Professor Nigel Howard in the early 1990s drawing from his work on game theory and metagame analysis. It has been turned to defence, political, legal, financial and commercial applications. Much of the theoretical background to General Rupert Smith's book The Utility of Force drew its inspiration from the theory of confrontation analysis.I am in debt to Professor Nigel Howard, whose explanation of Confrontation Analysis and Game Theory at a seminar in


id: confrontation-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Confrontation_analysis-1.md
category: reference
title: Confrontation analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Confrontation_analysis
date_saved: 2026-05-05T06:37:44.023896+00:00
instance: kb-cron
parent: confrontation-analysis-20260505-0
summary:
A second example on the left shows the other two dilemmas: The trust dilemma and the co-operation dilemma. This example is taken from lead-up to the 2003 Iraq war. Here the USA thought that Saddam Hussein was developing Weapons of Mass Destruction. Saddam said he was not, but the USA doubted this and thought that he was. They therefore invaded Iraq. Confrontation analysis does not necessarily produce a win-win solution (although end states are more likely to remain stable if they do); however, the word confrontation should not necessarily imply that any negotiations should be carried out in an aggressive way. The card tables are isomorphic to game theory models. The aim is to find the dilemmas facing participants and so help to forecast how the participants might change the options table to eliminate them. The forecast requires both analysis of the model and its dilemmas, and also exploration of the reality outside the model; both will show the strategies the participant might use change the options table to eliminate dilemmas. Sometimes analysis of the ticks and crosses can be supported by values showing the payoff to each of the parties. == References == == External links == ConfrontationAnalysis.info A


id: cryptography-20260505-0
file: data/en.wikipedia.org/wiki/Cryptography-0.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
Cryptography, or cryptology, is the practice and study of techniques for secure communication in the presence of adversarial behavior. More generally, cryptography is about constructing and analyzing protocols that prevent third parties or the public from reading private messages. Modern cryptography exists at the intersection of the disciplines of mathematics, computer science, information security, electrical engineering, digital signal processing, physics, and others. Core concepts related to information security (data confidentiality, data integrity, authentication and non-repudiation) are also central to cryptography. Practical applications of cryptography include electronic commerce, chip-based payment cards, digital currencies, computer passwords and military communications. Cryptography prior to the modern age was effectively synonymous with encryption, converting readable information (plaintext) to unintelligible nonsense text (ciphertext), which can only be read by reversing the process (decryption). The sender of an encrypted (coded) message shares the decryption (decoding) technique only with the intended recipients to preclude access from adversaries. The cryptography literature often uses the names "Alice" (or "A") for the sender, "Bob" (or "B") for the intended recipient, and "Eve" (or "E") for the eavesdropping adversary. Since the development of rotor cipher machines in World War I and the advent of computers in World War II, cryptography methods have


id: cryptography-20260505-1
file: data/en.wikipedia.org/wiki/Cryptography-1.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
The first use of the term "cryptograph" (as opposed to "cryptogram") dates back to the 19th century – originating from "The Gold-Bug", a story by Edgar Allan Poe. The etymology of the term goes back to the Greek language; it is formed by two constituents: "crypton" (hidden), and "grapho" (to write). Until modern times, cryptography referred almost exclusively to "encryption", which is the process of converting ordinary information (called plaintext) into an unintelligible form (called ciphertext). Decryption is the reverse, in other words, moving from the unintelligible ciphertext back to plaintext. A cipher (or cypher) is a pair of algorithms that carry out the encryption and the reversing decryption. The detailed operation of a cipher is controlled both by the algorithm and, in each instance, by a "key". The key is a secret (ideally known only to the communicants), usually a string of characters (ideally short so it can be remembered by the user), which is needed to decrypt the ciphertext. In formal mathematical terms, a "cryptosystem" is the ordered list of elements of finite possible plaintexts, finite possible cyphertexts, finite possible keys, and the encryption and decryption algorithms that correspond to each key. Keys are important both formally and


id: cryptography-20260505-2
file: data/en.wikipedia.org/wiki/Cryptography-2.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
The main classical cipher types are transposition ciphers, which rearrange the order of letters in a message (e.g., 'hello world' becomes 'ehlol owrdl' in a trivially simple rearrangement scheme), and substitution ciphers, which systematically replace letters or groups of letters with other letters or groups of letters (e.g., 'fly at once' becomes 'gmz bu podf' by replacing each letter with the one following it in the Latin alphabet). Simple versions of either have never offered much confidentiality from enterprising opponents. An early substitution cipher was the Caesar cipher, in which each letter in the plaintext was replaced by a letter three positions further down the alphabet. Suetonius reports that Julius Caesar used it with a shift of three to communicate with his generals. Atbash is an example of an early Hebrew cipher. The earliest known use of cryptography is some carved ciphertext on stone in Egypt (c. 1900 BCE), but this may have been done for the amusement of literate observers rather than as a way of concealing information. The Greeks of Classical times are said to have known of ciphers (e.g., the scytale transposition cipher claimed to have been used by the Spartan military). Steganography (i.e., hiding even the


id: cryptography-20260505-3
file: data/en.wikipedia.org/wiki/Cryptography-3.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
Language letter frequencies may offer little help for some extended historical encryption techniques such as homophonic cipher that tend to flatten the frequency distribution. For those ciphers, language letter group (or n-gram) frequencies may provide an attack. Essentially all ciphers remained vulnerable to cryptanalysis using the frequency analysis technique until the development of the polyalphabetic cipher, most clearly by Leon Battista Alberti around the year 1467, though there is some indication that it was already known to Al-Kindi. Alberti's innovation was to use different ciphers (i.e., substitution alphabets) for various parts of a message (perhaps for each successive plaintext letter at the limit). He also invented what was probably the first automatic cipher device, a wheel that implemented a partial realization of his invention. In the Vigenère cipher, a polyalphabetic cipher, encryption uses a key word, which controls letter substitution depending on which letter of the key word is used. In the mid-19th century Charles Babbage showed that the Vigenère cipher was vulnerable to Kasiski examination, but this was first published about ten years later by Friedrich Kasiski. Although frequency analysis can be a powerful and general technique against many ciphers, encryption has still often been effective in practice, as


id: cryptography-20260505-4
file: data/en.wikipedia.org/wiki/Cryptography-4.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
== Modern cryptography == Claude Shannon's two papers, his 1948 paper on information theory, and especially his 1949 paper on cryptography, laid the foundations of modern cryptography and provided a mathematical basis for future cryptography. His 1949 paper has been noted as having provided a "solid theoretical basis for cryptography and for cryptanalysis", and as having turned cryptography from an "art to a science". As a result of his contributions and work, he has been described as the "founding father of modern cryptography". Prior to the early 20th century, cryptography was mainly concerned with linguistic and lexicographic patterns. Since then cryptography has broadened in scope, and now makes extensive use of mathematical subdisciplines, including information theory, computational complexity, statistics, combinatorics, abstract algebra, number theory, and finite mathematics. Cryptography is also a branch of engineering, but an unusual one since it deals with active, intelligent, and malevolent opposition; other kinds of engineering (e.g., civil or chemical engineering) need deal only with neutral natural forces. There is also active research examining the relationship between cryptographic problems and quantum physics. Just as the development of digital computers and electronics helped in cryptanalysis, it made possible much more complex ciphers. Furthermore, computers allowed for


id: cryptography-20260505-5
file: data/en.wikipedia.org/wiki/Cryptography-5.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
Symmetric-key cryptosystems use the same key for encryption and decryption of a message, although a message or group of messages can have a different key than others. A significant disadvantage of symmetric ciphers is the key management necessary to use them securely. Each distinct pair of communicating parties must, ideally, share a different key, and perhaps for each ciphertext exchanged as well. The number of keys required increases as the square of the number of network members, which very quickly requires complex key management schemes to keep them all consistent and secret. In a groundbreaking 1976 paper, Whitfield Diffie and Martin Hellman proposed the notion of public-key (also, more generally, called asymmetric key) cryptography in which two different but mathematically related keys are used—a public key and a private key. A public key system is so constructed that calculation of one key (the 'private key') is computationally infeasible from the other (the 'public key'), even though they are necessarily related. Instead, both keys are generated secretly, as an interrelated pair. The historian David Kahn described public-key cryptography as "the most revolutionary new concept in the field since polyalphabetic substitution emerged in the Renaissance". In public-key cryptosystems, the public key may


id: cryptography-20260505-6
file: data/en.wikipedia.org/wiki/Cryptography-6.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
=== Cryptographic hash functions === Cryptographic hash functions are functions that take a variable-length input and return a fixed-length output, which can be used in, for example, a digital signature. For a hash function to be secure, it must be difficult to compute two inputs that hash to the same value (collision resistance) and to compute an input that hashes to a given output (preimage resistance). MD4 is a long-used hash function that is now broken; MD5, a strengthened variant of MD4, is also widely used but broken in practice. The US National Security Agency developed the Secure Hash Algorithm series of MD5-like hash functions: SHA-0 was a flawed algorithm that the agency withdrew; SHA-1 is widely deployed and more secure than MD5, but cryptanalysts have identified attacks against it; the SHA-2 family improves on SHA-1, but is vulnerable to clashes as of 2011; and the US standards authority thought it "prudent" from a security perspective to develop a new standard to "significantly improve the robustness of NIST's overall hash algorithm toolkit." Thus, a hash function design competition was meant to select a new U.S. national standard, to be called SHA-3, by 2012. The competition ended on October 2, 2012,


id: cryptography-20260505-7
file: data/en.wikipedia.org/wiki/Cryptography-7.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
The goal of cryptanalysis is to find some weakness or insecurity in a cryptographic scheme, thus permitting its subversion or evasion. It is a common misconception that every encryption method can be broken. In connection with his WWII work at Bell Labs, Claude Shannon proved that the one-time pad cipher is unbreakable, provided the key material is truly random, never reused, kept secret from all possible attackers, and of equal or greater length than the message. Most ciphers, apart from the one-time pad, can be broken with enough computational effort by brute force attack, but the amount of effort needed may be exponentially dependent on the key size, as compared to the effort needed to make use of the cipher. In such cases, effective security could be achieved if it is proven that the effort required (i.e., "work factor", in Shannon's terms) is beyond the ability of any adversary. This means it must be shown that no efficient method (as opposed to the time-consuming brute force method) can be found to break the cipher. Since no such proof has been found to date, the one-time-pad remains the only theoretically unbreakable cipher. Although well-implemented one-time-pad encryption cannot be broken, traffic analysis


id: cryptography-20260505-8
file: data/en.wikipedia.org/wiki/Cryptography-8.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
One or more cryptographic primitives are often used to develop a more complex algorithm, called a cryptographic system, or cryptosystem. Cryptosystems (e.g., El-Gamal encryption) are designed to provide particular functionality (e.g., public key encryption) while guaranteeing certain security properties (e.g., chosen-plaintext attack (CPA) security in the random oracle model). Cryptosystems use the properties of the underlying cryptographic primitives to support the system's security properties. As the distinction between primitives and cryptosystems is somewhat arbitrary, a sophisticated cryptosystem can be derived from a combination of several more primitive cryptosystems. In many cases, the cryptosystem's structure involves back and forth communication among two or more parties in space (e.g., between the sender of a secure message and its receiver) or across time (e.g., cryptographically protected backup data). Such cryptosystems are sometimes called cryptographic protocols. Some widely known cryptosystems include RSA, Schnorr signature, ElGamal encryption, and Pretty Good Privacy (PGP). More complex cryptosystems include electronic cash systems, signcryption systems, etc. Some more 'theoretical' cryptosystems include interactive proof systems, (like zero-knowledge proofs) and systems for secret sharing. === Lightweight cryptography === Lightweight cryptography (LWC) concerns cryptographic algorithms developed for a strictly constrained environment. The growth of Internet of Things (IoT) has spiked research into


id: cryptography-20260505-9
file: data/en.wikipedia.org/wiki/Cryptography-9.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
=== Export controls === In the 1990s, there were several challenges to US export regulation of cryptography. After the source code for Philip Zimmermann's Pretty Good Privacy (PGP) encryption program found its way onto the Internet in June 1991, a complaint by RSA Security (then called RSA Data Security, Inc.) resulted in a lengthy criminal investigation of Zimmermann by the US Customs Service and the FBI, though no charges were ever filed. Daniel J. Bernstein, then a graduate student at UC Berkeley, brought a lawsuit against the US government challenging some aspects of the restrictions based on free speech grounds. The 1995 case Bernstein v. United States ultimately resulted in a 1999 decision that printed source code for cryptographic algorithms and systems was protected as free speech by the United States Constitution. In 1996, thirty-nine countries signed the Wassenaar Arrangement, an arms control treaty that deals with the export of arms and "dual-use" technologies such as cryptography. The treaty stipulated that the use of cryptography with short key-lengths (56-bit for symmetric encryption, 512-bit for RSA) would no longer be export-controlled. Cryptography exports from the US became less strictly regulated as a consequence of a major relaxation in 2000; there are


id: cryptography-20260505-10
file: data/en.wikipedia.org/wiki/Cryptography-10.md
category: reference
title: Cryptography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cryptography
date_saved: 2026-05-05T06:37:45.235702+00:00
instance: kb-cron
parent: cryptography-20260505-0
summary:
In the United Kingdom, the Regulation of Investigatory Powers Act gives UK police the powers to force suspects to decrypt files or hand over passwords that protect encryption keys. Failure to comply is an offense in its own right, punishable on conviction by a two-year jail sentence or up to five years in cases involving national security. Successful prosecutions have occurred under the Act; the first, in 2009, resulted in a term of 13 months' imprisonment. Similar forced disclosure laws in Australia, Finland, France, and India compel individual suspects under investigation to hand over encryption keys or passwords during a criminal investigation. In the United States, the federal criminal case of United States v. Fricosu addressed whether a search warrant can compel a person to reveal an encryption passphrase or password. The Electronic Frontier Foundation (EFF) argued that this is a violation of the protection from self-incrimination given by the Fifth Amendment. In 2012, the court ruled that under the All Writs Act, the defendant was required to produce an unencrypted hard drive for the court. In many jurisdictions, the legal status of forced disclosure remains unclear. The 2016 FBI–Apple encryption dispute concerns the ability of courts in the United


id: data-mining-20260505-0
file: data/en.wikipedia.org/wiki/Data_mining-0.md
category: reference
title: Data mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_mining
date_saved: 2026-05-05T06:37:46.478722+00:00
instance: kb-cron
parent: data-mining-20260505-0
summary:
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support systems, including artificial intelligence (e.g., machine learning) and business intelligence. Often the more general terms (large scale) data analysis and analytics—or, when referring to actual methods, artificial intelligence and


id: data-mining-20260505-1
file: data/en.wikipedia.org/wiki/Data_mining-1.md
category: reference
title: Data mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_mining
date_saved: 2026-05-05T06:37:46.478722+00:00
instance: kb-cron
parent: data-mining-20260505-0
summary:
=== Pre-processing === Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data. === Data mining === Data mining involves six common classes of tasks: Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation due to being out of standard range. Association rule learning (dependency modeling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the


id: data-mining-20260505-2
file: data/en.wikipedia.org/wiki/Data_mining-2.md
category: reference
title: Data mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_mining
date_saved: 2026-05-05T06:37:46.478722+00:00
instance: kb-cron
parent: data-mining-20260505-0
summary:
== Privacy concerns and ethics == While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to user behavior (ethical and otherwise). The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns. Data mining requires data preparation which uncovers information or patterns which compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together (possibly from various sources) in a way that facilitates analysis (but that also might make identification of private, individual-level data deducible or otherwise apparent). The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous. Data may also be modified so as to become anonymous, so that individuals


id: data-mining-20260505-3
file: data/en.wikipedia.org/wiki/Data_mining-3.md
category: reference
title: Data mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_mining
date_saved: 2026-05-05T06:37:46.478722+00:00
instance: kb-cron
parent: data-mining-20260505-0
summary:
=== Situation in the United States === US copyright law, and in particular its provision for fair use, upholds the legality of content mining in America, and other fair use countries such as Israel, Taiwan and South Korea. As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitization project of in-copyright books was lawful, in part because of the transformative uses that the digitization project displayed—one being text and data mining. == Software == === Free open-source data mining software and applications === The following applications are available under free/open-source licenses. Public access to application source code is also available. Carrot2: Text and search results clustering framework. Chemicalize.org: A chemical structure miner and web search engine. ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language. GATE: a natural language processing and language engineering tool. KNIME: The Konstanz Information Miner, a user-friendly and comprehensive data analytics framework. Massive Online Analysis (MOA): a real-time big data stream mining with concept drift


id: decision-theory-20260505-0
file: data/en.wikipedia.org/wiki/Decision_theory-0.md
category: reference
title: Decision theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Decision_theory
date_saved: 2026-05-05T06:37:47.689613+00:00
instance: kb-cron
parent: decision-theory-20260505-0
summary:
Decision theory or the theory of rational choice is a branch of probability, economics, and analytic philosophy that uses expected utility and probability to model how individuals would behave rationally under uncertainty. It differs from the cognitive and behavioral sciences in that it is mainly prescriptive and concerned with identifying optimal decisions for a rational agent, rather than describing how people actually make decisions. Despite this, the field is important to the study of real human behavior by social scientists, as it lays the foundations to mathematically model and analyze individuals in fields such as sociology, economics, criminology, cognitive science, moral philosophy and political science. == History == The roots of decision theory lie in probability theory, developed by Blaise Pascal and Pierre de Fermat in the 17th century, which was later refined by others like Christiaan Huygens. These developments provided a framework for understanding risk and uncertainty, which are central to decision-making. In the 18th century, Daniel Bernoulli introduced the concept of "expected utility" in the context of gambling, which was later formalized by John von Neumann and Oskar Morgenstern in the 1940s. Their work on Game Theory and Expected Utility Theory helped establish a rational basis for decision-making


id: decision-theory-20260505-1
file: data/en.wikipedia.org/wiki/Decision_theory-1.md
category: reference
title: Decision theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Decision_theory
date_saved: 2026-05-05T06:37:47.689613+00:00
instance: kb-cron
parent: decision-theory-20260505-0
summary:
Intertemporal choice is concerned with the kind of choice where different actions lead to outcomes that are realized at different stages over time. It is also described as cost-benefit decision making since it involves the choices between rewards that vary according to magnitude and time of arrival. If someone received a windfall of several thousand dollars, they could spend it on an expensive holiday, giving them immediate pleasure, or they could invest it in a pension scheme, giving them an income at some time in the future. What is the optimal thing to do? The answer depends partly on factors such as the expected rates of interest and inflation, the person's life expectancy, and their confidence in the pensions industry. However even with all those factors taken into account, human behavior again deviates greatly from the predictions of prescriptive decision theory, leading to alternative models in which, for example, objective interest rates are replaced by subjective discount rates. === Interaction of decision makers === Some decisions are difficult because of the need to take into account how other people in the situation will respond to the decision that is taken. The analysis of such social decisions is often treated under


id: exact-sciences-20260505-0
file: data/en.wikipedia.org/wiki/Exact_sciences-0.md
category: reference
title: Exact sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Exact_sciences
date_saved: 2026-05-05T06:37:48.877692+00:00
instance: kb-cron
parent: exact-sciences-20260505-0
summary:
The exact sciences or quantitative sciences, sometimes called the exact mathematical sciences, are those sciences "which admit of absolute precision in their results"; especially the mathematical sciences. Examples of the exact sciences are mathematics, optics, astronomy, and physics, which many philosophers from René Descartes, Gottfried Leibniz, and Immanuel Kant to the logical positivists took as paradigms of rational and objective knowledge. These sciences have been practiced in many cultures from antiquity to modern times. Given their ties to mathematics, the exact sciences are characterized by accurate quantitative expression, precise predictions and/or rigorous methods of testing hypotheses involving quantifiable predictions and measurements. The distinction between the quantitative exact sciences and those sciences that deal with the causes of things is due to Aristotle, who distinguished mathematics from natural philosophy and considered the exact sciences to be the "more natural of the branches of mathematics." Thomas Aquinas employed this distinction when he said that astronomy explains the spherical shape of the Earth by mathematical reasoning while physics explains it by material causes. This distinction was widely, but not universally, accepted until the Scientific Revolution of the 17th century. Edward Grant has proposed that a fundamental change leading to the new sciences was


id: game-theory-20260505-0
file: data/en.wikipedia.org/wiki/Game_theory-0.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
Game theory is the study of mathematical models of strategic interactions. It has applications in many fields of social science, and is used extensively in economics, logic, systems science and computer science. Initially, game theory addressed two-person zero-sum games, in which a participant's gains or losses are exactly balanced by the losses and gains of the other participant. In the 1950s, it was extended to the study of non zero-sum games, and was eventually applied to a wide range of behavioral relations. It is now an umbrella term for the science of rational decision making in humans, animals, and computers. Modern game theory began with the idea of mixed-strategy equilibria in two-person zero-sum games and its proof by John von Neumann. Von Neumann's original proof used the Brouwer fixed-point theorem on continuous mappings into compact convex sets, which became a standard method in game theory and mathematical economics. His paper was followed by Theory of Games and Economic Behavior (1944), co-written with Oskar Morgenstern, which considered cooperative games of several players. The second edition provided an axiomatic theory of expected utility, which allowed mathematical statisticians and economists to treat decision-making under uncertainty. Game theory was developed extensively in the 1950s,


id: game-theory-20260505-1
file: data/en.wikipedia.org/wiki/Game_theory-1.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
==== Prize-winning achievements ==== In 1965, Reinhard Selten introduced his solution concept of subgame perfect equilibria, which further refined the Nash equilibrium. Later he would introduce trembling hand perfection as well. In 1994 Nash, Selten and Harsanyi became Economics Nobel Laureates for their contributions to economic game theory. In the 1970s, game theory was extensively applied in biology, largely as a result of the work of John Maynard Smith and his evolutionarily stable strategy. In addition, the concepts of correlated equilibrium, trembling hand perfection and common knowledge were introduced and analyzed. In 1994, John Nash was awarded the Nobel Memorial Prize in the Economic Sciences for his contribution to game theory. Nash's most famous contribution to game theory is the concept of the Nash equilibrium, which is a solution concept for non-cooperative games, published in 1951. A Nash equilibrium is a set of strategies, one for each player, such that no player can improve their payoff by unilaterally changing their strategy. In 2005, game theorists Thomas Schelling and Robert Aumann followed Nash, Selten, and Harsanyi as Nobel Laureates. Schelling worked on dynamic models, early examples of evolutionary game theory. Aumann contributed more to the equilibrium school, introducing equilibrium coarsening and


id: game-theory-20260505-2
file: data/en.wikipedia.org/wiki/Game_theory-2.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
Simultaneous games are games where both players move simultaneously, or instead the later players are unaware of the earlier players' actions (making them effectively simultaneous). Sequential games (a type of dynamic games) are games where players do not make decisions simultaneously, and player's earlier actions affect the outcome and decisions of other players. This need not be perfect information about every action of earlier players; it might be very little knowledge. For instance, a player may know that an earlier player did not perform one particular action, while they do not know which of the other available actions the first player actually performed. The difference between simultaneous and sequential games is captured in the different representations discussed above. Often, normal form is used to represent simultaneous games, while extensive form is used to represent sequential ones. The transformation of extensive to normal form is one way, meaning that multiple extensive form games correspond to the same normal form. Consequently, notions of equilibrium for simultaneous games are insufficient for reasoning about sequential games; see subgame perfection. In short, the differences between sequential and simultaneous games are as follows: === Perfect information and imperfect information === An important subset of sequential games


id: game-theory-20260505-3
file: data/en.wikipedia.org/wiki/Game_theory-3.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
Differential games such as the continuous pursuit and evasion game are continuous games where the evolution of the players' state variables is governed by differential equations. The problem of finding an optimal strategy in a differential game is closely related to the optimal control theory. In particular, there are two types of strategies: the open-loop strategies are found using the Pontryagin maximum principle while the closed-loop strategies are found using Bellman's Dynamic Programming method. A particular case of differential games are the games with a random time horizon. In such games, the terminal time is a random variable with a given probability distribution function. Therefore, the players maximize the mathematical expectation of the cost function. It was shown that the modified optimization problem can be reformulated as a discounted differential game over an infinite time interval. === Evolutionary game theory === Evolutionary game theory studies players who adjust their strategies over time according to rules that are not necessarily rational or farsighted. In general, the evolution of strategies over time according to such rules is modeled as a Markov chain with a state variable such as the current strategy profile or how the game has been played in the recent


id: game-theory-20260505-4
file: data/en.wikipedia.org/wiki/Game_theory-4.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
The extensive form can be used to formalize games with a time sequencing of moves. Extensive form games can be visualized using game trees (as pictured here). Here each vertex (or node) represents a point of choice for a player. The player is specified by a number listed by the vertex. The lines out of the vertex represent a possible action for that player. The payoffs are specified at the bottom of the tree. The extensive form can be viewed as a multi-player generalization of a decision tree. To solve any extensive form game, backward induction must be used. It involves working backward up the game tree to determine what a rational player would do at the last vertex of the tree, what the player with the previous move would do given that the player with the last move is rational, and so on until the first vertex of the tree is reached. The game pictured consists of two players. The way this particular game is structured (i.e., with sequential decision making and perfect information), Player 1 "moves" first by choosing either F or U (fair or unfair). Next in the sequence, Player 2, who has now observed Player 1's


id: game-theory-20260505-5
file: data/en.wikipedia.org/wiki/Game_theory-5.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
== General and applied uses == As a method of applied mathematics, game theory has been used to study a wide variety of human and animal behaviors. It was initially developed in economics to understand a large collection of economic behaviors, including behaviors of firms, markets, and consumers. The first use of game-theoretic analysis was by Antoine Augustin Cournot in 1838 with his solution of the Cournot duopoly. The use of game theory in the social sciences has expanded, and game theory has been applied to political, sociological, and psychological behaviors as well. Although pre-twentieth-century naturalists such as Charles Darwin made game-theoretic kinds of statements, the use of game-theoretic analysis in biology began with Ronald Fisher's studies of animal behavior during the 1930s. This work predates the name "game theory", but it shares many important features with this field. The developments in economics were later applied to biology largely by John Maynard Smith in his 1982 book Evolution and the Theory of Games. In addition to being used to describe, predict, and explain behavior, game theory has also been used to develop theories of ethical or normative behavior and to prescribe such behavior. In economics and philosophy, scholars have applied


id: game-theory-20260505-6
file: data/en.wikipedia.org/wiki/Game_theory-6.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
==== Managerial economics ==== Game theory also has an extensive use in a specific branch or stream of economics – Managerial Economics. One important usage of it in the field of managerial economics is in analyzing strategic interactions between firms. For example, firms may be competing in a market with limited resources, and game theory can help managers understand how their decisions impact their competitors and the overall market outcomes. Game theory can also be used to analyze cooperation between firms, such as in forming strategic alliances or joint ventures. Another use of game theory in managerial economics is in analyzing pricing strategies. For example, firms may use game theory to determine the optimal pricing strategy based on how they expect their competitors to respond to their pricing decisions. Overall, game theory serves as a useful tool for analyzing strategic interactions and decision making in the context of managerial economics. === Business === The Chartered Institute of Procurement & Supply (CIPS) promotes knowledge and use of game theory within the context of business procurement. CIPS and TWS Partners have conducted a series of surveys designed to explore the understanding, awareness and application of game theory among procurement professionals. Some of


id: game-theory-20260505-7
file: data/en.wikipedia.org/wiki/Game_theory-7.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
The application of game theory to political science is focused in the overlapping areas of fair division, political economy, public choice, war bargaining, positive political theory, and social choice theory. In each of these areas, researchers have developed game-theoretic models in which the players are often voters, states, special interest groups, and politicians. Early examples of game theory applied to political science are provided by Anthony Downs. In his 1957 book An Economic Theory of Democracy, he applies the Hotelling firm location model to the political process. In the Downsian model, political candidates commit to ideologies on a one-dimensional policy space. Downs first shows how the political candidates will converge to the ideology preferred by the median voter if voters are fully informed, but then argues that voters choose to remain rationally ignorant which allows for candidate divergence. Game theory was applied in 1962 to the Cuban Missile Crisis during the presidency of John F. Kennedy. It has also been proposed that game theory explains the stability of any form of political government. Taking the simplest case of a monarchy, for example, the king, being only one person, does not and cannot maintain his authority by personally exercising physical control


id: game-theory-20260505-8
file: data/en.wikipedia.org/wiki/Game_theory-8.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
Unlike those in economics, the payoffs for games in biology are often interpreted as corresponding to fitness. In addition, the focus has been less on equilibria that correspond to a notion of rationality and more on ones that would be maintained by evolutionary forces. The best-known equilibrium in biology is known as the evolutionarily stable strategy (ESS), first introduced in (Maynard Smith & Price 1973). Although its initial motivation did not involve any of the mental requirements of the Nash equilibrium, every ESS is a Nash equilibrium. In biology, game theory has been used as a model to understand many different phenomena. It was first used to explain the evolution (and stability) of the approximate 1:1 sex ratios. (Fisher 1930) suggested that the 1:1 sex ratios are a result of evolutionary forces acting on individuals who could be seen as trying to maximize their number of grandchildren. Additionally, biologists have used evolutionary game theory and the ESS to explain the emergence of animal communication. The analysis of signaling games and other communication games has provided insight into the evolution of communication among animals. For example, the mobbing behavior of many species, in which a large number of prey animals attack


id: game-theory-20260505-9
file: data/en.wikipedia.org/wiki/Game_theory-9.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
Game theory has been put to several uses in philosophy. Responding to two papers by W.V.O. Quine (1960, 1967), Lewis (1969) used game theory to develop a philosophical account of convention. In so doing, he provided the first analysis of common knowledge and employed it in analyzing play in coordination games. In addition, he first suggested that one can understand meaning in terms of signaling games. This later suggestion has been pursued by several philosophers since Lewis. Following Lewis (1969) game-theoretic account of conventions, Edna Ullmann-Margalit (1977) and Bicchieri (2006) have developed theories of social norms that define them as Nash equilibria that result from transforming a mixed-motive game into a coordination game. Game theory has also challenged philosophers to think in terms of interactive epistemology: what it means for a collective to have common beliefs or knowledge, and what are the consequences of this knowledge for the social outcomes resulting from the interactions of agents. Philosophers who have worked in this area include Bicchieri (1989, 1993), Skyrms (1990), and Stalnaker (1999). The synthesis of game theory with ethics was championed by R. B. Braithwaite. The hope was that rigorous mathematical analysis of game theory might help formalize the more


id: game-theory-20260505-10
file: data/en.wikipedia.org/wiki/Game_theory-10.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
=== Trust game === The Trust Game is an experiment designed to measure trust in economic decisions. It is also called "the investment game" and is designed to investigate trust and demonstrate its importance rather than "rationality" of self-interest. The game was designed by Berg Joyce, John Dickhaut and Kevin McCabe in 1995. In the game, one player (the investor) is given a sum of money and must decide how much of it to give to another player (the trustee). The amount given is then tripled by the experimenter. The trustee then decides how much of the tripled amount to return to the investor. If the trustee is completely self-interested, then they would return nothing. However, experiments have shown that this isn't the expected behavior of the trustee. The outcome instead suggests that people are willing to place trust, by risking some amount of money, in the belief that there will be reciprocity. === Cournot Competition === The Cournot competition model involves players choosing quantity of a homogenous product to produce independently and simultaneously, where marginal cost can be different for each firm and the firm's payoff is profit. The production costs are public information and the firm aims to


id: game-theory-20260505-11
file: data/en.wikipedia.org/wiki/Game_theory-11.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
== See also == Applied ethics – Practical application of moral considerations Bandwidth-sharing game – Type of resource allocation game Chainstore paradox – Game theory paradox Collective intentionality – Social concept in philosophy of mind Core (game theory) – Set in game theory Glossary of game theory Intra-household bargaining Kingmaker scenario – Endgame situation in game theory Law and economics – Analysis of law using economic theory Mutual assured destruction – Doctrine of military strategyPages displaying short descriptions of redirect targets Outline of artificial intelligence Parrondo's paradox – Paradox of combining strategies Precautionary principle – Risk management strategy Quantum refereed game Risk management – Identification, evaluation and control of risks Self-confirming equilibrium – Aspect of game theory Tragedy of the commons – Overuse of a shared resource Traveler's dilemma – Non-zero-sum game thought experiment Wilson doctrine (economics) – Argument in economic theory Compositional game theory Lists List of cognitive biases List of emerging technologies List of games in game theory == Notes == == References == == Further reading == Ben-David, S.; Borodin, A.; Karp, R.; Tardos, G.; Wigderson, A. (January 1994). "On the power of randomization in on-line algorithms". Algorithmica. 11 (1): 2–14. doi:10.1007/BF01294260. S2CID 26771869. Downs, Anthony (1957),


id: game-theory-20260505-12
file: data/en.wikipedia.org/wiki/Game_theory-12.md
category: reference
title: Game theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Game_theory
date_saved: 2026-05-05T06:37:50.428962+00:00
instance: kb-cron
parent: game-theory-20260505-0
summary:
=== Historically important texts === Aumann, R. J.; Shapley, L. S. (1974), Values of Non-Atomic Games, Princeton University Press Cournot, A. Augustin (1838), "Recherches sur les principles mathematiques de la théorie des richesses", Libraire des Sciences Politiques et Sociales Edgeworth, Francis Y. (1881), Mathematical Psychics, London: Kegan Paul Farquharson, Robin (1969), Theory of Voting, Blackwell (Yale U.P. in the U.S.), ISBN 978-0-631-12460-3 Luce, R. Duncan; Raiffa, Howard (1957), Games and decisions: introduction and critical survey, New York: Wiley reprinted edition: R. Duncan Luce; Howard Raiffa (1989), Games and decisions: introduction and critical survey, New York: Dover Publications, ISBN 978-0-486-65943-5 Maynard Smith, John (1982), Evolution and the theory of games, Cambridge University Press, ISBN 978-0-521-28884-2 Nash, John (1950), "Equilibrium points in n-person games", Proceedings of the National Academy of Sciences of the United States of America, 36 (1): 48–49, Bibcode:1950PNAS...36...48N, doi:10.1073/pnas.36.1.48, PMC 1063129, PMID 16588946 Shapley, L.S. (1953), A Value for n-person Games, In: Contributions to the Theory of Games volume II, H. W. Kuhn and A. W. Tucker (eds.) Shapley, L. S. (October 1953). "Stochastic Games". Proceedings of the National Academy of Sciences. 39 (10): 1095–1100. Bibcode:1953PNAS...39.1095S. doi:10.1073/pnas.39.10.1095. PMC 1063912. PMID 16589380. von Neumann, John (1928), "Zur Theorie der Gesellschaftsspiele",


id: grammar-systems-theory-20260505-0
file: data/en.wikipedia.org/wiki/Grammar_systems_theory-0.md
category: reference
title: Grammar systems theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Grammar_systems_theory
date_saved: 2026-05-05T06:37:51.751577+00:00
instance: kb-cron
parent: grammar-systems-theory-20260505-0
summary:
Grammar systems theory is a field of theoretical computer science that studies systems of finite collections of formal grammars generating a formal language. Each grammar works on a string, a so-called sequential form that represents an environment. Grammar systems can thus be used as a formalization of decentralized or distributed systems of agents in artificial intelligence. Let A {\displaystyle \mathbb {A} } be a simple reactive agent moving on the table and trying not to fall down from the table with two reactions, t for turning and ƒ for moving forward. The set of possible behaviors of A {\displaystyle \mathbb {A} } can then be described as formal language L A = { ( f m t n f r ) + : 1 ≤ m ≤ k ; 1 ≤ n ≤ ℓ ; 1 ≤ r ≤ k } , {\displaystyle \mathbb {L_{A}} =\{(f^{m}t^{n}f^{r})^{+}:1\leq m\leq k;1\leq n\leq \ell ;1\leq r\leq k\},} where ƒ can be done maximally k times and t can be done maximally ℓ times considering the dimensions of the table. Let G A {\displaystyle \mathbb {G_{A}} } be a formal grammar which generates language L A {\displaystyle \mathbb {L_{A}} } . The behavior of A


id: homeokinetics-20260505-0
file: data/en.wikipedia.org/wiki/Homeokinetics-0.md
category: reference
title: Homeokinetics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Homeokinetics
date_saved: 2026-05-05T06:37:53.074479+00:00
instance: kb-cron
parent: homeokinetics-20260505-0
summary:
Homeokinetics is the study of self-organizing, complex systems. Standard physics studies systems at separate levels, such as atomic physics, nuclear physics, biophysics, social physics, and galactic physics. Homeokinetic physics studies the up-down processes that bind these levels. Tools such as mechanics, quantum field theory, and the laws of thermodynamics provide the key relationships. The subject, described as the physics and thermodynamics associated with the up down movement between levels of systems, originated in the late 1970s work of American physicists Harry Soodak and Arthur Iberall. Complex systems are universes, galaxies, social systems, people, or even those that seem as simple as gases. The basic premise is that the entire universe consists of atomistic-like units bound in interactive ensembles to form systems, level by level, in a nested hierarchy. Homeokinetics treats all complex systems on an equal footing, animate and inanimate, providing them with a common viewpoint. The complexity in studying how they work is reduced by the emergence of common languages in all complex systems. == History == Arthur Iberall, Warren McCulloch and Harry Soodak developed the concept of homeokinetics as a new branch of physics. It began through Iberall's biophysical research for the NASA exobiology program into the dynamics


id: image-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Image_analysis-0.md
category: reference
title: Image analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Image_analysis
date_saved: 2026-05-05T06:37:54.389321+00:00
instance: kb-cron
parent: image-analysis-20260505-0
summary:
Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information. On the other hand, the human visual cortex is an excellent image analysis apparatus, especially for extracting higher-level information, and for many applications — including medicine, security, and remote sensing — human analysts still cannot be replaced by computers. For this reason, many important image analysis tools such as edge detectors and neural networks are inspired by human visual perception models. == Digital == Digital Image Analysis or Computer Image Analysis is when a computer or electrical device automatically studies an image to obtain useful information from it. Note that the device is often a computer but may also be an electrical circuit, a digital camera or a mobile phone. It involves the fields of computer or machine vision, and medical imaging, and makes heavy use of pattern recognition, digital


id: information-theory-20260505-0
file: data/en.wikipedia.org/wiki/Information_theory-0.md
category: reference
title: Information theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Information_theory
date_saved: 2026-05-05T06:37:55.695356+00:00
instance: kb-cron
parent: information-theory-20260505-0
summary:
Information theory is the mathematical study of the quantification, storage, and communication of a particular type of mathematically defined information. The field was established and formalized by Claude Shannon in the 1940s, though early contributions were made in the 1920s through the works of Harry Nyquist and Ralph Hartley. Information theory was initially formed in the context of telecommunication but soon found a wide range of other applications. It is now at the intersection of mathematics, statistics and computer science, and has applications in diverse fields ranging from electrical engineering and physics to neurobiology. As a simple example of the concept, if one flips a fair coin and does not yet know the outcome (heads or tails), then they lack a certain amount of information. After looking at the coin, they gain information about the outcome. For a fair coin, the probability of either heads or tails is 1/2 and the amount of information is expressed as − log 2 ⁡ ( 1 / 2 ) {\displaystyle -\log _{2}(1/2)} = 1 bit of information. A key concept in information theory is information entropy. In Shannon's formulation entropy is equal to the lack of information about an event. In the above


id: information-theory-20260505-1
file: data/en.wikipedia.org/wiki/Information_theory-1.md
category: reference
title: Information theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Information_theory
date_saved: 2026-05-05T06:37:55.695356+00:00
instance: kb-cron
parent: information-theory-20260505-0
summary:
The landmark event establishing the discipline of information theory and bringing it to immediate worldwide attention was the publication of Claude Shannon's classic paper "A Mathematical Theory of Communication" in the Bell System Technical Journal in July and October 1948. Historian James Gleick rated the paper as the most important development of 1948, noting that the paper was "even more profound and more fundamental" than the transistor. He came to be known as the "father of information theory". Shannon outlined some of his initial ideas of information theory as early as 1939 in a letter to Vannevar Bush. Prior to this paper, limited information-theoretic ideas had been developed at Bell Labs, all implicitly assuming events of equal probability. Harry Nyquist's 1924 paper, Certain Factors Affecting Telegraph Speed, contains a theoretical section quantifying "intelligence" and the "line speed" at which it can be transmitted by a communication system, giving the relation W = K log m (recalling the Boltzmann constant), where W is the speed of transmission of intelligence, m is the number of different voltage levels to choose from at each time step, and K is a constant. Ralph Hartley's 1928 paper, Transmission of Information, uses the word information as


id: information-theory-20260505-2
file: data/en.wikipedia.org/wiki/Information_theory-2.md
category: reference
title: Information theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Information_theory
date_saved: 2026-05-05T06:37:55.695356+00:00
instance: kb-cron
parent: information-theory-20260505-0
summary:
Intuitively, the entropy H ( X ) {\displaystyle H(X)} of a discrete random variable X is a measure of the amount of uncertainty associated with the value of X {\displaystyle X} when only its distribution is known. A high entropy indicates the outcomes are more evenly distributed, making the result harder to predict. For example, if one transmits 1000 bits (0s and 1s), and the value of each of these bits is known to the receiver (has a specific value with certainty) ahead of transmission, no information is transmitted. If, however, each bit is independently and equally likely to be 0 or 1, 1000 shannons of information (more often called bits) have been transmitted. ==== Properties ==== A key property of entropy is that it is maximized when all the messages in the message space are equiprobable. For a source with n possible symbols, where p i = 1 n {\textstyle p_{i}={\frac {1}{n}}} for all i {\displaystyle i} , the entropy is given by: H ( X ) = log 2 ⁡ ( n ) {\displaystyle H(X)=\log _{2}(n)} This maximum value represents the most unpredictable state. For a source that emits a sequence of N {\displaystyle N} symbols that are


id: information-theory-20260505-3
file: data/en.wikipedia.org/wiki/Information_theory-3.md
category: reference
title: Information theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Information_theory
date_saved: 2026-05-05T06:37:55.695356+00:00
instance: kb-cron
parent: information-theory-20260505-0
summary:
=== Mutual information (transinformation) === Mutual information measures the amount of information that can be obtained about one random variable by observing another. It is important in communication where it can be used to maximize the amount of information shared between sent and received signals. The mutual information of X relative to Y is given by: I ( X ; Y ) = E X , Y [ S I ( x , y ) ] = ∑ x , y p ( x , y ) log ⁡ p ( x , y ) p ( x ) p ( y ) {\displaystyle I(X;Y)=\mathbb {E} _{X,Y}[SI(x,y)]=\sum _{x,y}p(x,y)\log {\frac {p(x,y)}{p(x)\,p(y)}}} where SI (Specific mutual Information) is the pointwise mutual information. A basic property of the mutual information is that: I ( X ; Y ) = H ( X ) − H ( X | Y ) . {\displaystyle I(X;Y)=H(X)-H(X|Y).\,} That is, knowing Y {\textstyle Y} , we can save an average of I(X; Y) bits in encoding X {\displaystyle X} compared to not knowing Y {\displaystyle Y} . Mutual information is symmetric: I ( X ; Y ) = I ( Y ; X ) = H ( X


id: information-theory-20260505-4
file: data/en.wikipedia.org/wiki/Information_theory-4.md
category: reference
title: Information theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Information_theory
date_saved: 2026-05-05T06:37:55.695356+00:00
instance: kb-cron
parent: information-theory-20260505-0
summary:
I ( X n → Y n ) ≜ ∑ i = 1 n I ( X i ; Y i | Y i − 1 ) {\displaystyle I(X^{n}\to Y^{n})\ \triangleq \ \sum _{i=1}^{n}I(X^{i};Y_{i}|Y^{i-1})} , where I ( X i ; Y i | Y i − 1 ) {\displaystyle I(X^{i};Y_{i}|Y^{i-1})} is the conditional mutual information I ( X 1 , X 2 , . . . , X i ; Y i | Y 1 , Y 2 , . . . , Y i − 1 ) {\displaystyle I(X_{1},X_{2},...,X_{i};Y_{i}|Y_{1},Y_{2},...,Y_{i-1})} . In contrast to mutual information, directed information is not symmetric. The I ( X n → Y n ) {\displaystyle I(X^{n}\to Y^{n})} measures the information bits that are transmitted causally from X n {\displaystyle X^{n}} to Y n {\displaystyle Y^{n}} . The Directed information has many applications in problems where causality plays an important role such as capacity of channel with feedback, capacity of discrete memoryless networks with feedback, gambling with causal side information, compression with causal side information, real-time control communication settings, and in statistical physics. === Other quantities === Other important information theoretic quantities include the Rényi entropy and the Tsallis entropy (generalizations of the concept


id: information-theory-20260505-5
file: data/en.wikipedia.org/wiki/Information_theory-5.md
category: reference
title: Information theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Information_theory
date_saved: 2026-05-05T06:37:55.695356+00:00
instance: kb-cron
parent: information-theory-20260505-0
summary:
→ Message W Encoder f n → E n c o d e d s e q u e n c e X n Channel p ( y | x ) → R e c e i v e d s e q u e n c e Y n Decoder g n → E s t i m a t e d m e s s a g e W ^ {\displaystyle {\xrightarrow[{\text{Message}}]{W}}{\begin{array}{|c| }\hline {\text{Encoder}}\\f_{n}\\\hline \end{array}}{\xrightarrow[{\mathrm {Encoded \atop sequence} }]{X^{n}}}{\begin{array}{|c| }\hline {\text{Channel}}\\p(y|x)\\\hline \end{array}}{\xrightarrow[{\mathrm {Received \atop sequence} }]{Y^{n}}}{\begin{array}{|c| }\hline {\text{Decoder}}\\g_{n}\\\hline \end{array}}{\xrightarrow[{\mathrm {Estimated \atop message} }]{\hat {W}}}} Here X {\displaystyle X} represents the space of messages transmitted, and Y {\textstyle Y} the space of messages received during a unit time over our channel. Let p(y|x) be the conditional probability distribution function of Y {\textstyle Y} given X {\displaystyle X} . We will consider p(y|x) to be an inherent fixed property of our communications channel (representing the nature of the noise of our channel). Then the joint distribution of X {\displaystyle X} and Y {\textstyle Y} is completely determined by our channel and by our choice of f(x), the marginal distribution of messages we choose to send over the channel. Under these


id: information-theory-20260505-6
file: data/en.wikipedia.org/wiki/Information_theory-6.md
category: reference
title: Information theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Information_theory
date_saved: 2026-05-05T06:37:55.695356+00:00
instance: kb-cron
parent: information-theory-20260505-0
summary:
Information theoretic concepts apply to cryptography and cryptanalysis. Turing's information unit, the ban, was used in the Ultra project, breaking the German Enigma machine code and hastening the end of World War II in Europe. Shannon himself defined an important concept now called the unicity distance. Based on the redundancy of the plaintext, it attempts to give a minimum amount of ciphertext necessary to ensure unique decipherability. Information theory leads us to believe it is much more difficult to keep secrets than it might first appear. A brute force attack can break systems based on asymmetric key algorithms or on most commonly used methods of symmetric key algorithms (sometimes called secret key algorithms), such as block ciphers. The security of all such methods comes from the assumption that no known attack can break them in a practical amount of time. Information theoretic security refers to methods such as the one-time pad that are not vulnerable to such brute force attacks. In such cases, the positive conditional mutual information between the plaintext and ciphertext (conditioned on the key) can ensure proper transmission, while the unconditional mutual information between the plaintext and ciphertext remains zero, resulting in absolutely secure communications. In other


id: logic-20260505-0
file: data/en.wikipedia.org/wiki/Logic-0.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the study of deductively valid inferences or logical truths. It examines how conclusions follow from premises based on the structure of arguments alone, independent of their topic and content. Informal logic is associated with informal fallacies, critical thinking, and argumentation theory. Informal logic examines arguments expressed in natural language whereas formal logic uses formal language. When used as a countable noun, the term "a logic" refers to a specific logical formal system that articulates a proof system. Logic plays a central role in many fields, such as philosophy, mathematics, computer science, and linguistics. Logic studies arguments, which consist of a set of premises that leads to a conclusion. An example is the argument from the premises "it's Sunday" and "if it's Sunday then I don't have to work" leading to the conclusion "I don't have to work." Premises and conclusions express propositions or claims that can be true or false. An important feature of propositions is their internal structure. For example, complex propositions are made up of simpler propositions linked by logical vocabulary like ∧ {\displaystyle \land } (and) or → {\displaystyle \to


id: logic-20260505-1
file: data/en.wikipedia.org/wiki/Logic-1.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
Formal logic uses formal languages to express, analyze, and clarify arguments. They normally have a very limited vocabulary and exact syntactic rules. These rules specify how their symbols can be combined to construct sentences, so-called well-formed formulas. This simplicity and exactness of formal logic make it capable of formulating precise rules of inference. They determine whether a given argument is valid. Because of the reliance on formal language, natural language arguments cannot be studied directly. Instead, they need to be translated into formal language before their validity can be assessed. The term "logic" can also be used in a slightly different sense as a countable noun. In this sense, a logic is a logical formal system. Distinct logics differ from each other concerning the rules of inference they accept as valid and the formal languages used to express them. Starting in the late 19th century, many new formal systems have been proposed. There are disagreements about what makes a formal system a logic. For example, it has been suggested that only logically complete systems, like first-order logic, qualify as logics. For such reasons, some theorists deny that higher-order logics are logics in the strict sense. === Informal logic === When


id: logic-20260505-2
file: data/en.wikipedia.org/wiki/Logic-2.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
Premises and conclusions are the basic parts of inferences or arguments and therefore play a central role in logic. In the case of a valid inference or a correct argument, the conclusion follows from the premises, or in other words, the premises support the conclusion. For instance, the premises "Mars is red" and "Mars is a planet" support the conclusion "Mars is a red planet". For most types of logic, it is accepted that premises and conclusions have to be truth-bearers. This means that they have a truth value: they are either true or false. Contemporary philosophy generally sees them either as propositions or as sentences. Propositions are the denotations of sentences and are usually seen as abstract objects. For example, the English sentence "the tree is green" is different from the German sentence "der Baum ist grün" but both express the same proposition. Propositional theories of premises and conclusions are often criticized because they rely on abstract objects. For instance, philosophical naturalists usually reject the existence of abstract objects. Other arguments concern the challenges involved in specifying the identity criteria of propositions. These objections are avoided by seeing premises and conclusions not as propositions but as sentences, i.e. as


id: logic-20260505-3
file: data/en.wikipedia.org/wiki/Logic-3.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
Logic is commonly defined in terms of arguments or inferences as the study of their correctness. An argument is a set of premises together with a conclusion. An inference is the process of reasoning from these premises to the conclusion. But these terms are often used interchangeably in logic. Arguments are correct or incorrect depending on whether their premises support their conclusion. Premises and conclusions, on the other hand, are true or false depending on whether they are in accord with reality. In formal logic, a sound argument is an argument that is both correct and has only true premises. Sometimes a distinction is made between simple and complex arguments. A complex argument is made up of a chain of simple arguments. This means that the conclusion of one argument acts as a premise of later arguments. For a complex argument to be successful, each link of the chain has to be successful. Arguments and inferences are either correct or incorrect. If they are correct then their premises support their conclusion. In the incorrect case, this support is missing. It can take different forms corresponding to the different types of reasoning. The strongest form of support corresponds to deductive reasoning.


id: logic-20260505-4
file: data/en.wikipedia.org/wiki/Logic-4.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
==== Ampliative ==== Ampliative arguments are arguments whose conclusions contain additional information not found in their premises. In this regard, they are more interesting since they contain information on the depth level and the thinker may learn something genuinely new. But this feature comes with a certain cost: the premises support the conclusion in the sense that they make its truth more likely but they do not ensure its truth. This means that the conclusion of an ampliative argument may be false even though all its premises are true. This characteristic is closely related to non-monotonicity and defeasibility: it may be necessary to retract an earlier conclusion upon receiving new information or in light of new inferences drawn. Ampliative reasoning plays a central role in many arguments found in everyday discourse and the sciences. Ampliative arguments are not automatically incorrect. Instead, they just follow different standards of correctness. The support they provide for their conclusion usually comes in degrees. This means that strong ampliative arguments make their conclusion very likely while weak ones are less certain. As a consequence, the line between correct and incorrect arguments is blurry in some cases, such as when the premises offer weak but non-negligible


id: logic-20260505-5
file: data/en.wikipedia.org/wiki/Logic-5.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
=== Definitory and strategic rules === The main focus of most logicians is to study the criteria according to which an argument is correct or incorrect. A fallacy is committed if these criteria are violated. In the case of formal logic, they are known as rules of inference. They are definitory rules, which determine whether an inference is correct or which inferences are allowed. Definitory rules contrast with strategic rules. Strategic rules specify which inferential moves are necessary to reach a given conclusion based on a set of premises. This distinction does not just apply to logic but also to games. In chess, for example, the definitory rules dictate that bishops may only move diagonally. The strategic rules, on the other hand, describe how the allowed moves may be used to win a game, for instance, by controlling the center and by defending one's king. It has been argued that logicians should give more emphasis to strategic rules since they are highly relevant for effective reasoning. === Formal systems === A formal system of logic consists of a formal language together with a set of axioms and a proof system used to draw inferences from these axioms. In logic, axioms


id: logic-20260505-6
file: data/en.wikipedia.org/wiki/Logic-6.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
In Aristotelian logic, the subject can be universal, particular, indefinite, or singular. For example, the term "all humans" is a universal subject in the proposition "all humans are mortal". A similar proposition could be formed by replacing it with the particular term "some humans", the indefinite term "a human", or the singular term "Socrates". Aristotelian logic only includes predicates for simple properties of entities. But it lacks predicates corresponding to relations between entities. The predicate can be linked to the subject in two ways: either by affirming it or by denying it. For example, the proposition "Socrates is not a cat" involves the denial of the predicate "cat" to the subject "Socrates". Using combinations of subjects and predicates, a great variety of propositions and syllogisms can be formed. Syllogisms are characterized by the fact that the premises are linked to each other and to the conclusion by sharing one term in each case. Thus, these three propositions contain three terms, referred to as major term, minor term, and middle term. The central aspect of Aristotelian logic involves classifying all possible syllogisms into valid and invalid arguments according to how the propositions are formed. For example, the syllogism "all men are


id: logic-20260505-7
file: data/en.wikipedia.org/wiki/Logic-7.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
Modal logic is an extension of classical logic. In its original form, sometimes called "alethic modal logic", it introduces two new symbols: ◊ {\displaystyle \Diamond } expresses that something is possible while ◻ {\displaystyle \Box } expresses that something is necessary. For example, if the formula B ( s ) {\displaystyle B(s)} stands for the sentence "Socrates is a banker" then the formula ◊ B ( s ) {\displaystyle \Diamond B(s)} articulates the sentence "It is possible that Socrates is a banker". To include these symbols in the logical formalism, modal logic introduces new rules of inference that govern what role they play in inferences. One rule of inference states that, if something is necessary, then it is also possible. This means that ◊ A {\displaystyle \Diamond A} follows from ◻ A {\displaystyle \Box A} . Another principle states that if a proposition is necessary then its negation is impossible and vice versa. This means that ◻ A {\displaystyle \Box A} is equivalent to ¬ ◊ ¬ A {\displaystyle \lnot \Diamond \lnot A} . Other forms of modal logic introduce similar symbols but associate different meanings with them to apply modal logic to other fields. For example, deontic logic concerns


id: logic-20260505-8
file: data/en.wikipedia.org/wiki/Logic-8.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
Informal logic is usually carried out in a less systematic way. It often focuses on more specific issues, like investigating a particular type of fallacy or studying a certain aspect of argumentation. Nonetheless, some frameworks of informal logic have also been presented that try to provide a systematic characterization of the correctness of arguments. The pragmatic or dialogical approach to informal logic sees arguments as speech acts and not merely as a set of premises together with a conclusion. As speech acts, they occur in a certain context, like a dialogue, which affects the standards of right and wrong arguments. A prominent version by Douglas N. Walton understands a dialogue as a game between two players. The initial position of each player is characterized by the propositions to which they are committed and the conclusion they intend to prove. Dialogues are games of persuasion: each player has the goal of convincing the opponent of their own conclusion. This is achieved by making arguments: arguments are the moves of the game. They affect to which propositions the players are committed. A winning move is a successful argument that takes the opponent's commitments as premises and shows how one's own conclusion follows


id: logic-20260505-9
file: data/en.wikipedia.org/wiki/Logic-9.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
The term "mathematical logic" is sometimes used as a synonym of "formal logic". But in a more restricted sense, it refers to the study of logic within mathematics. Major subareas include model theory, proof theory, set theory, and computability theory. Research in mathematical logic commonly addresses the mathematical properties of formal systems of logic. However, it can also include attempts to use logic to analyze mathematical reasoning or to establish logic-based foundations of mathematics. The latter was a major concern in early 20th-century mathematical logic, which pursued the program of logicism pioneered by philosopher-logicians such as Gottlob Frege, Alfred North Whitehead, and Bertrand Russell. Mathematical theories were supposed to be logical tautologies, and their program was to show this by means of a reduction of mathematics to logic. Many attempts to realize this program failed, from the crippling of Frege's project in his Grundgesetze by Russell's paradox, to the defeat of Hilbert's program by Gödel's incompleteness theorems. Set theory originated in the study of the infinite by Georg Cantor, and it has been the source of many of the most challenging and important issues in mathematical logic. They include Cantor's theorem, the status of the Axiom of Choice, the question


id: logic-20260505-10
file: data/en.wikipedia.org/wiki/Logic-10.md
category: reference
title: Logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Logic
date_saved: 2026-05-05T06:37:57.098691+00:00
instance: kb-cron
parent: logic-20260505-0
summary:
Logic was developed independently in several cultures during antiquity. One major early contributor was Aristotle, who developed term logic in his Organon and Prior Analytics. He was responsible for the introduction of the hypothetical syllogism and temporal modal logic. Further innovations include inductive logic as well as the discussion of new logical concepts such as terms, predicables, syllogisms, and propositions. Aristotelian logic was highly regarded in classical and medieval times, both in Europe and the Middle East. It remained in wide use in the West until the early 19th century. It has now been superseded by later work, though many of its key insights are still present in modern systems of logic. Ibn Sina (Avicenna) was the founder of Avicennian logic, which replaced Aristotelian logic as the dominant system of logic in the Islamic world. It influenced Western medieval writers such as Albertus Magnus and William of Ockham. Ibn Sina wrote on the hypothetical syllogism and on the propositional calculus. He developed an original "temporally modalized" syllogistic theory, involving temporal logic and modal logic. He also made use of inductive logic, such as his methods of agreement, difference, and concomitant variation, which are critical to the scientific method. Fakhr al-Din


id: mathematical-linguistics-20260505-0
file: data/en.wikipedia.org/wiki/Mathematical_linguistics-0.md
category: reference
title: Mathematical linguistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematical_linguistics
date_saved: 2026-05-05T06:37:58.346133+00:00
instance: kb-cron
parent: mathematical-linguistics-20260505-0
summary:
Mathematical linguistics is the application of mathematics to model phenomena and solve problems in general linguistics and theoretical linguistics. Mathematical linguistics has a significant amount of overlap with computational linguistics. == Discrete mathematics == Discrete mathematics is used in language modeling, including formal grammars, language representation, and historical linguistic trends. === Set theory === Semantic classes, word classes, natural classes, and the allophonic variations of each phoneme in a language are all examples of applied set theory. Set theory and concatenation theory are used extensively in phonetics and phonology. === Combinatorics === In phonotactics, combinatorics is useful for determining which sequences of phonemes are permissible in a given language, and for calculating the total number of possible syllables or words, based on a given set of phonological constraints. Combinatorics on words can reveal patterns within words, morphemes, and sentences. === Finite-state transducers === Context-sensitive rewriting rules of the form a → b / c _ d, used in linguistics to model phonological rules and sound change, are computationally equivalent to finite-state transducers, provided that application is nonrecursive, i.e. the rule is not allowed to rewrite the same substring twice. Weighted FSTs found applications in natural language processing, including machine translation,


id: mathematics-20260505-0
file: data/en.wikipedia.org/wiki/Mathematics-0.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
Mathematics is a field of study that discovers and organizes methods, theories, and theorems that are developed and proved either in response to the needs of empirical sciences or the needs of mathematics itself. There are many areas of mathematics, including number theory (the study of integers and their properties), algebra (the study of operations and the structures they form), geometry (the study of shapes and spaces that contain them), analysis (the study of approximating continuous changes), and set theory (presently used as a foundation for all mathematics). Mathematics involves the description and manipulation of abstract objects that are either abstractions from nature or purely abstract entities that are stipulated to have certain properties, called axioms. Mathematics uses pure reason to prove the properties of objects through proofs, which consist of a succession of applications of deductive rules to already established results. These results, called theorems, include previously proved theorems, axioms, and—in case of abstraction from nature—some basic properties that are considered true starting points of the theory under consideration. Mathematics is essential in the natural sciences, engineering, medicine, finance, computer science, and the social sciences. Although mathematics is extensively used for modeling phenomena, the fundamental truths of mathematics are


id: mathematics-20260505-1
file: data/en.wikipedia.org/wiki/Mathematics-1.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
Geometry is one of the oldest branches of mathematics. It started with empirical recipes concerning shapes, such as lines, angles and circles, which were developed mainly for the needs of surveying and architecture, but has since blossomed out into many other subfields. A fundamental innovation was the ancient Greeks' introduction of the concept of proofs, which require that every assertion must be proved. For example, it is not sufficient to verify by measurement that, say, two lengths are equal; their equality must be proven via reasoning from previously accepted results (theorems) and a few basic statements. The basic statements are not subject to proof because they are self-evident (postulates), or are part of the definition of the subject of study (axioms). This principle, foundational for all mathematics, was first elaborated for geometry, and was systematized by Euclid around 300 BC in his book Elements. The resulting Euclidean geometry is the study of shapes and their arrangements constructed from lines, planes and circles in the Euclidean plane (plane geometry) and the three-dimensional Euclidean space. Euclidean geometry was developed without change of methods or scope until the 17th century, when René Descartes introduced what is now called Cartesian coordinates. This constituted a


id: mathematics-20260505-2
file: data/en.wikipedia.org/wiki/Mathematics-2.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
group theory field theory vector spaces, whose study is essentially the same as linear algebra ring theory commutative algebra, which is the study of commutative rings, includes the study of polynomials, and is a foundational part of algebraic geometry homological algebra Lie algebra and Lie group theory Boolean algebra, which is widely used for the study of the logical structure of computers The study of types of algebraic structures as mathematical objects is the purpose of universal algebra and category theory. The latter applies to every mathematical structure (not only algebraic ones). At its origin, it was introduced, together with homological algebra for allowing the algebraic study of non-algebraic objects such as topological spaces; this particular area of application is called algebraic topology. === Calculus and analysis === Calculus, formerly called infinitesimal calculus, was introduced independently and simultaneously by 17th-century mathematicians Newton and Leibniz. It is fundamentally the study of the relationship between variables that depend continuously on each other. Calculus was expanded in the 18th century by Euler with the introduction of the concept of a function and many other results. Presently, "calculus" refers mainly to the elementary part of this theory, and "analysis" is commonly used for advanced


id: mathematics-20260505-3
file: data/en.wikipedia.org/wiki/Mathematics-3.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
Computational mathematics is the study of mathematical problems that are typically too large for human, numerical capacity. Part of computational mathematics involves numerical analysis, which is the study of methods for problems in analysis using functional analysis and approximation theory. Numerical analysis broadly includes the study of approximation and discretization, with special focus on rounding errors. Numerical analysis and, more broadly, scientific computing, also study non-analytic topics of mathematical science, especially algorithmic-matrix-and-graph theory. Other areas of computational mathematics include computer algebra and symbolic computation. == History == === Etymology === The word mathematics comes from the Ancient Greek word máthēma (μάθημα), meaning 'something learned, knowledge, mathematics', and the derived expression mathēmatikḗ tékhnē (μαθηματικὴ τέχνη), meaning 'mathematical science'. It entered the English language during the Late Middle English period through French and Latin. Traditionally, one of the two main schools of thought in Pythagoreanism was known as the mathēmatikoi (μαθηματικοί)—which at the time meant "learners" rather than "mathematicians" in the modern sense. The Pythagoreans were likely the first to constrain the use of the word to just the study of arithmetic and geometry. By the time of Aristotle (384–322 BC) this meaning was fully established. In Latin and English, until around


id: mathematics-20260505-4
file: data/en.wikipedia.org/wiki/Mathematics-4.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
During the Golden Age of Islam, especially during the 9th and 10th centuries, mathematics saw many important innovations building on Greek mathematics. The most notable achievement of Islamic mathematics was the development of algebra. Other achievements of the Islamic period include advances in spherical trigonometry and the addition of the decimal point to the Arabic numeral system. Many notable mathematicians from this period were Persian, such as Al-Khwarizmi, Omar Khayyam and Sharaf al-Dīn al-Ṭūsī. The Greek and Arabic mathematical texts were in turn translated to Latin during the Middle Ages and made available in Europe. During the early modern period, mathematics began to develop at an accelerating pace in Western Europe, with innovations that revolutionized mathematics, such as the introduction of variables and symbolic notation by François Viète (1540–1603), the introduction of logarithms by John Napier in 1614, which greatly simplified numerical calculations, especially for astronomy and marine navigation, the introduction of coordinates by René Descartes (1596–1650) for reducing geometry to algebra, and the development of calculus by Isaac Newton (1643–1727) and Gottfried Leibniz (1646–1716). Leonhard Euler (1707–1783), the most notable mathematician of the 18th century, unified these innovations into a single corpus with a standardized terminology, and completed them


id: mathematics-20260505-5
file: data/en.wikipedia.org/wiki/Mathematics-5.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
== Relationship with sciences == Mathematics is used in most sciences for modeling phenomena, which then allows predictions to be made from experimental laws. The independence of mathematical truth from any experimentation implies that the accuracy of such predictions depends only on the adequacy of the model. Inaccurate predictions, rather than being caused by invalid mathematical concepts, imply the need to change the mathematical model used. For example, the perihelion precession of Mercury could only be explained after the emergence of Einstein's general relativity, which replaced Newton's law of gravitation as a better mathematical model. There is still a philosophical debate whether mathematics is a science. However, in practice, mathematicians are typically grouped with scientists, and mathematics shares much in common with the physical sciences. Like them, it is falsifiable, which means in mathematics that, if a result or a theory is wrong, this can be proved by providing a counterexample. Similarly as in science, theories and results (theorems) are often obtained from experimentation. In mathematics, the experimentation may consist of computation on selected examples or of the study of figures or other representations of mathematical objects (often mind representations without physical support). For example, when asked how he came


id: mathematics-20260505-6
file: data/en.wikipedia.org/wiki/Mathematics-6.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
=== Unreasonable effectiveness === The unreasonable effectiveness of mathematics is a phenomenon that was named and first made explicit by physicist Eugene Wigner. It is the fact that many mathematical theories (even the "purest") have applications outside their initial object. These applications may be completely outside their initial area of mathematics, and may concern physical phenomena that were completely unknown when the mathematical theory was introduced. Examples of unexpected applications of mathematical theories can be found in many areas of mathematics. A notable example is the prime factorization of natural numbers that was discovered more than 2,000 years before its common use for secure internet communications through the RSA cryptosystem. A second historical example is the theory of ellipses. They were studied by the ancient Greek mathematicians as conic sections (that is, intersections of cones with planes). It was almost 2,000 years later that Johannes Kepler discovered that the trajectories of the planets are ellipses. In the 19th century, the internal development of geometry (pure mathematics) led to definition and study of non-Euclidean geometries, spaces of dimension higher than three and manifolds. At this time, these concepts seemed totally disconnected from the physical reality, but at the beginning of the


id: mathematics-20260505-7
file: data/en.wikipedia.org/wiki/Mathematics-7.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
Often the fundamental postulate of mathematical economics is that of the rational individual actor – Homo economicus (lit. 'economic man'). In this model, the individual seeks to maximize their self-interest, and always makes optimal choices using perfect information. This atomistic view of economics allows it to relatively easily mathematize its thinking, because individual calculations are transposed into mathematical calculations. Such mathematical modeling allows one to probe economic mechanisms. Some reject or criticize the concept of Homo economicus. Economists note that real people have limited information, make poor choices, and care about fairness and altruism, not just personal gain. Without mathematical modeling, it is hard to go beyond statistical observations or untestable speculation. Mathematical modeling allows economists to create structured frameworks to test hypotheses and analyze complex interactions. Models provide clarity and precision, enabling the translation of theoretical concepts into quantifiable predictions that can be tested against real-world data. At the start of the 20th century, there was a development to express historical movements in formulas. In 1922, Nikolai Kondratiev discerned the ~50-year-long Kondratiev cycle, which explains phases of economic growth or crisis. Towards the end of the 19th century, mathematicians extended their analysis into geopolitics. Peter Turchin developed cliodynamics in


id: mathematics-20260505-8
file: data/en.wikipedia.org/wiki/Mathematics-8.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
Mathematical reasoning requires rigor. This means that the definitions must be absolutely unambiguous and the proofs must be reducible to a succession of applications of inference rules, without any use of empirical evidence and intuition. Rigorous reasoning is not specific to mathematics, but, in mathematics, the standard of rigor is much higher than elsewhere. Despite mathematics' concision, rigorous proofs can require hundreds of pages to express, such as the 255-page Feit–Thompson theorem. The emergence of computer-assisted proofs has allowed proof lengths to further expand. The result of this trend is a philosophy of the quasi-empiricist proof that can not be considered infallible, but has a probability attached to it. The concept of rigor in mathematics dates back to ancient Greece, where their society encouraged logical, deductive reasoning. However, this rigorous approach would tend to discourage exploration of new approaches, such as irrational numbers and concepts of infinity. The method of demonstrating rigorous proof was enhanced in the sixteenth century through the use of symbolic notation. In the 18th century, social transition led to mathematicians earning their keep through teaching, which led to more careful thinking about the underlying concepts of mathematics. This produced more rigorous approaches, while transitioning from geometric


id: mathematics-20260505-9
file: data/en.wikipedia.org/wiki/Mathematics-9.md
category: reference
title: Mathematics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematics
date_saved: 2026-05-05T06:37:59.647491+00:00
instance: kb-cron
parent: mathematics-20260505-0
summary:
=== Psychology (aesthetic, creativity and intuition) === The validity of a mathematical theorem relies only on the rigor of its proof, which could theoretically be done automatically by a computer program. This does not mean that there is no place for creativity in a mathematical work. On the contrary, many important mathematical results (theorems) are solutions of problems that other mathematicians failed to solve, and the invention of a way for solving them may be a fundamental way of the solving process. An extreme example is Apery's theorem: Roger Apery provided only the ideas for a proof, and the formal proof was given only several months later by three other mathematicians. Creativity and rigor are not the only psychological aspects of the activity of mathematicians. Some mathematicians can see their activity as a game, more specifically as solving puzzles. This aspect of mathematical activity is emphasized in recreational mathematics. Mathematicians can find an aesthetic value to mathematics. Like beauty, it is hard to define, it is commonly related to elegance, which involves qualities like simplicity, symmetry, completeness, and generality. G. H. Hardy in A Mathematician's Apology expressed the belief that the aesthetic considerations are, in themselves, sufficient to justify the


id: formal-ontology-20260505-0
file: data/en.wikipedia.org/wiki/Formal_ontology-0.md
category: reference
title: Formal ontology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Formal_ontology
date_saved: 2026-05-05T06:38:00.980559+00:00
instance: kb-cron
parent: formal-ontology-20260505-0
summary:
In philosophy, the term formal ontology is used to refer to an ontology defined by axioms in a formal language with the goal to provide an unbiased (domain- and application-independent) view on reality, which can help the modeler of domain- or application-specific ontologies to avoid possibly erroneous ontological assumptions encountered in modeling large-scale ontologies. By maintaining an independent view on reality, a formal (upper level) ontology gains the following properties: indefinite expandability: the ontology remains consistent with increasing content. content and context independence: any kind of 'concept' can find its place. accommodate different levels of granularity. == Historical background == Theories on how to conceptualize reality date back as far as Plato and Aristotle. The term 'formal ontology' itself was coined by Edmund Husserl in the second edition of his Logical Investigations (1900–01), where it refers to an ontological counterpart of formal logic. Formal ontology for Husserl embraces an axiomatized mereology and a theory of dependence relations, for example between the qualities of an object and the object itself. 'Formal' signifies not the use of a formal-logical language, but rather: non-material, or in other words domain-independent (of universal application). Husserl's ideas on formal ontology were developed especially by his Polish


id: formal-ontology-20260505-1
file: data/en.wikipedia.org/wiki/Formal_ontology-1.md
category: reference
title: Formal ontology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Formal_ontology
date_saved: 2026-05-05T06:38:00.980559+00:00
instance: kb-cron
parent: formal-ontology-20260505-0
summary:
=== Formal ontology as a crossmapping hub: crossmapping taxonomies, databases and nonformal ontologies === Aligning terminologies and ontologies is not an easy task. The divergence of the underlying meaning of word descriptions and terms within different information sources is a well known obstacle for direct approaches to data integration and mapping. One single description may have a completely different meaning in one data source when compared with another. This is because different databases/terminologies often have a different viewpoint on similar items. They are usually built with a specific application-perspective in mind and their hierarchical structure represents this. A formal ontology, on the other hand, represents entities without a particular application scope. Its hierarchy reflects ontological principles and a basic class-subclass relation between its concepts. A consistent framework like this is ideal for crossmapping data sources. However, one cannot just integrate these external data sources in the formal ontology. A direct incorporation would lead to corruption of the framework and principles of the formal ontology. A formal ontology is a great crossmapping hub only if a complete distinction between the content and structure of the external information sources and the formal ontology itself is maintained. This is possible by specifying a


id: organoid-intelligence-20260505-0
file: data/en.wikipedia.org/wiki/Organoid_intelligence-0.md
category: reference
title: Organoid intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Organoid_intelligence
date_saved: 2026-05-05T06:38:02.272885+00:00
instance: kb-cron
parent: organoid-intelligence-20260505-0
summary:
Organoid intelligence (OI) is an emerging field of study in computer science and biology that develops and studies biological wetware computing using 3D cultures of human brain cells (or brain organoids) and brain-machine interface technologies. Such technologies may be referred to as OIs or the nervous filesystem. Organoid intelligent computer systems can be an example of biohybrid systems. == Differences with non-organic computing == As opposed to traditional non-organic silicon-based approaches, OI seeks to use lab-grown cerebral organoids to serve as "biological hardware". While these structures are still far from being able to think like a regular human brain and do not yet possess strong computing capabilities, OI research currently offers the potential to improve the understanding of brain development, learning and memory, potentially finding treatments for neurological disorders such as dementia. Thomas Hartung, a professor from Johns Hopkins University, argued in 2023 that "while silicon-based computers are certainly better with numbers, brains are better at learning." He noted that transistor density in computer chip may be approaching its limits, whereas brains, being wired differently, are more energy-efficient and can store large amounts of information. Some researchers claim that even though human brains are slower than machines at processing simple


id: oxford-model-20260505-0
file: data/en.wikipedia.org/wiki/Oxford_model-0.md
category: reference
title: Oxford model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Oxford_model
date_saved: 2026-05-05T06:38:03.437831+00:00
instance: kb-cron
parent: oxford-model-20260505-0
summary:
The Oxford Model or the Oxford macro econometric Model was a Macroeconomic model created by Lawrence Klein and Sir James Ball. It included a Phillips-type relation and led to an "explosion" of macroeconometric forecasting. == References ==


id: pattern-recognition-20260505-0
file: data/en.wikipedia.org/wiki/Pattern_recognition-0.md
category: reference
title: Pattern recognition
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pattern_recognition
date_saved: 2026-05-05T06:38:04.628167+00:00
instance: kb-cron
parent: pattern-recognition-20260505-0
summary:
Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess PR capabilities but their primary function is to distinguish and create emergent patterns. PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power. Pattern recognition systems are commonly trained from labeled "training" data. When no labeled data are available, other algorithms can be used to discover previously unknown patterns. KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition focuses more on the signal and also takes acquisition and signal processing into consideration. It originated in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition. In machine learning, pattern recognition is the


id: pattern-recognition-20260505-1
file: data/en.wikipedia.org/wiki/Pattern_recognition-1.md
category: reference
title: Pattern recognition
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pattern_recognition
date_saved: 2026-05-05T06:38:04.628167+00:00
instance: kb-cron
parent: pattern-recognition-20260505-0
summary:
Many common pattern recognition algorithms are probabilistic in nature, in that they use statistical inference to find the best label for a given instance. Unlike other algorithms, which simply output a "best" label, often probabilistic algorithms also output a probability of the instance being described by the given label. In addition, many probabilistic algorithms output a list of the N-best labels with associated probabilities, for some value of N, instead of simply a single best label. When the number of possible labels is fairly small (e.g., in the case of classification), N may be set so that the probability of all possible labels is output. Probabilistic algorithms have many advantages over non-probabilistic algorithms: They output a confidence value associated with their choice. (Note that some other algorithms may also output confidence values, but in general, only for probabilistic algorithms is this value mathematically grounded in probability theory. Non-probabilistic confidence values can in general not be given any specific meaning, and only used to compare against other confidence values output by the same algorithm.) Correspondingly, they can abstain when the confidence of choosing any particular output is too low. Because of the probabilities output, probabilistic pattern-recognition algorithms can be more effectively


id: pattern-recognition-20260505-2
file: data/en.wikipedia.org/wiki/Pattern_recognition-2.md
category: reference
title: Pattern recognition
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pattern_recognition
date_saved: 2026-05-05T06:38:04.628167+00:00
instance: kb-cron
parent: pattern-recognition-20260505-0
summary:
p ( l a b e l | x , θ ) = p ( x | l a b e l , θ ) p ( l a b e l | θ ) ∑ L ∈ all labels p ( x | L ) p ( L | θ ) . {\displaystyle p({\rm {label}}|{\boldsymbol {x}},{\boldsymbol {\theta }})={\frac {p({{\boldsymbol {x}}|{\rm {label,{\boldsymbol {\theta }}}}})p({\rm {label|{\boldsymbol {\theta }}}})}{\sum _{L\in {\text{all labels}}}p({\boldsymbol {x}}|L)p(L|{\boldsymbol {\theta }})}}.} When the labels are continuously distributed (e.g., in regression analysis), the denominator involves integration rather than summation: p ( l a b e l | x , θ ) = p ( x | l a b e l , θ ) p ( l a b e l | θ ) ∫ L ∈ all labels p ( x | L ) p ( L | θ ) d ⁡ L . {\displaystyle p({\rm {label}}|{\boldsymbol {x}},{\boldsymbol {\theta }})={\frac {p({{\boldsymbol {x}}|{\rm {label,{\boldsymbol {\theta }}}}})p({\rm {label|{\boldsymbol {\theta }}}})}{\int _{L\in {\text{all labels}}}p({\boldsymbol {x}}|L)p(L|{\boldsymbol {\theta }})\operatorname {d} L}}.} The value of θ {\displaystyle {\boldsymbol {\theta }}} is typically learned using maximum a posteriori (MAP) estimation. This finds the best value that simultaneously meets two conflicting objects: To perform as well


id: pattern-recognition-20260505-3
file: data/en.wikipedia.org/wiki/Pattern_recognition-3.md
category: reference
title: Pattern recognition
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pattern_recognition
date_saved: 2026-05-05T06:38:04.628167+00:00
instance: kb-cron
parent: pattern-recognition-20260505-0
summary:
identification and authentication: e.g., license plate recognition, fingerprint analysis, face detection/verification, and voice-based authentication. medical diagnosis: e.g., screening for cervical cancer (Papnet), breast tumors or heart sounds; defense: various navigation and guidance systems, target recognition systems, shape recognition technology etc. mobility: advanced driver assistance systems, autonomous vehicle technology, etc. In psychology, pattern recognition is used to make sense of and identify objects, and is closely related to perception. This explains how the sensory inputs humans receive are made meaningful. Pattern recognition can be thought of in two different ways. The first concerns template matching and the second concerns feature detection. A template is a pattern used to produce items of the same proportions. The template-matching hypothesis suggests that incoming stimuli are compared with templates in the long-term memory. If there is a match, the stimulus is identified. Feature detection models, such as the Pandemonium system for classifying letters (Selfridge, 1959), suggest that the stimuli are broken down into their component parts for identification. One observation is a capital E having three horizontal lines and one vertical line. == Algorithms == Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and on


id: perceptual-control-theory-20260505-0
file: data/en.wikipedia.org/wiki/Perceptual_control_theory-0.md
category: reference
title: Perceptual control theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Perceptual_control_theory
date_saved: 2026-05-05T06:38:05.894022+00:00
instance: kb-cron
parent: perceptual-control-theory-20260505-0
summary:
Perceptual control theory (PCT) is a model of behavior based on the properties of negative feedback control loops. A control loop maintains a sensed variable at or near a reference value by means of the effects of its outputs upon that variable, as mediated by physical properties of the environment. In engineering control theory, reference values are set by a user outside the system. An example is a thermostat. In a living organism, reference values for controlled perceptual variables are endogenously maintained. Biological homeostasis and reflexes are simple, low-level examples. The discovery of mathematical principles of control introduced a way to model a negative feedback loop closed through the environment (circular causation), which spawned perceptual control theory. It differs fundamentally from some models in behavioral and cognitive psychology that model stimuli as causes of behavior (linear causation). PCT research is published in experimental psychology, neuroscience, ethology, anthropology, linguistics, sociology, robotics, developmental psychology, organizational psychology and management, and a number of other fields. PCT has been applied to design and administration of educational systems, and has led to a psychotherapy called the method of levels. == Principles and differences from other theories == The perceptual control theory is deeply rooted in


id: perceptual-control-theory-20260505-1
file: data/en.wikipedia.org/wiki/Perceptual_control_theory-1.md
category: reference
title: Perceptual control theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Perceptual_control_theory
date_saved: 2026-05-05T06:38:05.894022+00:00
instance: kb-cron
parent: perceptual-control-theory-20260505-0
summary:
== Example == A simple negative feedback control system is a cruise control system for a car. A cruise control system has a sensor which "perceives" speed as the rate of spin of the drive shaft directly connected to the wheels. It also has a driver-adjustable 'goal' specifying a particular speed. The sensed speed is continuously compared against the specified speed by a device (called a "comparator") which subtracts the currently sensed input value from the stored goal value. The difference (the error signal) determines the throttle setting (the accelerator depression), so that the engine output is continuously varied to prevent the speed of the car from increasing or decreasing from that desired speed as environmental conditions change. If the speed of the car starts to drop below the goal-speed, for example when climbing a hill, the small increase in the error signal, amplified, causes engine output to increase, which keeps the error very nearly at zero. If the speed begins to exceed the goal, e.g. when going down a hill, the engine is throttled back so as to act as a brake, so again the speed is kept from departing more than a barely detectable amount from the goal


id: perceptual-control-theory-20260505-2
file: data/en.wikipedia.org/wiki/Perceptual_control_theory-2.md
category: reference
title: Perceptual control theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Perceptual_control_theory
date_saved: 2026-05-05T06:38:05.894022+00:00
instance: kb-cron
parent: perceptual-control-theory-20260505-0
summary:
== Mathematics == To illustrate the mathematical calculations employed in a PCT simulation, consider a pursuit tracking task in which the participant keeps a mouse cursor aligned with a moving target on a computer monitor. The model assumes that a perceptual signal within the participant represents the magnitude of the input quantity qi. (This has been demonstrated to be a rate of firing in a neuron, at least at the lowest levels.) In the tracking task, the input quantity is the vertical distance between the target position T and the cursor position C, and the random variation of the target position acts as the disturbance d of that input quantity. This suggests that the perceptual signal p quantitatively represents the cursor position C minus the target position T, as expressed in the equation p=C–T. Between the perception of target and cursor and the construction of the signal representing the distance between them there is a delay of τ milliseconds, so that the working perceptual signal at time t represents the target-to-cursor distance at a prior time, t – τ. Consequently, the equation used in the model is 1. p(t) = C(t–τ) – T(t–τ) The negative feedback control system receives a


id: perceptual-control-theory-20260505-3
file: data/en.wikipedia.org/wiki/Perceptual_control_theory-3.md
category: reference
title: Perceptual control theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Perceptual_control_theory
date_saved: 2026-05-05T06:38:05.894022+00:00
instance: kb-cron
parent: perceptual-control-theory-20260505-0
summary:
== Distinctions from engineering control theory == In the artificial systems that are specified by engineering control theory, the reference signal is considered to be an external input to the 'plant'. In engineering control theory, the reference signal or set point is public; in PCT, it is not, but rather must be deduced from the results of the test for controlled variables, as described above in the methodology section. This is because in living systems a reference signal is not an externally accessible input, but instead originates within the system. In the hierarchical model, error output of higher-level control loops, as described in the next section below, evokes the reference signal r from synapse-local memory, and the strength of r is proportional to the (weighted) strength of the error signal or signals from one or more higher-level systems. In engineering control systems, in the case where there are several such reference inputs, a 'Controller' is designed to manipulate those inputs so as to obtain the effect on the output of the system that is desired by the system's designer, and the task of a control theory (so conceived) is to calculate those manipulations so as to avoid instability and oscillation.


id: perceptual-control-theory-20260505-4
file: data/en.wikipedia.org/wiki/Perceptual_control_theory-4.md
category: reference
title: Perceptual control theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Perceptual_control_theory
date_saved: 2026-05-05T06:38:05.894022+00:00
instance: kb-cron
parent: perceptual-control-theory-20260505-0
summary:
The reorganization concept has led to a method of psychotherapy called the method of levels (MOL). Using MOL, the therapist aims to help the patient shift his or her awareness to higher levels of perception in order to resolve conflicts and allow reorganization to take place. == Neuroscience == === Learning === Currently, no one theory has been agreed upon to explain the synaptic, neuronal or systemic basis of learning. Prominent since 1973, however, is the idea that long-term potentiation (LTP) of populations of synapses induces learning through both pre- and postsynaptic mechanisms. LTP is a form of Hebbian learning, which proposed that high-frequency, tonic activation of a circuit of neurones increases the efficacy with which they are activated and the size of their response to a given stimulus as compared to the standard neurone (Hebb, 1949). These mechanisms are the principles behind Hebb's famously simple explanation: "Those that fire together, wire together". LTP has received much support since it was first observed by Terje Lømo in 1966 and is still the subject of many modern studies and clinical research. However, there are possible alternative mechanisms underlying LTP, as presented by Enoki, Hu, Hamilton and Fine in 2009, published in


id: perceptual-control-theory-20260505-5
file: data/en.wikipedia.org/wiki/Perceptual_control_theory-5.md
category: reference
title: Perceptual control theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Perceptual_control_theory
date_saved: 2026-05-05T06:38:05.894022+00:00
instance: kb-cron
parent: perceptual-control-theory-20260505-0
summary:
=== Hierarchical organisation === Botvinick in 2008 proposed that one of the founding insights of the cognitive revolution was the recognition of hierarchical structure in human behavior. Despite decades of research, however, the computational mechanisms underlying hierarchically organized behavior are still not fully understood. Bedre, Hoffman, Cooney & D'Esposito in 2009 proposed that the fundamental goal in cognitive neuroscience is to characterize the functional organization of the frontal cortex that supports the control of action. Recent neuroimaging data has supported the hypothesis that the frontal lobes are organized hierarchically, such that control is supported in progressively caudal regions as control moves to more concrete specification of action. However, it is still not clear whether lower-order control processors are differentially affected by impairments in higher-order control when between-level interactions are required to complete a task, or whether there are feedback influences of lower-level on higher-level control. Botvinik in 2008 found that all existing models of hierarchically structured behavior share at least one general assumption – that the hierarchical, part–whole organization of human action is mirrored in the internal or neural representations underlying it. Specifically, the assumption is that there exist representations not only of low-level motor behaviors, but also separable representations


id: perceptual-control-theory-20260505-6
file: data/en.wikipedia.org/wiki/Perceptual_control_theory-6.md
category: reference
title: Perceptual control theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Perceptual_control_theory
date_saved: 2026-05-05T06:38:05.894022+00:00
instance: kb-cron
parent: perceptual-control-theory-20260505-0
summary:
== Current situation and prospects == The preceding explanation of PCT principles provides justification of how this theory can provide a valid explanation of neural organisation and how it can explain some of the current issues of conceptual models. Perceptual control theory currently proposes a hierarchy of 11 levels of perceptions controlled by systems in the human mind and neural architecture. These are: intensity, sensation, configuration, transition, event, relationship, category, sequence, program, principle, and system concept. Diverse perceptual signals at a lower level (e.g. visual perceptions of intensities) are combined in an input function to construct a single perception at the higher level (e.g. visual perception of a color sensation). The perceptions that are constructed and controlled at the lower levels are passed along as the perceptual inputs at the higher levels. The higher levels in turn control by adjusting the reference levels (goals) of the lower levels, in effect telling the lower levels what to perceive. While many computer demonstrations of principles have been developed, the proposed higher levels are difficult to model because too little is known about how the brain works at these levels. Isolated higher-level control processes can be investigated, but models of an extensive hierarchy


id: prescriptive-analytics-20260505-0
file: data/en.wikipedia.org/wiki/Prescriptive_analytics-0.md
category: reference
title: Prescriptive analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prescriptive_analytics
date_saved: 2026-05-05T06:38:07.110448+00:00
instance: kb-cron
parent: prescriptive-analytics-20260505-0
summary:
Prescriptive analytics is a form of business analytics which suggests decision options for how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. It enables an enterprise to consider "the best course of action to take" in the light of information derived from descriptive and predictive analytics. == Overview == Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics. Referred to as the "final frontier of analytic capabilities", prescriptive analytics entails the application of mathematical and computational sciences and suggests decision options for how to take advantage of the results of descriptive and predictive phases. The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most management reporting – such as sales, marketing, operations, and finance – uses this type of post-mortem analysis. The next phase is predictive analytics. Predictive analytics answers the question of what is likely to happen. This is where historical data is


id: prescriptive-analytics-20260505-1
file: data/en.wikipedia.org/wiki/Prescriptive_analytics-1.md
category: reference
title: Prescriptive analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prescriptive_analytics
date_saved: 2026-05-05T06:38:07.110448+00:00
instance: kb-cron
parent: prescriptive-analytics-20260505-0
summary:
=== Unconventional Resource Development === With the value of the end product determined by global commodity economics, the basis of competition for operators in upstream E&P is the ability to effectively deploy capital to locate and extract resources more efficiently, effectively, predictably, and safely than their peers. In unconventional resource plays, operational efficiency and effectiveness is diminished by reservoir inconsistencies, and decision-making impaired by high degrees of uncertainty. These challenges manifest themselves in the form of low recovery factors and wide performance variations. Prescriptive Analytics software can accurately predict production and prescribe optimal configurations of controllable drilling, completion, and production variables by modeling numerous internal and external variables simultaneously, regardless of source, structure, size, or format. Prescriptive analytics software can also provide decision options and show the impact of each decision option so the operations managers can proactively take appropriate actions, on time, to guarantee future exploration and production performance, and maximize the economic value of assets at every point over the course of their serviceable lifetimes. === Oilfield Equipment Maintenance === In the realm of oilfield equipment maintenance, Prescriptive Analytics can optimize configuration, anticipate and prevent unplanned downtime, optimize field scheduling, and improve maintenance planning. According to General Electric,


id: queueing-theory-20260505-0
file: data/en.wikipedia.org/wiki/Queueing_theory-0.md
category: reference
title: Queueing theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Queueing_theory
date_saved: 2026-05-05T06:38:20.704411+00:00
instance: kb-cron
parent: queueing-theory-20260505-0
summary:
Queueing theory is the mathematical study of waiting lines, or queues. A queueing model is constructed so that queue lengths and waiting time can be predicted. Queueing theory is generally considered a branch of operations research because the results are often used when making business decisions about the resources needed to provide a service. Queueing theory has its origins in research by Agner Krarup Erlang, who created models to describe the system of incoming calls at the Copenhagen Telephone Exchange Company. These ideas were seminal to the field of teletraffic engineering and have since seen applications in telecommunications, traffic engineering, computing, project management, and particularly industrial engineering, where they are applied in the design of factories, shops, offices, and hospitals. == Description == Queueing theory is one of the major areas of study in the discipline of management science. Through management science, businesses are able to solve a variety of problems using different scientific and mathematical approaches. Queueing analysis is the probabilistic analysis of waiting lines, and thus the results, also referred to as the operating characteristics, are probabilistic rather than deterministic. The probability that n customers are in the queueing system, the average number of customers in the queueing


id: queueing-theory-20260505-1
file: data/en.wikipedia.org/wiki/Queueing_theory-1.md
category: reference
title: Queueing theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Queueing_theory
date_saved: 2026-05-05T06:38:20.704411+00:00
instance: kb-cron
parent: queueing-theory-20260505-0
summary:
P n = λ n − 1 λ n − 2 ⋯ λ 0 μ n μ n − 1 ⋯ μ 1 P 0 = P 0 ∏ i = 0 n − 1 λ i μ i + 1 {\displaystyle P_{n}={\frac {\lambda _{n-1}\lambda _{n-2}\cdots \lambda _{0}}{\mu _{n}\mu _{n-1}\cdots \mu _{1}}}P_{0}=P_{0}\prod _{i=0}^{n-1}{\frac {\lambda _{i}}{\mu _{i+1}}}} . The condition ∑ n = 0 ∞ P n = P 0 + P 0 ∑ n = 1 ∞ ∏ i = 0 n − 1 λ i μ i + 1 = 1 {\displaystyle \sum _{n=0}^{\infty }P_{n}=P_{0}+P_{0}\sum _{n=1}^{\infty }\prod _{i=0}^{n-1}{\frac {\lambda _{i}}{\mu _{i+1}}}=1} leads to P 0 = 1 1 + ∑ n = 1 ∞ ∏ i = 0 n − 1 λ i μ i + 1 {\displaystyle P_{0}={\frac {1}{1+\sum _{n=1}^{\infty }\prod _{i=0}^{n-1}{\frac {\lambda _{i}}{\mu _{i+1}}}}}} which, together with the equation for P n {\displaystyle P_{n}} ( n ≥ 1 ) {\displaystyle (n\geq 1)} , fully describes the required steady state probabilities. === Kendall's notation === Single queueing nodes are usually described using Kendall's notation in the form A/S/c where A describes the distribution of durations between each arrival to the queue, S the distribution of service times for


id: queueing-theory-20260505-2
file: data/en.wikipedia.org/wiki/Queueing_theory-2.md
category: reference
title: Queueing theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Queueing_theory
date_saved: 2026-05-05T06:38:20.704411+00:00
instance: kb-cron
parent: queueing-theory-20260505-0
summary:
M stands for Markov or memoryless, and means arrivals occur according to a Poisson process D stands for deterministic, and means jobs arriving at the queue require a fixed amount of service k describes the number of servers at the queueing node (k = 1, 2, 3, ...) If the node has more jobs than servers, then jobs will queue and wait for service. The M/G/1 queue was solved by Felix Pollaczek in 1930, a solution later recast in probabilistic terms by Aleksandr Khinchin and now known as the Pollaczek–Khinchine formula. After the 1940s, queueing theory became an area of research interest to mathematicians. In 1953, David George Kendall solved the GI/M/k queue and introduced the modern notation for queues, now known as Kendall's notation. In 1957, Pollaczek studied the GI/G/1 using an integral equation. John Kingman gave a formula for the mean waiting time in a G/G/1 queue, now known as Kingman's formula. Leonard Kleinrock worked on the application of queueing theory to message switching in the early 1960s and packet switching in the early 1970s. His initial contribution to this field was his doctoral thesis at the Massachusetts Institute of Technology in 1962, published in book form in


id: queueing-theory-20260505-3
file: data/en.wikipedia.org/wiki/Queueing_theory-3.md
category: reference
title: Queueing theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Queueing_theory
date_saved: 2026-05-05T06:38:20.704411+00:00
instance: kb-cron
parent: queueing-theory-20260505-0
summary:
=== Mean-field limits === Mean-field models consider the limiting behaviour of the empirical measure (proportion of queues in different states) as the number of queues m approaches infinity. The impact of other queues on any given queue in the network is approximated by a differential equation. The deterministic model converges to the same stationary distribution as the original model. === Heavy traffic/diffusion approximations === In a system with high occupancy rates (utilisation near 1), a heavy traffic approximation can be used to approximate the queueing length process by a reflected Brownian motion, Ornstein–Uhlenbeck process, or more general diffusion process. The number of dimensions of the Brownian process is equal to the number of queueing nodes, with the diffusion restricted to the non-negative orthant. === Fluid limits === Fluid models are continuous deterministic analogs of queueing networks obtained by taking the limit when the process is scaled in time and space, allowing heterogeneous objects. This scaled trajectory converges to a deterministic equation which allows the stability of the system to be proven. It is known that a queueing network can be stable but have an unstable fluid limit. === Queueing applications === Queueing theory finds widespread application in computer science and


id: risk-analysis-business-20260505-0
file: data/en.wikipedia.org/wiki/Risk_analysis_(business)-0.md
category: reference
title: Risk analysis (business)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Risk_analysis_(business)
date_saved: 2026-05-05T06:38:21.886836+00:00
instance: kb-cron
parent: risk-analysis-business-20260505-0
summary:
Risk analysis is the process of identifying and assessing risks that may jeopardize an organization's success. It typically fits into a larger risk management framework. Diligent risk analysis helps construct preventive measures to reduce the probability of incidents from occurring, as well as counter-measures to address incidents as they develop to minimize negative impacts on the organization. A popular method to perform risk analysis on IT systems is called facilitated risk analysis process (FRAP). == Facilitated risk analysis process == FRAP analyzes one system, application or segment of business processes at a time. FRAP assumes that additional efforts to develop precisely quantified risks are not cost-effective because: such estimates are time-consuming risk documentation becomes too voluminous for practical use specific loss estimates are generally not needed to determine if controls are needed. without assumptions, there is little risk analysis After identifying and categorizing risks, a team identifies the controls that could mitigate the risk. The decision for what controls are needed lies with the business manager. The team's conclusions as to what risks exist and what controls needed are documented, along with a related action plan for control implementation. Three of the most important risks a software company faces are:


id: statistics-20260505-0
file: data/en.wikipedia.org/wiki/Statistics-0.md
category: reference
title: Statistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistics
date_saved: 2026-05-05T06:38:24.334738+00:00
instance: kb-cron
parent: statistics-20260505-0
summary:
Statistics (from German: Statistik, orig. "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments. Statistics is deeply related to subjects like physics, chemistry, geography, geopolitics, and especially mathematics. When census data (comprising every member of the target population) cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Two main


id: statistics-20260505-1
file: data/en.wikipedia.org/wiki/Statistics-1.md
category: reference
title: Statistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistics
date_saved: 2026-05-05T06:38:24.334738+00:00
instance: kb-cron
parent: statistics-20260505-0
summary:
==== Sampling ==== When full census data cannot be collected, statisticians collect sample data by developing specific experiment designs and survey samples. Statistics itself also provides tools for prediction and forecasting through statistical models. To use a sample as a guide to an entire population, it is important that it truly represents the overall population. Representative sampling ensures that inferences and conclusions can safely be extended from the sample to the population as a whole. A major problem lies in determining the extent to which the chosen sample is actually representative. Statistics offers methods to estimate and correct for any bias in the sample and data collection procedures. There are also methods of experimental design that can lessen these issues at the outset of a study, strengthening its ability to discern truths about the population. Sampling theory is part of the mathematical discipline of probability theory. Probability is used in mathematical statistics to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures. The use of any statistical method is valid only when the system or population under consideration satisfies the assumptions of the method. The difference in point of view between classic probability theory


id: statistics-20260505-2
file: data/en.wikipedia.org/wiki/Statistics-2.md
category: reference
title: Statistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistics
date_saved: 2026-05-05T06:38:24.334738+00:00
instance: kb-cron
parent: statistics-20260505-0
summary:
Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one (injective) transformation. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation. Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit), and permit any linear transformation. Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature. Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables


id: statistics-20260505-3
file: data/en.wikipedia.org/wiki/Statistics-3.md
category: reference
title: Statistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistics
date_saved: 2026-05-05T06:38:24.334738+00:00
instance: kb-cron
parent: statistics-20260505-0
summary:
===== Null hypothesis and alternative hypothesis ===== Interpretation of statistical information can often involve the development of a null hypothesis which is usually (but not necessarily) that no relationship exists among variables or that no change occurred over time. The alternative hypothesis is the name of the hypothesis that contradicts the null hypothesis. The best illustration for a novice is the predicament encountered by a criminal trial. The null hypothesis, H0, asserts that the defendant is innocent, whereas the alternative hypothesis, H1, asserts that the defendant is guilty. The indictment comes because of suspicion of the guilt. The H0 (the status quo) stands in opposition to H1 and is maintained unless H1 is supported by evidence "beyond a reasonable doubt". However, "failure to reject H0" in this case does not imply innocence, but merely that the evidence was insufficient to convict. So the jury does not necessarily accept H0 but fails to reject H0. While one can not "prove" a null hypothesis, one can test how close it is to being true with a power test, which tests for type II errors. The null hypothesis cannot be proven true because it is already assumed to be true when the test


id: statistics-20260505-4
file: data/en.wikipedia.org/wiki/Statistics-4.md
category: reference
title: Statistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistics
date_saved: 2026-05-05T06:38:24.334738+00:00
instance: kb-cron
parent: statistics-20260505-0
summary:
The standard approach is to test a null hypothesis against an alternative hypothesis. A critical region is the set of values of the estimator that leads to refuting the null hypothesis. The probability of type I error is therefore the probability that the estimator belongs to the critical region given that null hypothesis is true (statistical significance) and the probability of type II error is the probability that the estimator does not belong to the critical region given that the alternative hypothesis is true. The statistical power of a test is the probability that it correctly rejects the null hypothesis when the null hypothesis is false. Referring to statistical significance does not necessarily mean that the overall result is significant in real-world terms. For example, in a large study of a drug it may be shown that the drug has a statistically significant but very small beneficial effect, such that it is unlikely to help the patient noticeably. Although in principle the acceptable level of statistical significance may be subject to debate, the significance level is the largest p-value that allows the test to reject the null hypothesis. This test is logically equivalent to saying that the p-value is the


id: statistics-20260505-5
file: data/en.wikipedia.org/wiki/Statistics-5.md
category: reference
title: Statistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistics
date_saved: 2026-05-05T06:38:24.334738+00:00
instance: kb-cron
parent: statistics-20260505-0
summary:
The mathematical foundations of statistics developed from discussions concerning games of chance among mathematicians such as Gerolamo Cardano, Blaise Pascal, Pierre de Fermat, and Christiaan Huygens. Although the idea of probability was already examined in ancient and medieval law and philosophy (such as the work of Juan Caramuel), probability theory as a mathematical discipline only took shape at the very end of the 17th century, particularly in Jacob Bernoulli's posthumous work Ars Conjectandi. This was the first book where the realm of games of chance and the realm of the probable (which concerned opinion, evidence, and argument) were combined and submitted to mathematical analysis. The method of least squares was first described by Adrien-Marie Legendre in 1805, though Carl Friedrich Gauss presumably made use of it a decade earlier in 1795. In the 1830s-1850s, "statistical offices" and national "statistical societies" were founded in Europe and America, and in the mid-19th century, the idea arose of "organized contacts between the statisticians of different countries although informal contacts occurred earlier". In those days, the name "statistics" referred mainly to "matters of state", and British statisticians were often called "statists". Belgian scientist Adolphe Quetelet (1796–1874) introduced the notion of the "average man" (l'homme


id: statistics-20260505-6
file: data/en.wikipedia.org/wiki/Statistics-6.md
category: reference
title: Statistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistics
date_saved: 2026-05-05T06:38:24.334738+00:00
instance: kb-cron
parent: statistics-20260505-0
summary:
=== Statistics in academia === Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, government, and business. Business statistics applies statistical methods in econometrics, auditing and production and operations, including services improvement and marketing research. A study of two journals in tropical biology found that the 12 most frequent statistical tests are: analysis of variance (ANOVA), chi-squared test, Student's t-test, linear regression, Pearson's correlation coefficient, Mann-Whitney U test, Kruskal-Wallis test, Shannon's diversity index, Tukey's range test, cluster analysis, Spearman's rank correlation coefficient and principal component analysis. A typical statistics course covers descriptive statistics, probability, binomial and normal distributions, test of hypotheses and confidence intervals, linear regression, and correlation. Modern fundamental statistical courses for undergraduate students focus on correct test selection, results interpretation, and use of free statistics software. === Statistical computing === The rapid and sustained increases in computing power starting from the second half of the 20th century have had a substantial impact on the practice of statistical science. Early statistical models were almost always from the class of linear models, but powerful computers, coupled with suitable numerical algorithms, caused an increased interest in nonlinear models (such as neural networks) as well as


id: statistics-20260505-7
file: data/en.wikipedia.org/wiki/Statistics-7.md
category: reference
title: Statistics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistics
date_saved: 2026-05-05T06:38:24.334738+00:00
instance: kb-cron
parent: statistics-20260505-0
summary:
Who says so? (Do they have an axe to grind?) How do they know? (Do they have the resources to know the facts?) What's missing? (Do they give us a complete picture?) Did someone change the subject? (Do they offer us the right answer to the wrong problem?) Does it make sense? (Is their conclusion logical and consistent with what we already know?) === Misinterpretation: correlation === The concept of correlation is particularly noteworthy for the potential confusion it can cause. Statistical analysis of a data set often reveals that two variables (properties) of the population under consideration tend to vary together, as if they were connected. For example, a study of annual income that also looks at age of death, might find that poor people tend to have shorter lives than affluent people. The two variables are said to be correlated; however, they may or may not be the cause of one another. The correlation phenomena could be caused by a third, previously unconsidered phenomenon, called a lurking variable or confounding variable. For this reason, there is no way to immediately infer the existence of a causal relationship between the two variables. == See also == Foundations and major


id: systems-ecology-20260505-0
file: data/en.wikipedia.org/wiki/Systems_ecology-0.md
category: reference
title: Systems ecology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Systems_ecology
date_saved: 2026-05-05T06:38:25.582540+00:00
instance: kb-cron
parent: systems-ecology-20260505-0
summary:
Systems ecology is an interdisciplinary field of ecology, a subset of Earth system science, that takes a holistic approach to the study of ecological systems, especially ecosystems. Systems ecology can be seen as an application of general systems theory to ecology. Central to the systems ecology approach is the idea that an ecosystem is a complex system exhibiting emergent properties. Systems ecology focuses on interactions and transactions within and between biological and ecological systems, and is especially concerned with the way the functioning of ecosystems can be influenced by human interventions. It uses and extends concepts from thermodynamics and develops other macroscopic descriptions of complex systems. == Overview == Systems ecology seeks a holistic view of the interactions and transactions within and between biological and ecological systems. Systems ecologists realise that the function of any ecosystem can be influenced by human economics in fundamental ways. They have therefore taken an additional transdisciplinary step by including economics in the consideration of ecological-economic systems. In the words of R.L. Kitching: Systems ecology can be defined as the approach to the study of ecology of organisms using the techniques and philosophy of systems analysis: that is, the methods and tools developed, largely in


id: systems-science-20260505-0
file: data/en.wikipedia.org/wiki/Systems_science-0.md
category: reference
title: Systems science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Systems_science
date_saved: 2026-05-05T06:38:26.826730+00:00
instance: kb-cron
parent: systems-science-20260505-0
summary:
Systems science, also referred to as systems research or simply systems, is a transdisciplinary field that is concerned with understanding simple and complex systems in nature and society, which leads to the advancements of formal, natural, social, and applied attributions throughout engineering, technology, and science itself. To systems scientists, the world can be understood as a system of systems. The field aims to develop transdisciplinary foundations that are applicable in a variety of areas, such as psychology, biology, medicine, communication, business, technology, computer science, engineering, and social sciences. Themes commonly stressed in system science are (a) holistic view, (b) interaction between a system and its embedding environment, and (c) complex (often subtle) trajectories of dynamic behavior that sometimes are stable (and thus reinforcing), while at various 'boundary conditions' can become wildly unstable (and thus destructive). Concerns about Earth-scale biosphere/geosphere dynamics is an example of the nature of problems to which systems science seeks to contribute meaningful insights. == Associated fields == The systems sciences are a broad array of fields. One way of conceiving of these is in three groups: fields that have developed systems ideas primarily through theory; those that have done so primarily through practical engagements with problem


id: theoretical-computer-science-20260505-0
file: data/en.wikipedia.org/wiki/Theoretical_computer_science-0.md
category: reference
title: Theoretical computer science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Theoretical_computer_science
date_saved: 2026-05-05T06:38:28.140736+00:00
instance: kb-cron
parent: theoretical-computer-science-20260505-0
summary:
Theoretical computer science is a subfield of computer science and mathematics that focuses on the abstract and mathematical foundations of computation. It is difficult to circumscribe the theoretical areas precisely. The ACM's Special Interest Group on Algorithms and Computation Theory (SIGACT) provides the following description: TCS covers a wide variety of topics including algorithms, data structures, computational complexity, parallel and distributed computation, probabilistic computation, quantum computation, automata theory, information theory, cryptography, program semantics and verification, algorithmic game theory, machine learning, computational biology, computational economics, computational geometry, and computational number theory and algebra. Work in this field is often distinguished by its emphasis on mathematical technique and rigor. == History == Theoretical computer science is closely related to mathematics and logic. In the 20th century, it emancipated itself and became an independent discipline. Pioneers of the discipline were Kurt Gödel, Alonzo Church, Alan Turing, Stephen Cole Kleene, Claude Shannon, John von Neumann and Noam Chomsky. While logical inference and mathematical proof had existed previously, in 1931 Kurt Gödel proved with his incompleteness theorem that there are fundamental limitations on what statements could be proved or disproved. Information theory was added to the field with a 1948 mathematical theory of communication by


id: theoretical-computer-science-20260505-1
file: data/en.wikipedia.org/wiki/Theoretical_computer_science-1.md
category: reference
title: Theoretical computer science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Theoretical_computer_science
date_saved: 2026-05-05T06:38:28.140736+00:00
instance: kb-cron
parent: theoretical-computer-science-20260505-0
summary:
Computational number theory, also known as algorithmic number theory, is the study of algorithms for performing number theoretic computations. The best known problem in the field is integer factorization. === Cryptography === Cryptography is the practice and study of techniques for secure communication in the presence of third parties (called adversaries). More generally, it is about constructing and analyzing protocols that overcome the influence of adversaries and that are related to various aspects in information security such as data confidentiality, data integrity, authentication, and non-repudiation. Modern cryptography intersects the disciplines of mathematics, computer science, and electrical engineering. Applications of cryptography include ATM cards, computer passwords, and electronic commerce. Modern cryptography is heavily based on mathematical theory and computer science practice; cryptographic algorithms are designed around computational hardness assumptions, making such algorithms hard to break in practice by any adversary. It is theoretically possible to break such a system, but it is infeasible to do so by any known practical means. These schemes are therefore termed computationally secure; theoretical advances, e.g., improvements in integer factorization algorithms, and faster computing technology require these solutions to be continually adapted. There exist information-theoretically secure schemes that provably cannot be broken even with unlimited computing


id: theoretical-computer-science-20260505-2
file: data/en.wikipedia.org/wiki/Theoretical_computer_science-2.md
category: reference
title: Theoretical computer science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Theoretical_computer_science
date_saved: 2026-05-05T06:38:28.140736+00:00
instance: kb-cron
parent: theoretical-computer-science-20260505-0
summary:
Machine learning is a scientific discipline that deals with the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions. Machine learning can be considered a subfield of computer science and statistics. It has strong ties to artificial intelligence and optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets of the same field." === Natural computation === === Parallel computation === Parallel computing is a form of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved "in parallel". There are several different forms of parallel computing: bit-level, instruction level, data, and task parallelism. Parallelism has


id: theoretical-computer-science-20260505-3
file: data/en.wikipedia.org/wiki/Theoretical_computer_science-3.md
category: reference
title: Theoretical computer science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Theoretical_computer_science
date_saved: 2026-05-05T06:38:28.140736+00:00
instance: kb-cron
parent: theoretical-computer-science-20260505-0
summary:
== Journals and newsletters == Discrete Mathematics and Theoretical Computer Science Information and Computation Theory of Computing (open access journal) Formal Aspects of Computing Journal of the ACM SIAM Journal on Computing (SICOMP) SIGACT News Theoretical Computer Science Theory of Computing Systems TheoretiCS (open access journal) International Journal of Foundations of Computer Science Chicago Journal of Theoretical Computer Science (open access journal) Foundations and Trends in Theoretical Computer Science Journal of Automata, Languages and Combinatorics Acta Informatica Fundamenta Informaticae ACM Transactions on Computation Theory Computational Complexity Journal of Complexity ACM Transactions on Algorithms Information Processing Letters Open Computer Science (open access journal) == Conferences == Annual ACM Symposium on Theory of Computing (STOC) Annual IEEE Symposium on Foundations of Computer Science (FOCS) Innovations in Theoretical Computer Science (ITCS) Mathematical Foundations of Computer Science (MFCS) International Computer Science Symposium in Russia (CSR) ACM–SIAM Symposium on Discrete Algorithms (SODA) IEEE Symposium on Logic in Computer Science (LICS) Computational Complexity Conference (CCC) International Colloquium on Automata, Languages and Programming (ICALP) Annual Symposium on Computational Geometry (SoCG) ACM Symposium on Principles of Distributed Computing (PODC) ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) Annual Conference on Learning Theory (COLT) International Conference on Current


id: natural-science-20260505-0
file: data/en.wikipedia.org/wiki/Natural_science-0.md
category: reference
title: Natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_science
date_saved: 2026-05-05T06:38:29.805658+00:00
instance: kb-cron
parent: natural-science-20260505-0
summary:
Natural science or empirical science is a branch of science concerned with the description, understanding, and prediction of natural phenomena, based on empirical evidence from observation and experimentation. Mechanisms such as peer review and reproducibility of findings are used to try to ensure the validity of scientific advances. Natural science can be divided into two main branches: life science and physical science. Life science is alternatively known as biology. Physical science is subdivided into physics, astronomy, Earth science, and chemistry. These branches of natural science may be further divided into more specialized branches, also known as fields. As empirical sciences, natural sciences use tools from the formal sciences, such as mathematics and logic, converting information about nature into measurements that can be explained as clear statements of the "laws of nature". Modern natural science succeeded more classical approaches to natural philosophy. Galileo Galilei, Johannes Kepler, René Descartes, Francis Bacon, and Isaac Newton debated the benefits of a more mathematical as against a more experimental method in investigating nature. Still, philosophical perspectives, conjectures, and presuppositions, often overlooked, remain necessary in natural science. Systematic data collection, including discovery science, succeeded natural history, which emerged in the 16th century by describing and classifying


id: natural-science-20260505-1
file: data/en.wikipedia.org/wiki/Natural_science-1.md
category: reference
title: Natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_science
date_saved: 2026-05-05T06:38:29.805658+00:00
instance: kb-cron
parent: natural-science-20260505-0
summary:
Planetary science, or planetology, is the scientific study of planets, which include terrestrial planets like the Earth, and other types of planets, such as gas giants and ice giants. Planetary science also concerns other celestial bodies, such as dwarf planets, moons, asteroids, and comets. This largely includes the Solar System, but recently has started to expand to exoplanets, particularly terrestrial exoplanets. It explores various objects, spanning from micrometeoroids to gas giants, to establish their composition, movements, genesis, interrelation, and past. Planetary science is an interdisciplinary domain, having originated from astronomy and Earth science, and currently encompassing a multitude of areas, such as planetary geology, cosmochemistry, atmospheric science, physics, oceanography, hydrology, theoretical planetology, glaciology, and exoplanetology. Related fields encompass space physics, which delves into the impact of the Sun on the bodies in the Solar System, and astrobiology. Planetary science comprises interconnected observational and theoretical branches. Observational research entails a combination of space exploration, primarily through robotic spacecraft missions utilizing remote sensing, and comparative experimental work conducted in Earth-based laboratories. The theoretical aspect involves extensive mathematical modelling and computer simulation. Typically, planetary scientists are situated within astronomy and physics or Earth sciences departments in universities or research centers. However, there are


id: natural-science-20260505-2
file: data/en.wikipedia.org/wiki/Natural_science-2.md
category: reference
title: Natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_science
date_saved: 2026-05-05T06:38:29.805658+00:00
instance: kb-cron
parent: natural-science-20260505-0
summary:
This discipline is the science of celestial objects and phenomena that originate outside the Earth's atmosphere. It is concerned with the evolution, physics, chemistry, meteorology, geology, and motion of celestial objects, as well as the formation and development of the universe. Astronomy includes examining, studying, and modeling stars, planets, and comets. Most of the information used by astronomers is gathered by remote observation. However, some laboratory reproduction of celestial phenomena has been performed (such as the molecular chemistry of the interstellar medium). There is considerable overlap with physics and in some areas of earth science. There are also interdisciplinary fields such as astrophysics, planetary sciences, and cosmology, along with allied disciplines such as space physics and astrochemistry. While the study of celestial features and phenomena can be traced back to antiquity, the scientific methodology of this field began to develop in the middle of the 17th century. A key factor was Galileo's introduction of the telescope to examine the night sky in more detail. The mathematical treatment of astronomy began with Newton's development of celestial mechanics and the laws of gravitation. However, it was triggered by earlier work of astronomers such as Kepler. By the 19th century, astronomy had developed


id: natural-science-20260505-3
file: data/en.wikipedia.org/wiki/Natural_science-3.md
category: reference
title: Natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_science
date_saved: 2026-05-05T06:38:29.805658+00:00
instance: kb-cron
parent: natural-science-20260505-0
summary:
Some scholars trace the origins of natural science as far back as pre-literate human societies, where understanding the natural world was necessary for survival. People observed and built up knowledge about the behaviour of animals and the usefulness of plants as food and medicine, which was passed down from generation to generation. These primitive understandings gave way to more formalized inquiry around 3500 to 3000 BC in the Mesopotamian and Ancient Egyptian cultures, which produced the first known written evidence of natural philosophy, the precursor of natural science. While the writings show an interest in astronomy, mathematics, and other aspects of the physical world, the ultimate aim of inquiry about nature's workings was, in all cases, religious or mythological, not scientific. A tradition of scientific inquiry also emerged in Ancient China, where Taoist alchemists and philosophers experimented with elixirs to extend life and cure ailments. They focused on the yin and yang, or contrasting elements in nature; the yin was associated with femininity and coldness, while yang was associated with masculinity and warmth. The five phases – fire, earth, metal, wood, and water – described a cycle of transformations in nature. The water turned into wood, which turned into the


id: natural-science-20260505-4
file: data/en.wikipedia.org/wiki/Natural_science-4.md
category: reference
title: Natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_science
date_saved: 2026-05-05T06:38:29.805658+00:00
instance: kb-cron
parent: natural-science-20260505-0
summary:
Aristotle's works and other Greek natural philosophy did not reach the West until about the middle of the 12th century, when works were translated from Greek and Arabic into Latin. The development of European civilization later in the Middle Ages brought with it further advances in natural philosophy. European inventions such as the horseshoe, horse collar and crop rotation allowed for rapid population growth, eventually giving way to urbanization and the foundation of schools connected to monasteries and cathedrals in modern-day France and England. Aided by the schools, an approach to Christian theology developed that sought to answer questions about nature and other subjects using logic. This approach, however, was seen by some detractors as heresy. By the 12th century, Western European scholars and philosophers came into contact with a body of knowledge of which they had previously been ignorant: a large corpus of works in Greek and Arabic that were preserved by Islamic scholars. Through translation into Latin, Western Europe was introduced to Aristotle and his natural philosophy. These works were taught at new universities in Paris and Oxford by the early 13th century, although the practice was frowned upon by the Catholic church. A 1210 decree from the


id: natural-science-20260505-5
file: data/en.wikipedia.org/wiki/Natural_science-5.md
category: reference
title: Natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_science
date_saved: 2026-05-05T06:38:29.805658+00:00
instance: kb-cron
parent: natural-science-20260505-0
summary:
The titles of Galileo's work Two New Sciences and Johannes Kepler's New Astronomy underscored the atmosphere of change that took hold in the 17th century as Aristotle was dismissed in favour of novel methods of inquiry into the natural world. Bacon was instrumental in popularizing this change; he argued that people should use the arts and sciences to gain dominion over nature. To achieve this, he wrote that "human life [must] be endowed with discoveries and powers." He defined natural philosophy as "the knowledge of Causes and secret motions of things; and enlarging the bounds of Human Empire, to the effecting of all things possible." Bacon proposed that scientific inquiry be supported by the state and fed by the collaborative research of scientists, a vision that was unprecedented in its scope, ambition, and forms at the time. Natural philosophers came to view nature increasingly as a mechanism that could be taken apart and understood, much like a complex clock. Natural philosophers including Isaac Newton, Evangelista Torricelli and Francesco Redi, Edme Mariotte, Jean-Baptiste Denis and Jacques Rohault conducted experiments focusing on the flow of water, measuring atmospheric pressure using a barometer and disproving spontaneous generation. Scientific societies and scientific journals emerged


id: natural-science-20260505-6
file: data/en.wikipedia.org/wiki/Natural_science-6.md
category: reference
title: Natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_science
date_saved: 2026-05-05T06:38:29.805658+00:00
instance: kb-cron
parent: natural-science-20260505-0
summary:
Aside from the logical and mathematical sciences, there are three great branches of natural science which stand apart by reason of the variety of far reaching deductions drawn from a small number of primary postulates — they are mechanics, electrodynamics, and thermodynamics. Today, natural sciences are more commonly divided into life sciences, such as botany and zoology, and physical sciences, which include physics, chemistry, astronomy, and Earth sciences. == See also == Branches of science Empiricism List of academic disciplines and sub-disciplines Natural history Natural Sciences (Cambridge), for the Tripos at the University of Cambridge == References == === Bibliography === == Further reading == Defining Natural Sciences Ledoux, S. F., 2002: Defining Natural Sciences, Behaviourology Today, 5(1), 34–36. Stokes, Donald E. (1997). Pasteur's Quadrant: Basic Science and Technological Innovation. Revised and translated by Albert V. Carozzi and Marguerite Carozzi. Washington, D.C.: Brookings Institution Press. ISBN 978-0-8157-8177-6. The History of Recent Science and Technology Natural Sciences Contains updated information on research in the Natural Sciences including biology, geography and the applied life and earth sciences. Reviews of Books About Natural Science This site contains over 50 previously published reviews of books about natural science, plus selected essays on timely topics


id: outline-of-natural-science-20260505-0
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-0.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
The following outline is provided as an overview of and topical guide to natural science: Natural science – a major branch of science that tries to explain, and predict, nature's phenomena based on empirical evidence. In natural science, hypothesis must be verified scientifically to be regarded as scientific theory. Validity, accuracy, and social mechanisms ensuring quality control, such as peer review and repeatability of findings, are amongst the criteria and methods used for this purpose. Natural science can be broken into 2 main branches: life science, and physical science. Each of these branches, and all of their sub-branches, are referred to as natural sciences. == Overview == Natural science can be described as all of the following: Branch of science – systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe. Major category of academic disciplines – an academic discipline is focused study in one academic field or profession. A discipline incorporates expertise, people, projects, communities, challenges, studies, inquiry, and research areas that are strongly associated with academic areas of study or areas of professional practice. For example, the branches of science are commonly referred to as the scientific disciplines. For instance,


id: outline-of-natural-science-20260505-1
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-1.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
==== Physics ==== Physics – physical science that studies matter and its motion through space-time, and related concepts such as energy and force. Acoustics – study of mechanical waves in solids, liquids, and gases (such as vibration and sound) Agrophysics – study of physics applied to agroecosystems Soil physics – study of soil physical properties and processes. Atmospheric physics – study of the application of physics to the atmosphere Atomic, molecular, and optical physics – study of how matter and light interact Biophysics – study of physical processes relating to biology Medical physics – application of physics concepts, theories and methods to medicine. Neurophysics – branch of biophysics dealing with the nervous system. Chemical physics – branch of physics that studies chemical processes from the point of view of physics. Computational physics – study and implementation of numerical algorithms to solve problems in physics for which a quantitative theory already exists. Condensed matter physics – study of the physical properties of condensed phases of matter. Cryogenics – cryogenics is the study of the production of very low temperature (below −150 °C, −238 °F or 123K) and the behavior of materials at those temperatures. Dynamics – study of the causes of


id: outline-of-natural-science-20260505-2
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-2.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
==== Chemistry ==== Chemistry – physical science of atomic matter (matter that is composed of chemical elements), especially its chemical reactions, but also including its properties, structure, composition, behavior, and changes as they relate the chemical reactions Analytical chemistry – study of the separation, identification, and quantification of the chemical components of natural and artificial materials. Astrochemistry – study of the abundance and reactions of chemical elements and molecules in the universe, and their interaction with radiation. Cosmochemistry – study of the chemical composition of matter in the universe and the processes that led to those compositions Atmospheric chemistry – branch of atmospheric science in which the chemistry of the Earth's atmosphere and that of other planets is studied. It is a multidisciplinary field of research and draws on environmental chemistry, physics, meteorology, computer modeling, oceanography, geology and volcanology and other disciplines Biochemistry – study of chemical processes in living organisms, including, but not limited to, living matter. Biochemistry governs all living organisms and living processes. Agrochemistry – study of both chemistry and biochemistry which are important in agricultural production, the processing of raw products into foods and beverages, and in environmental monitoring and remediation. Bioinorganic chemistry – examines the


id: outline-of-natural-science-20260505-3
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-3.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
Materials science – interdisciplinary field applying the properties of matter to various areas of science and engineering. Nanotechnology – study of manipulating matter on an atomic and molecular scale Oenology – science and study of all aspects of wine and winemaking except vine-growing and grape-harvesting, which is a subfield called viticulture. Spectroscopy – study of the interaction between matter and radiated energy Surface science – Surface science is the study of physical and chemical phenomena that occur at the interface of two phases, including solid–liquid interfaces, solid–gas interfaces, solid–vacuum interfaces, and liquid–gas interfaces.


id: outline-of-natural-science-20260505-4
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-4.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
==== Earth science ==== Earth science – all-embracing term for the sciences related to the planet Earth. Earth science, and all of its branches, are branches of physical science. Atmospheric sciences – umbrella term for the study of the atmosphere, its processes, the effects other systems have on the atmosphere, and the effects of the atmosphere on these other systems. Biogeography – study of the distribution of species (biology), organisms, and ecosystems in geographic space and through geological time. Cartography – study and practice of making maps or globes. Climatology – study of climate, scientifically defined as weather conditions averaged over a period of time Coastal geography – study of the dynamic interface between the ocean and the land, incorporating both the physical geography (i.e. coastal geomorphology, geology and oceanography) and the human geography (sociology and history) of the coast. Environmental science – an integrated, quantitative, and interdisciplinary approach to the study of environmental systems. Ecology – scientific study of the distribution and abundance of living organisms and how the distribution and abundance are affected by interactions between the organisms and their environment. Freshwater biology – scientific biological study of freshwater ecosystems and is a branch of Limnology Marine biology


id: outline-of-natural-science-20260505-5
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-5.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
==== Biology ==== Biology – study of living organisms. Aerobiology – study of airborne organic particles Agriculture – study of producing crops from the land, with an emphasis on practical applications Anatomy – study of form and function, in plants, animals, and other organisms, or specifically in humans Human anatomy – scientific study of the morphology of the adult human. Biochemistry – study of the chemical reactions required for life to exist and function, usually a focus on the cellular level Bioengineering – study of biology through the means of engineering with an emphasis on applied knowledge and especially related to biotechnology Biogeography – study of the distribution of species spatially and temporally Bioinformatics – use of information technology and statistics for the study, collection, and storage of genomic and other biological data Biomathematics or mathematical biology – quantitative or mathematical study of biological processes, with an emphasis on modeling Biomechanics – often considered a branch of medicine, the study of the mechanics of living beings, with an emphasis on applied use through prosthetics or orthotics Biomedical sciences Biomedical research – study of the human body in health and disease Clinical research Biophysics – study of biological processes through physics,


id: outline-of-natural-science-20260505-6
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-6.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
=== History of the branches of natural science === Natural philosophy History of physical science – history of the branch of natural science that studies non-living systems, in contrast to the biological sciences. It in turn has many branches, each referred to as a "physical science", together called the "physical sciences". However, the term "physical" creates an unintended, somewhat arbitrary distinction, since many branches of physical science also study biological phenomena (organic chemistry, for example). History of physics – history of the physical science that studies matter and its motion through space-time, and related concepts such as energy and force History of acoustics – history of the study of mechanical waves in solids, liquids, and gases (such as vibration and sound) History of agrophysics – history of the study of physics applied to agroecosystems History of soil physics – history of the study of soil physical properties and processes. History of astrophysics – history of the study of the physical aspects of celestial objects History of astronomy – history of the studies the universe beyond Earth, including its formation and development, and the evolution, physics, chemistry, meteorology, and motion of celestial objects (such as galaxies, planets, etc.) and phenomena that


id: outline-of-natural-science-20260505-7
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-7.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
History of solid state physics – history of the study of rigid matter, or solids, through methods such as quantum mechanics, crystallography, electromagnetism, and metallurgy. History of vehicle dynamics – history of the dynamics of vehicles, here assumed to be ground vehicles. History of chemistry – history of the physical science of atomic matter (matter that is composed of chemical elements), especially its chemical reactions, but also including its properties, structure, composition, behavior, and changes as they relate the chemical reactions History of analytical chemistry – history of the study of the separation, identification, and quantification of the chemical components of natural and artificial materials. History of astrochemistry – history of the study of the abundance and reactions of chemical elements and molecules in the universe, and their interaction with radiation. History of cosmochemistry – history of the study of the chemical composition of matter in the universe and the processes that led to those compositions History of atmospheric chemistry – history of the branch of atmospheric science in which the chemistry of the Earth's atmosphere and that of other planets is studied. It is a multidisciplinary field of research and draws on environmental chemistry, physics, meteorology, computer modeling, oceanography,


id: outline-of-natural-science-20260505-8
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-8.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
History of quantum chemistry – history of the branch of chemistry whose primary focus is the application of quantum mechanics in physical models and experiments of chemical systems. History of sonochemistry – history of the study of the effect of sonic waves and wave properties on chemical systems. History of stereochemistry – history of the study of the relative spatial arrangement of atoms within molecules. History of supramolecular chemistry – history of the area of chemistry beyond the molecules and focuses on the chemical systems made up of a discrete number of assembled molecular subunits or components. History of thermochemistry – history of the study of the energy and heat associated with chemical reactions and/or physical transformations. History of phytochemistry – history of the strict sense of the word the study of phytochemicals. History of polymer chemistry – history of the multidisciplinary science that deals with the chemical synthesis and chemical properties of polymers or macromolecules. History of solid-state chemistry – history of the study of the synthesis, structure, and properties of solid phase materials, particularly, but not necessarily exclusively of, non-molecular solids Multidisciplinary fields involving chemistry History of chemical biology – history of the scientific discipline spanning the fields


id: outline-of-natural-science-20260505-9
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-9.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
History of oceanography – history of the branch of Earth science that studies the ocean History of paleoclimatology – history of the study of changes in climate taken on the scale of the entire history of Earth History of paleontology – history of the study of prehistoric life History of petrology – history of the branch of geology that studies the origin, composition, distribution and structure of rocks. History of limnology – history of the study of inland waters History of seismology – history of the scientific study of earthquakes and the propagation of elastic waves through the Earth or through other planet-like bodies History of soil science – history of the study of soil as a natural resource on the surface of the earth including soil formation, classification and mapping; physical, chemical, biological, and fertility properties of soils; and these properties in relation to the use and management of soils. History of topography – history of the study of surface shape and features of the Earth and other observable astronomical objects including planets, moons, and asteroids. History of volcanology – history of the study of volcanoes, lava, magma, and related geological, geophysical and geochemical phenomena. History of biology –


id: outline-of-natural-science-20260505-10
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-10.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
History of mycology – history of the study of fungi History of oceanography – history of the study of the ocean, including ocean life, environment, geography, weather, and other aspects influencing the ocean History of oncology – history of the study of cancer processes, including virus or mutation oncogenesis, angiogenesis and tissues remoldings History of population biology – history of the study of groups of conspecific organisms, including History of population ecology – history of the study of how population dynamics and extinction History of population genetics – history of the study of changes in gene frequencies in populations of organisms Pathobiology or pathology – history of the study of diseases, and the causes, processes, nature, and development of disease History of parasitology – history of the study of parasites and parasitism History of pharmacology – history of the study and practical application of preparation, use, and effects of drugs and synthetic medicines History of physiology – history of the study of the functioning of living organisms and the organs and parts of living organisms History of immunology – History of kinesiology – history of the Kinesiology, also known as human kinetics, is the scientific study of human movement History


id: outline-of-natural-science-20260505-11
file: data/en.wikipedia.org/wiki/Outline_of_natural_science-11.md
category: reference
title: Outline of natural science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_natural_science
date_saved: 2026-05-05T06:38:32.410581+00:00
instance: kb-cron
parent: outline-of-natural-science-20260505-0
summary:
== General natural science concepts == Discovery science Empirical evidence Experiment Hypothesis Laboratory Nature Occam's razor Peer review Physical law Reproducibility Scientific evidence Scientific method Scientific theory Testability Universe Validity == Natural science organizations == == Natural science publications == Physical science journals List of astronomy journals List of chemistry journals List of earth and atmospheric sciences journals List of physics journals List of biology journals List of botany journals List of zoology journals == Persons influential in natural science == List of physicists List of biophysicists List of geophysicists List of astronomers List of cosmologists List of chemists List of biochemists List of neurochemists List of electrochemists List of earth scientists List of Russian earth scientists List of cartographers List of climatologists List of geographers List of geologists List of geophysicists List of glaciologists List of mineralogists List of meteorologists List of paleontologists List of soil scientists List of biologists List of botanists by author abbreviation List of carcinologists List of coleopterists List of ecologists List of malacologists List of mammalogists List of mycologists List of ornithologists List of pathologists List of zoologists by author abbreviation List of Nobel Prize winners in physiology or medicine == See also ==


id: anthropocene-working-group-20260505-0
file: data/en.wikipedia.org/wiki/Anthropocene_Working_Group-0.md
category: reference
title: Anthropocene Working Group
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Anthropocene_Working_Group
date_saved: 2026-05-05T06:38:33.636039+00:00
instance: kb-cron
parent: anthropocene-working-group-20260505-0
summary:
The Anthropocene Working Group (AWG) is an interdisciplinary research group dedicated to the study of the Anthropocene as a geological time unit. It was established in 2009 as part of the Subcommission on Quaternary Stratigraphy (SQS), a constituent body of the International Commission on Stratigraphy (ICS). As of 2021, the research group features 37 members, with the physical geographer Simon Turner as Secretary and the geologist Colin Neil Waters as chair of the group. The late Nobel Prize-winning Paul Crutzen, who popularized the word 'Anthropocene' in 2000, had also been a member of the group until he died on January 28, 2021. The main goal of the AWG is providing scientific evidence robust enough for the Anthropocene to be formally ratified by the International Union of Geological Sciences (IUGS) as an epoch within the Geologic time scale. == History == Prior to the establishment of the Anthropocene Working Group in 2009, no research program dedicated to the formalization of the Anthropocene in the geologic time scale existed. The idea of naming the current epoch 'Anthropocene' rather than using its formal time unit, the Holocene, became popular after Paul Crutzen and Eugene Stoermer published in May 2000 an article on the


id: anthropocene-working-group-20260505-1
file: data/en.wikipedia.org/wiki/Anthropocene_Working_Group-1.md
category: reference
title: Anthropocene Working Group
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Anthropocene_Working_Group
date_saved: 2026-05-05T06:38:33.636039+00:00
instance: kb-cron
parent: anthropocene-working-group-20260505-0
summary:
== Research == The Anthropocene Working Group is one of four workings groups part of the Subcommission on Quaternary Stratigraphy (the other three being the Pleistocene–Holocene boundary working group, Middle/Late Pleistocene boundary working group, and Early/Middle Pleistocene boundary working group). The AWG members (including Paul Crutzen, who was awarded the Nobel prize for chemistry in 1995 for his researcher on ozone depletion; John McNeill, a pioneering researcher in the field of environmental history; and Naomi Oreskes, author of the book Merchants of Doubt) have diverse disciplinary backgrounds, ranging from international law, archaeology, and history to philosophy, natural science, and geography. Since no direct funding supports the research program, communication among members happens mostly through email, whereas meetings are usually founded by hosting institutions. As for most of the epochs in the Phanerozoic (the current Eon, starting 539 million years ago), determining the beginning of the Anthropocene by locating and agreeing upon its lower boundary is a necessary step in its process of formal recognition as a geochronological/chronostratigraphic unit. A lower boundary is defined by locating a GSSP (informally known as 'golden spike') in the stratigraphic section of a stage, the chronostratigraphic taxonomic equivalent of an epoch. Alternatively, if a 'golden


id: faculty-of-natural-sciences-imperial-college-london-20260505-0
file: data/en.wikipedia.org/wiki/Faculty_of_Natural_Sciences,_Imperial_College_London-0.md
category: reference
title: Faculty of Natural Sciences, Imperial College London
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Faculty_of_Natural_Sciences,_Imperial_College_London
date_saved: 2026-05-05T06:38:34.862895+00:00
instance: kb-cron
parent: faculty-of-natural-sciences-imperial-college-london-20260505-0
summary:
The Faculty of Natural Sciences is one of four faculties of Imperial College London in London, England. It was formed in 2001 from the former Royal College of Science, a constituent college of Imperial College which dated back to 1848, and the faculty largely consists of the original departments of the college. Undergraduate teaching occurs for all departments at the South Kensington campus, with research being split between South Kensington and the new innovation campus at White City. Students who study at the departments of the Faculty are represented by the Royal College of Science Union, a constituent union of the college union which caters specifically to students on natural science courses. Graduates who obtain an undergraduate degree, either BSc or MSci, from the faculty are awarded the Associateship of the Royal College of Science (ARCS) as an additional degree. == History == The origins of the faculty lie in the Royal College of Chemistry, which, after being founded in 1845, moved to a new site in South Kensington in the early 1870s. Incorporated into the Normal School of Science, the college was later renamed the Royal College of Science in 1890, and in 1907 became a constituent college of


id: natural-sciences-collections-association-20260505-0
file: data/en.wikipedia.org/wiki/Natural_Sciences_Collections_Association-0.md
category: reference
title: Natural Sciences Collections Association
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_Sciences_Collections_Association
date_saved: 2026-05-05T06:38:36.261825+00:00
instance: kb-cron
parent: natural-sciences-collections-association-20260505-0
summary:
The Natural Sciences Collections Association (NatSCA) is a United Kingdom based membership organisation and charitable organization (No.1098156) run by volunteers from the membership. NatSCA's mission is "to promote and support natural science collections, the institutions that house them and the people that work with them, in order to improve collections care, understanding, accessibility and enjoyment for all." The subject specialist network promotes research and exchange of ideas, advances in technical and ethical standards, and raises the public profile of the conservation and preservation of natural science collections and objects. The organisation describes their focus as: Community – developing an open, friendly and accessible network for sharing information, experience and skills. Support – facilitating the professional development of stakeholders in natural science collections. Standards – identifying and promoting good quality practice in the care and use of natural science collections. Promoting collections – increasing awareness of the scientific and cultural value of natural science collections. Advocacy – challenging neglect of collections and lobbying for the appropriate resourcing of collections for their care and sustainable use. == History == The Natural Sciences Collections Association was founded in April 2003 from the merger of Natural Sciences Conservation Group (NCSG) and Biological Curator's Group


id: nature-journal-20260505-0
file: data/en.wikipedia.org/wiki/Nature_(journal)-0.md
category: reference
title: Nature (journal)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nature_(journal)
date_saved: 2026-05-05T06:38:37.517402+00:00
instance: kb-cron
parent: nature-journal-20260505-0
summary:
Nature is a British weekly international scientific journal publishing peer-reviewed research across the natural sciences, including biology, physics, chemistry, the earth sciences, and related interdisciplinary fields. It operates editorial offices in London, the United States, continental Europe, and Asia under the international scientific publishing company Springer Nature. According to the 2022 Journal Citation Reports, Nature had one of the highest impact factors among multidisciplinary science journals (50.5), reflecting its strong citation influence within the scientific literature; some commentators also regard it as among the most influential scientific journals worldwide. In 2007, Nature (together with Science) received the Prince of Asturias Award for Communications and Humanity. As of 2012, it claimed an online readership of about three million unique readers per month. Founded in the autumn of 1869, Nature was first circulated by Norman Lockyer and Alexander MacMillan as a public forum for scientific innovations. The mid-20th century facilitated an editorial expansion for the journal; Nature redoubled its efforts in explanatory and scientific journalism. The late 1980s and early 1990s saw the creation of a network of editorial offices outside of Britain and the establishment of ten new supplementary, specialty publications (e.g. Nature Materials). Since the late 2000s, dedicated editorial and


id: nature-journal-20260505-1
file: data/en.wikipedia.org/wiki/Nature_(journal)-1.md
category: reference
title: Nature (journal)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nature_(journal)
date_saved: 2026-05-05T06:38:37.517402+00:00
instance: kb-cron
parent: nature-journal-20260505-0
summary:
Not long after the conclusion of The Reader, a former editor, Norman Lockyer, decided to create a new scientific journal titled Nature, taking its name from a line by William Wordsworth: "To the solid ground of nature trusts the Mind that builds for aye". First owned and published by Alexander Macmillan, Nature was similar to its predecessors in its attempt to "provide cultivated readers with an accessible forum for reading about advances in scientific knowledge." Janet Browne has proposed that "far more than any other science journal of the period, Nature was conceived, born, and raised to serve polemic purpose." Many of the early editions of Nature consisted of articles written by members of a group that called itself the X Club, a group of scientists known for having liberal, progressive, and somewhat controversial scientific beliefs for their time. Initiated by Thomas Henry Huxley, the group consisted of such important scientists as Joseph Dalton Hooker, Herbert Spencer, and John Tyndall, along with another five scientists and mathematicians; these scientists were all avid supporters of Darwin's theory of evolution as common descent, a theory which, during the latter half of the 19th century, received a great deal of criticism among more


id: nature-journal-20260505-2
file: data/en.wikipedia.org/wiki/Nature_(journal)-2.md
category: reference
title: Nature (journal)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nature_(journal)
date_saved: 2026-05-05T06:38:37.517402+00:00
instance: kb-cron
parent: nature-journal-20260505-0
summary:
According to Science, another academic journal, being published in Nature has been known to carry a certain level of prestige in academia. In particular, empirical papers are often highly cited, which can lead to promotions, grant funding, and attention from the mainstream media. Because of these positive feedback effects, competition among scientists to publish in high-level journals like Nature and its closest competitor, Science, can be very fierce. Nature's impact factor, a measure of how many citations a journal generates in other works, was 42.778 in 2019 (as measured by Thomson ISI). However, as with many journals, most papers receive far fewer citations than the impact factor would indicate. Nature's journal impact factor carries a long tail. Studies of methodological quality and reliability have found that some high-prestige journals, including Nature, "publish significantly substandard structures", and overall "reliability of published research works in several fields may be decreasing with increasing journal rank". As with most other professional scientific journals, papers undergo an initial screening by the editor, followed by peer review. In this process, the editor selects several other scientists to read and critique articles, based on their expertise with the subject matter but who have no connection to the


id: nature-journal-20260505-3
file: data/en.wikipedia.org/wiki/Nature_(journal)-3.md
category: reference
title: Nature (journal)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nature_(journal)
date_saved: 2026-05-05T06:38:37.517402+00:00
instance: kb-cron
parent: nature-journal-20260505-0
summary:
In a 2003 editorial, "Coping with Peer Rejection", Nature acknowledged several other rejection missteps:[T]here are unarguable faux pas in our history. These include the rejection of Cherenkov radiation, Hideki Yukawa's meson, work on photosynthesis by Johann Deisenhofer, Robert Huber and Hartmut Michel, and the initial rejection (but eventual acceptance) of Stephen Hawking's black-hole radiation. The journal has also faced scrutiny for not following editorial procedure. For example, before publishing Watson and Crick's 1953 paper on the structure of DNA, Nature did not send the paper to peer review. John Maddox, Nature's editor, stated: "The Watson and Crick paper was not peer-reviewed by Nature ... the paper could not have been refereed: its correctness is self-evident. No referee working in the field ... could have kept his mouth shut once he saw the structure." In June 1988, Nature published a controversial and seemingly anomalous paper detailing Jacques Benveniste and his team's work studying water memory. The paper concluded that less than a single molecule of antibody diluted in water could trigger an immune response in human basophils, defying the physical law of mass action. The paper gained substantial media attention in Paris, France, chiefly because their research sought funding from homeopathic


id: prodromus-20260505-0
file: data/en.wikipedia.org/wiki/Prodromus-0.md
category: reference
title: Prodromus
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prodromus
date_saved: 2026-05-05T06:38:38.830660+00:00
instance: kb-cron
parent: prodromus-20260505-0
summary:
A prodromus ('forerunner' or 'precursor') aka prodrome is a term used in the natural sciences to describe a preliminary publication intended as the basis for a later, more comprehensive work. It is also a medical term used for a premonitory symptom, that is, a symptom indicating the onset of a disease. The origin of the word is from the 19th century: via French from New Latin prodromus, from Greek prodromos, meaning forerunner. Nicolas Steno's De solido intra solidum naturaliter contento dissertationis prodromus, one of the early treatises attempting to explain the occurrence of fossils in solid rock. Ludovico Marracci's Arabic edition and Latin translation of the Qur’an was published in 1698. His ‘Introduction’ (Prodromus) had been published seven years earlier. Other notable prodromi include Prodromus Entomology, Prodromus Florae Novae Hollandiae et Insulae Van Diemen, Prodromus Systematis Naturalis Regni Vegetabilis. == See also == White paper == References ==


id: rangeland-management-20260505-0
file: data/en.wikipedia.org/wiki/Rangeland_management-0.md
category: reference
title: Rangeland management
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rangeland_management
date_saved: 2026-05-05T06:38:40.160840+00:00
instance: kb-cron
parent: rangeland-management-20260505-0
summary:
Rangeland management (also range management, range science, or arid-land management) is a natural science that centers around the study of rangelands and the "conservation and sustainable management [of Arid-Lands] for the benefit of current societies and future generations". Range management is defined by Holechek et al. as the "manipulation of rangeland components to obtain optimum combination of goods and services for society on a sustained basis". The United Nations (UN) has declared 2026 the International Year of Rangelands and Pastoralists, with the Food and Agriculture Organization leading the initiative. == History == The earliest form of Rangeland Management is not formally deemed part of the natural science studied today, although its roots can be traced to nomadic grazing practices of the Neolithic Revolution when humans domesticated plants and animals under pressures from population growth and environmental change. Humans might even have altered the environment in times preceding the Neolithic through hunting of large-game, whereby large losses of grazing herbivores could have resulted in altered ecological states; meaning humans have been inadvertently managing land throughout prehistory. Rangeland management was developed in the United States in response to rangeland deterioration and in some cases, denudation, due to overgrazing and other misuse of


id: uprrp-college-of-natural-sciences-20260505-0
file: data/en.wikipedia.org/wiki/UPRRP_College_of_Natural_Sciences-0.md
category: reference
title: UPRRP College of Natural Sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/UPRRP_College_of_Natural_Sciences
date_saved: 2026-05-05T06:38:41.484479+00:00
instance: kb-cron
parent: uprrp-college-of-natural-sciences-20260505-0
summary:
The College of Natural Sciences of the University of Puerto Rico, Río Piedras (Spanish: Universidad de Puerto Rico, Recinto de Río Piedras), was established in 1943. It is one of several colleges that make up the University of Puerto Rico, Rio Piedras campus. Facundo Bueso Sanllehí was its first dean. It is located in the city of San Juan, the capital of Puerto Rico. == Centers == Center for Applied Tropical Ecology and Conservation (CREST-CATEC) Center for Nanoscale Materials == Facilities == Animal House Botanical Garden Center for Educational Production Services Computing Center Department of Chemistry Support Facilities Electronics Workshop Field House at El Yunque (El Verde) Greenhouse Herbarium Instrumentation Workshop Machining Workshop Natural Sciences Library Zoology Museum == References == == External links == Official website


id: social-science-20260505-0
file: data/en.wikipedia.org/wiki/Social_science-0.md
category: reference
title: Social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_science
date_saved: 2026-05-05T06:38:43.218913+00:00
instance: kb-cron
parent: social-science-20260505-0
summary:
Social science (or the social sciences) is one of the branches of science, devoted to the study of societies and the relationships among members within those societies. The term was formerly used to refer to the field of sociology, the original "science of society", established in the 18th century. It now encompasses a wide array of additional academic disciplines, including anthropology, archaeology, economics, geography, history, linguistics, management, communication studies, psychology, sociology, culturology, and political science. The majority of positivist social scientists use methods resembling those used in the natural sciences as tools for understanding societies, and so define science in its stricter modern sense. Speculative social scientists, otherwise known as interpretivist scientists, by contrast, may use social critique or symbolic interpretation rather than constructing empirically falsifiable theories, and thus treat science in its broader sense. In modern academic practice, researchers are often eclectic, using multiple methodologies (combining both quantitative and qualitative research). To gain a deeper understanding of complex human behavior in digital environments, social science disciplines have increasingly integrated interdisciplinary approaches, big data, and computational tools. The term social research has also acquired a degree of autonomy as practitioners from various disciplines share similar goals and methods. == History


id: social-science-20260505-1
file: data/en.wikipedia.org/wiki/Social_science-1.md
category: reference
title: Social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_science
date_saved: 2026-05-05T06:38:43.218913+00:00
instance: kb-cron
parent: social-science-20260505-0
summary:
Social science and humanities have a mutual contempt for one another, the former looking down on the latter as unscientific, the latter regarding the former as philistine. [...] The difference comes down to the fact that social science really wants to be predictive, meaning that man is predictable, while the humanities say that he is not. == Branches == The social science disciplines are branches of knowledge taught and researched at the college or university level. Social science disciplines are defined and recognized by the academic journals in which research is published, and the learned social science societies and academic departments or faculties to which their practitioners belong. Social science fields of study usually have several sub-disciplines or branches, and the distinguishing lines between these are often both arbitrary and ambiguous. The following are widely-considered to be social sciences: === Anthropology === Anthropology is the holistic "science of man", a science of the totality of human existence. The discipline deals with the integration of different aspects of the social sciences, humanities, and human biology. In the twentieth century, academic disciplines have often been institutionally divided into three broad domains. Firstly, the natural sciences seek to derive general laws through reproducible


id: social-science-20260505-2
file: data/en.wikipedia.org/wiki/Social_science-2.md
category: reference
title: Social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_science
date_saved: 2026-05-05T06:38:43.218913+00:00
instance: kb-cron
parent: social-science-20260505-0
summary:
Economics has two broad branches: microeconomics, where the unit of analysis is the individual agent, such as a household or firm, and macroeconomics, where the unit of analysis is an economy as a whole. Another division of the subject distinguishes positive economics, which seeks to predict and explain economic phenomena, from normative economics, which orders choices and actions by some criterion; such orderings necessarily involve subjective value judgments. Since the early part of the 20th century, economics has focused largely on measurable quantities, employing both theoretical models and empirical analysis. Quantitative models, however, can be traced as far back as the physiocratic school. Economic reasoning has been increasingly applied in recent decades to other social situations such as politics, law, psychology, history, religion, marriage and family life, and other social interactions. The expanding domain of economics in the social sciences has been described as economic imperialism. === Education === Education encompasses teaching and learning specific skills, and also something less tangible but more profound: the imparting of knowledge, positive judgement and well-developed wisdom. Education has as one of its fundamental aspects the imparting of culture from generation to generation (see socialization). To educate means 'to draw out', from the Latin


id: social-science-20260505-3
file: data/en.wikipedia.org/wiki/Social_science-3.md
category: reference
title: Social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_science
date_saved: 2026-05-05T06:38:43.218913+00:00
instance: kb-cron
parent: social-science-20260505-0
summary:
Linguistics investigates the cognitive and social aspects of human language. The field is divided into areas that focus on aspects of the linguistic signal, such as syntax (the study of the rules that govern the structure of sentences), semantics (the study of meaning), morphology (the study of the structure of words), phonetics (the study of speech sounds) and phonology (the study of the abstract sound system of a particular language); however, work in areas like evolutionary linguistics (the study of the origins and evolution of language) and psycholinguistics (the study of psychological factors in human language) cut across these divisions. The overwhelming majority of modern research in linguistics takes a predominantly synchronic perspective (focusing on language at a particular point in time), and a great deal of it—partly owing to the influence of Noam Chomsky—aims at formulating theories of the cognitive processing of language. However, language does not exist in a vacuum, or only in the brain, and approaches like contact linguistics, creole studies, discourse analysis, social interactional linguistics, and sociolinguistics explore language in its social context. Sociolinguistics often makes use of traditional quantitative analysis and statistics in investigating the frequency of features, while some disciplines, like contact linguistics, focus


id: social-science-20260505-4
file: data/en.wikipedia.org/wiki/Social_science-4.md
category: reference
title: Social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_science
date_saved: 2026-05-05T06:38:43.218913+00:00
instance: kb-cron
parent: social-science-20260505-0
summary:
Sociology is the systematic study of society, individuals' relationship to their societies, the consequences of difference, and other aspects of human social action. The meaning of the word comes from the suffix -logy, which means "study of", derived from Ancient Greek, and the stem soci-, which is from the Latin word socius, meaning "companion", or society in general. Auguste Comte (1798–1857) coined the term sociology to describe a way to apply natural science principles and techniques to the social world in 1838. Comte endeavoured to unify history, psychology and economics through the descriptive understanding of the social realm. He proposed that social ills could be remedied through sociological positivism, an epistemological approach outlined in The Course in Positive Philosophy [1830–1842] and A General View of Positivism (1844). Though Comte is generally regarded as the "Father of Sociology", the discipline was formally established by another French thinker, Émile Durkheim (1858–1917), who developed positivism as a foundation to practical social research. Durkheim set up the first European department of sociology at the University of Bordeaux in 1895, publishing his Rules of the Sociological Method. In 1896, he established the journal L'Année sociologique. Durkheim's seminal monograph, Suicide (1897), a case study of suicide


id: social-science-20260505-5
file: data/en.wikipedia.org/wiki/Social_science-5.md
category: reference
title: Social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_science
date_saved: 2026-05-05T06:38:43.218913+00:00
instance: kb-cron
parent: social-science-20260505-0
summary:
Archaeology, a science that is focused on the study of human cultures by means of the recovery, documentation, analysis, and interpretation of material remains and environmental data, including architecture, artifacts, features, and landscapes. Area studies, interdisciplinary fields of research and scholarship pertaining to particular geographical, national/federal, or cultural regions. Behavioural science, which encompasses disciplines that explore the activities of and interactions among organisms in the natural world. Computational social science, an umbrella field encompassing computational approaches within the social sciences. Demography, the statistical study of human populations. Development studies, a branch of social science that addresses issues of concern to developing countries. Environmental social science, the broad study of interrelations between humans and the natural environment. Environmental studies, which integrates social, humanistic, and natural science perspectives on the relation between humans and the natural environment. Gender studies, which is focused on the study of gender identity, masculinity, femininity, transgender issues, and sexuality. Information science, an interdisciplinary science primarily concerned with the collection, classification, manipulation, storage, retrieval and dissemination of information. International studies, which covers both international relations (the study of foreign affairs and global issues among states within the international system) and international education. Legal management, a social sciences discipline


id: social-science-20260505-6
file: data/en.wikipedia.org/wiki/Social_science-6.md
category: reference
title: Social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_science
date_saved: 2026-05-05T06:38:43.218913+00:00
instance: kb-cron
parent: social-science-20260505-0
summary:
Quantitative designs approach social phenomena through quantifiable evidence, and often rely on statistical analysis of many cases (or across intentionally designed treatments in an experiment) to create valid and reliable general claims. Qualitative designs emphasize understanding of social phenomena through direct observation, communication with participants, or analysis of texts, and may stress contextual and subjective accuracy over generality. Social scientists will commonly combine quantitative and qualitative approaches as part of a multi-strategy design. Questionnaires, field-based data collection, archival database information and laboratory-based data collections are some of the measurement techniques used. It is noted the importance of measurement and analysis, focusing on the (difficult to achieve) goal of objective research or statistical hypothesis testing. A mathematical model uses mathematical language to describe a system. The process of developing a mathematical model is termed 'mathematical modelling' (also modeling). A mathematical model is "a representation of the essential aspects of an existing system (or a system to be constructed) that presents knowledge of that system in usable form". Mathematical models can take many forms, including but not limited to dynamical systems, statistical models, differential equations, or game theoretic models. These and other types of models can overlap, with a given model involving


id: social-science-20260505-7
file: data/en.wikipedia.org/wiki/Social_science-7.md
category: reference
title: Social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_science
date_saved: 2026-05-05T06:38:43.218913+00:00
instance: kb-cron
parent: social-science-20260505-0
summary:
== Bibliography == Michie, Jonathan, ed. Reader's Guide to the Social Sciences (2 vol. 2001) 1970 pages annotating the major topics in the late 20th century in all the social sciences. === 20th and 21st centuries sources === Neil J. Smelser and Paul B. Baltes (2001). International Encyclopedia of the Social & Behavioral Sciences, Amsterdam: Elsevier. Byrne, D.S. (1998). Complexity theory and the social sciences: an introduction. Routledge. ISBN 978-0-415-16296-8 Kuper, A., and Kuper, J. (1985). The Social Science Encyclopedia. London: Routledge & Kegan Paul. (ed., a limited preview of the 1996 version is available) Lave, C.A., and March, J.G. (1993). An introduction to models in the social sciences. Lanham, Md: University Press of America. Perry, John and Erna Perry. Contemporary Society: An Introduction to Social Science (12th Edition, 2008), college textbook Potter, D. (1988). Society and the social sciences: An introduction. London: Routledge [u.a.]. David L. Sills and Robert K. Merton (1968). International Encyclopedia of the Social Sciences. Seligman, Edwin R.A. and Alvin Johnson (1934). Encyclopedia of the Social Sciences. (13 vol.) Ward, L.F. (1924). Dynamic sociology, or applied social science: As based upon statical sociology and the less complex sciences. New York: D. Appleton. Leavitt, F.M., and Brown,


id: outline-of-social-science-20260505-0
file: data/en.wikipedia.org/wiki/Outline_of_social_science-0.md
category: reference
title: Outline of social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_social_science
date_saved: 2026-05-05T06:38:44.430858+00:00
instance: kb-cron
parent: outline-of-social-science-20260505-0
summary:
The following outline is provided as an overview of and topical guide to social science: Social science – main branch of science comprising scientific fields concerned with societies, human behaviour, and social relationships. == Definition == Social science can be described as all of the following: A science – systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe. Major category of academic disciplines – an academic discipline is focused study in one academic field or profession. A discipline incorporates expertise, people, projects, communities, challenges, studies, inquiry, and research areas that are strongly associated with academic areas of study or areas of professional practice. For example, the branches of science are commonly referred to as the scientific disciplines. For instance, gravitation is strongly associated with the discipline of physics, and is considered to be part of that disciplinary knowledge.


id: outline-of-social-science-20260505-1
file: data/en.wikipedia.org/wiki/Outline_of_social_science-1.md
category: reference
title: Outline of social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_social_science
date_saved: 2026-05-05T06:38:44.430858+00:00
instance: kb-cron
parent: outline-of-social-science-20260505-0
summary:
== Branches of social science == Anthropology - the study of humans, past and present, that draws and builds upon knowledge from the social sciences and biological sciences, as well as the humanities and the natural sciences. Anthropology of religion – the study of religious institutions about other social institutions, and the comparison of religious beliefs and practices across cultures Applied anthropology – application of the method and theory of anthropology to the analysis and solution of practical problems. Archaeology – the study of cultures via material remains and environmental data (Outline of archaeology) Experimental archaeology – Experimental archaeology employs several different methods, techniques, analyses, and approaches to generate and test hypotheses, based upon the archaeological source material, like ancient structures or artifacts. Historical archaeology – a form of archaeology dealing with topics that are already attested in written records. Zooarchaeology – study of faunal remains. Cultural anthropology – a branch of anthropology focused on the study of cultural variation among humans, collecting data about the effect of global economic and political processes on local cultural realities. Ethnobiology – the scientific study of dynamic relationships between peoples, biota, and environments, from the distant past to the immediate present. Ethnobotany –


id: outline-of-social-science-20260505-2
file: data/en.wikipedia.org/wiki/Outline_of_social_science-2.md
category: reference
title: Outline of social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_social_science
date_saved: 2026-05-05T06:38:44.430858+00:00
instance: kb-cron
parent: outline-of-social-science-20260505-0
summary:
Cultural studies – academic field grounded in critical theory and literary criticism. Development studies – a multidisciplinary branch of social science that addresses issues of concern to developing countries. Education – in the general sense is any act or experience that has a formative effect on the mind, character, or physical ability of an individual. In its technical sense, education is the process by which society deliberately transmits its accumulated knowledge, skills, and values from one generation to another. Environmental studies – the interdisciplinary academic field which systematically studies human interaction with the environment. Gender and sexuality studies – field of interdisciplinary study and academic field devoted to gender identity and gendered representation as central categories of analysis. Geography – the study of the lands, features, inhabitants, and phenomena of Earth. Physical geography – a branch of the science that studies physical features on the Earth's surface, water, the atmosphere and biodiversity on the Earth (is also listed in Earth Science). Geomorphology – branch of physical geography that studies the Earth and its landforms. Hydrology – study of water on the Earth's surface and in its atmosphere. Glaciology – study of ice sheets and glaciers on the Earth's surface. Biogeography


id: outline-of-social-science-20260505-3
file: data/en.wikipedia.org/wiki/Outline_of_social_science-3.md
category: reference
title: Outline of social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_social_science
date_saved: 2026-05-05T06:38:44.430858+00:00
instance: kb-cron
parent: outline-of-social-science-20260505-0
summary:
Etymology – the study of the history of words, their origins, and how their form and meaning have changed over time. Evolutionary linguistics – the scientific study of both the origins and development of language as well as the cultural evolution of languages. Forensic linguistics – application of linguistic knowledge, methods and insights to the forensic context of law, language, crime investigation, trial, and judicial procedure. Geolinguistics – branch of human geography that studies the geographic distribution of language or its constituent elements. Historical linguistics – the study of language change. Language – is a structured means of communication Lexis – total vocabulary or lexicon having items of lexical, rather than grammatical, meaning. Linguistic typology – subfield of linguistics that studies and classifies languages according to their structural features. Literature – refers to writing considered to be an art form or any single writing deemed to have artistic or intellectual value, often due to deploying language in ways that differ from ordinary usage. Mathematical linguistics – Mathematics has been applied in linguistics for analysis, grammar logic and other theoretical aspects in linguistics. Morphology – identification, analysis and description of the structure of a given language's morphemes and other linguistic units,


id: outline-of-social-science-20260505-4
file: data/en.wikipedia.org/wiki/Outline_of_social_science-4.md
category: reference
title: Outline of social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_social_science
date_saved: 2026-05-05T06:38:44.430858+00:00
instance: kb-cron
parent: outline-of-social-science-20260505-0
summary:
Educational psychology – the study of how humans learn in educational settings, the effectiveness of educational interventions, the psychology of teaching, and the social psychology of schools as organizations. Forensic psychology – the intersection between psychology and the courtroom—criminal, civil, family and Federal. Health psychology – concerned with understanding how biological, psychological, environmental, and cultural factors are involved in physical health and illness. Industrial and organizational psychology – the scientific study of employees, workplaces, and organizations. Legal psychology – involves empirical, psychological research of the law, legal institutions, and people who come into contact with the law. Media psychology – seeks an understanding of how people perceive, interpret, use, and respond to a media-rich world. Military psychology – research, design and application of psychological theories and experimentation data towards understanding, predicting and countering behaviours either in friendly or enemy forces or civilian population that may be undesirable, threatening or potentially dangerous to the conduct of military operations. Occupational health psychology – concerned with the psychosocial characteristics of workplaces that contribute to the development of health-related problems in people who work. Political psychology – an interdisciplinary academic field dedicated to understanding political science, politicians and political behaviour through the use of


id: outline-of-social-science-20260505-5
file: data/en.wikipedia.org/wiki/Outline_of_social_science-5.md
category: reference
title: Outline of social science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_social_science
date_saved: 2026-05-05T06:38:44.430858+00:00
instance: kb-cron
parent: outline-of-social-science-20260505-0
summary:
== History of social science == History of the social sciences History of anthropology History of archaeology History of area studies History of communication studies History of cultural studies History of development studies History of economics History of education History of environmental studies History of gender studies History of geography History of human geography History of history History of information science History of journalism History of law History of library science History of linguistics History of management History of political science History of international studies History of international relations History of political economy History of public administration History of psychology History of social psychology History of social work History of sociology History of criminal justice History of demography History of sustainability == Education and degrees == Bachelor of Social Science Bachelor of Science Bachelor of Arts Bachelor of Economics == General social science concepts == Ethical research in social science Open and closed systems in social science == Social science organizations == == Social science publications == List of social science journals == Social scientists == List of anthropologists List of business theorists List of economists List of socialist economists List of geographers List of political scientists List of psychologists


id: administrative-history-20260505-0
file: data/en.wikipedia.org/wiki/Administrative_history-0.md
category: reference
title: Administrative history
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Administrative_history
date_saved: 2026-05-05T06:38:45.629391+00:00
instance: kb-cron
parent: administrative-history-20260505-0
summary:
Administrative history is a historiographic field which looks at the history of state administrations and bureaucracies. Originally considered a sub-field of Administrative Sciences that was intended to improve contemporary governance, administrative history has become an increasingly separate field. Administrative historians study the changes in administrative ideologies and administrative law while also looking at civil servants and the relationship between government and society. It is related to political and constitutional history. The discipline is most common in France, Germany, the United Kingdom, and Italy. In 1965, when fields like social history were becoming ever more popular, G. R. Elton (then a fellow of Clare College, Cambridge) defended administrative history as the only field which can explain how the machinery of government actually worked in the past. == Journals == Academic journals which specialise in administrative history include: Jahrbuch für europäische Verwaltungsgeschichte (JEV) == The New Administrative History == Historians researching the medieval and early modern periods have begun to reexplore the possibilities of administrative history. This movement has been described as the "New Administrative History". It embraces a broad range of approaches, including interdisciplinary and theoretical work – as exemplified by John Sabapathy – and also more traditional institutional approaches, revisiting


id: animal-studies-20260505-0
file: data/en.wikipedia.org/wiki/Animal_studies-0.md
category: reference
title: Animal studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Animal_studies
date_saved: 2026-05-05T06:38:46.795450+00:00
instance: kb-cron
parent: animal-studies-20260505-0
summary:
Animal studies is a recently recognised field in which animals are studied in a variety of cross-disciplinary ways. Scholars who engage in animal studies may be formally trained in a number of diverse fields, including art history, anthropology, biology, film studies, geography, history, psychology, literary studies, museology, philosophy, communication, and sociology. They engage with questions about notions of "animality," "animalization," or "becoming animal," to understand human-made representations of and cultural ideas about "the animal" and what it is to be human by employing various theoretical perspectives. Using these perspectives, those who engage in animal studies seek to understand both human-animal relations now and in the past as defined by present knowledge. Because the field is still developing, scholars and others have some freedom to define their own criteria about what issues may structure the field. == History == Animal studies became popular in the 1970s as an interdisciplinary subject, animal studies exists at the intersection of a number of different fields of study such as journals and books series, etc. Different fields began to turn to animals as an important topic at different times and for various reasons, and these separate disciplinary histories shape how scholars approach animal studies. Historically,


id: animal-studies-20260505-1
file: data/en.wikipedia.org/wiki/Animal_studies-1.md
category: reference
title: Animal studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Animal_studies
date_saved: 2026-05-05T06:38:46.795450+00:00
instance: kb-cron
parent: animal-studies-20260505-0
summary:
== Research topics and methodologies == Researchers in animal studies examine the questions and issues that arise when traditional modes of humanistic and scientific inquiry begin to take animals seriously as subjects of thought and activity. Students of animal studies may examine how humanity is defined in relation to animals, or how representations of animals create understandings (and misunderstandings) of other species. In fact, animals often elicit fear in humans. A well-known animal phobia is ophidiophobia, the fear of snakes. People with animal phobias tend to negatively generalize animals, even species that are harmless. In most movies, predatory animals such as sharks and wolves are usually the antagonists, but this only causes significant damage to their reputation and makes people fear what they think their true nature is. In order to do so, animal studies pays close attention to the ways that humans anthropomorphize animals, and asks how humans might avoid bias in observing other creatures. Anthropomorphized animals are frequently found in children's books and films. Researchers are analyzing the positive and negative effects of anthropomorphized animals on a child's view of the non-human species. In addition, Donna Haraway's book, Primate Visions, examines how dioramas created for the American Museum


id: the-arts-and-politics-20260505-0
file: data/en.wikipedia.org/wiki/The_arts_and_politics-0.md
category: reference
title: The arts and politics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/The_arts_and_politics
date_saved: 2026-05-05T06:38:48.028186+00:00
instance: kb-cron
parent: the-arts-and-politics-20260505-0
summary:
A strong relationship between the arts and politics, particularly between various kinds of art and power, occurs across historical epochs and cultures. As they respond to contemporaneous events and politics, the arts take on political as well as social dimensions, becoming themselves a focus of controversy and even a force of political as well as social change. A widespread observation is that a great talent has a free spirit. For instance Pushkin, who some scholars regard as Russia's first great writer, is said to have attracted the irritation of the Russian officialdom and particularly of the Tsar, since he "instead of being a good servant of the state in the rank and file of the administration and extolling conventional virtues in his vocational writings (if write he must), composed extremely arrogant and extremely independent and extremely wicked verse in which a dangerous freedom of thought was evident in the novelty of his versification, in the audacity of his sensual fancy, and in his propensity for making fun of major and minor tyrants." == History of art == According to Groys, "Art has its own power in the world, and is as much a force in the power play of global


id: the-arts-and-politics-20260505-1
file: data/en.wikipedia.org/wiki/The_arts_and_politics-1.md
category: reference
title: The arts and politics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/The_arts_and_politics
date_saved: 2026-05-05T06:38:48.028186+00:00
instance: kb-cron
parent: the-arts-and-politics-20260505-0
summary:
=== Classical music === Ludwig van Beethoven did not use the original title "Ode to Freedom" of Friedrich Schiller's lyric, known in English as "Ode to Joy" (1785), in setting it to music in the final movement of his Ninth Symphony (1824), which "Napoleonic censors had forced the poet to change to 'Ode to Joy'." After the fall of the Berlin Wall, on 9 November 1989, that Christmas Day, when Leonard Bernstein conducted a performance of Beethoven's Ninth at the site of the former East German–West German border in Berlin, a concert telecast nationally in the United States, he substituted Freedom for Joy to reflect his own "personal message". === Folk and protest music === In February 1952, the United States Customs Service seized the passport of Paul Robeson, preventing him from leaving the United States to travel to the Fourth Canadian Convention of the International Union of Mine, Mill, and Smelter Workers, in Vancouver, British Columbia, Canada; but, after "The convention heard Robeson sing over the telephone", the union organized "a concert on the US-Canada border". According to the account of the "Paul Robeson Centennial Celebration": "Robeson sang and spoke for 45 minutes. He introduced his first song stating


id: ascribed-characteristics-20260505-0
file: data/en.wikipedia.org/wiki/Ascribed_characteristics-0.md
category: reference
title: Ascribed characteristics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ascribed_characteristics
date_saved: 2026-05-05T06:38:49.294861+00:00
instance: kb-cron
parent: ascribed-characteristics-20260505-0
summary:
Ascribed characteristics, as used in the social sciences, refers to properties of an individual attained at birth, by inheritance, or through the aging process. The individual has very little, if any, control over these characteristics. Typical examples include race, ethnicity, gender, caste, height, and appearance. The term is apt for describing characteristics chiefly caused by "nature" (e.g. genetics) and for those chiefly caused by "nurture" (e.g. parenting during early childhood), see: Nature versus nurture. == Use within demography == Demography being the statistical study of populations requires the ability to differentiate between populations. Most populations self-ascribe themselves as being different from others by the creation of a country. This allows demographers to draw lines between countries and compare them. However useful, countries have significant complex populations that require more exacting definitions. Commonly seen are uses of race, gender and ethnicity. In the scope of academic demography, all of these are social constructs, ascribed to groups or individuals for stratification. With these theories in place, many new theories can be formed and data collected to either prove or disprove them. Ascribed characteristics can have large by-products, whether perceived or not. Discussed below are race, gender, social status / caste and hiring


id: bioculture-20260505-0
file: data/en.wikipedia.org/wiki/Bioculture-0.md
category: reference
title: Bioculture
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Bioculture
date_saved: 2026-05-05T06:38:50.491441+00:00
instance: kb-cron
parent: bioculture-20260505-0
summary:
Bioculture is the combination of biological and cultural factors that affect human behavior. It is an area of study bounded by the medical sciences, social sciences, landscape ecology, cultural anthropology, biotechnology, disability studies, the humanities, and the economic and global environment. Along these lines, one can see the biosphere — the earth as it is affected by the human — as the adaptation of the natural to the human and biocultures as the inter-adaptation of the human to the new technologies and ways of knowing characterized by the 21st century’s attitude toward the body. It assumes that in bioculture there is a diverse way to know the workings of the body and mind, and that these are primarily culturally derived, and an expert's way of knowing produces specific strong results. However, the results do not have an exclusive purview over the body and mind. Plus, it seeks to develop and encourage not only the experts but also parts of people's bodies and minds as the subject of study. == Alternative definitions == An alternative definition of the term bioculture is all the practical aspects of the use of living things in culture, including agriculture, production of food and clothing, forestry,


id: change-and-continuity-20260505-0
file: data/en.wikipedia.org/wiki/Change_and_continuity-0.md
category: reference
title: Change and continuity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Change_and_continuity
date_saved: 2026-05-05T06:38:51.651645+00:00
instance: kb-cron
parent: change-and-continuity-20260505-0
summary:
Change and continuity is a classic dichotomy within the fields of history, historical sociology, and the social sciences more broadly. The question of change and continuity is considered a classic discussion in the study of historical developments. The dichotomy is used to discuss and evaluate the extent to which a historical development or event represents a decisive historical change or whether a situation remains largely unchanged. A good example of this discussion is the question of how much the Peace of Westphalia in 1648 represents an important change in European history. In a similar vein, historian Richard Kirkendall once questioned whether FDR's New Deal represented "a radical innovation or a continuation of earlier themes in American life?" and posed the question of whether "historical interpretations of the New Deal [should] stress change or emphasize continuity?" The issue here is if the New Deal marks something radically new (change) in US history or if the New Deal can be understood as a continuation (continuity) of tendencies in American history that were in place well before the 1930. The dichotomy is important in relation to constructing, discussing, and evaluating historical periodizations. In terms of creating and discussing periodization (e.g. the Enlightenment or


id: collective-mental-state-20260505-0
file: data/en.wikipedia.org/wiki/Collective_mental_state-0.md
category: reference
title: Collective mental state
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Collective_mental_state
date_saved: 2026-05-05T06:38:52.911405+00:00
instance: kb-cron
parent: collective-mental-state-20260505-0
summary:
Collective mental state is generally a literary or legal term, mostly used in sociology and philosophy (in addition to its singular use in psychiatry and psychology), to refer to the condition of someone's state and physical and mental being when in the presence of others. An assessment of a collective mental state includes a description of thought processes, memory, emotions, mood, cognitive state, and energy levels, including the meta overlay of interactions between individuals. == Overview == A collective mental state is both distinct from and contains other mental states of self-aware individuals. The collective mental state forms the basis for individual reflection, juxtaposed with the collective state, that leads to realizations about emotions, states of being, and individuality. The collective mental state is made of conscious minds and may therefore be a more complex version of something like a stampede, which is itself caused by sentinel animals through imitation. Collective mental states may be simulated through the use of a performer and audience; stand-up comedy desires to have a group of people collectively, simultaneously feel one thing: laughter. == Background == When a mental state is shared by a large proportion of the members of a group or society,


id: criminology-20260505-0
file: data/en.wikipedia.org/wiki/Criminology-0.md
category: reference
title: Criminology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Criminology
date_saved: 2026-05-05T06:38:54.233529+00:00
instance: kb-cron
parent: criminology-20260505-0
summary:
Criminology (from Latin crimen, 'accusation', and Ancient Greek -λογία, -logia, from λόγος logos, 'word, reason') is the interdisciplinary study of crime and deviant behaviour. Criminology is a multidisciplinary field in both the behavioural and social sciences, which draws primarily upon the research of sociologists, political scientists, economists, legal sociologists, psychologists, philosophers, psychiatrists, social workers, biologists, social anthropologists, scholars of law and jurisprudence, as well as the processes that define administration of justice and the criminal justice system. The interests of criminologists include the study of the nature of crime and criminals, origins of criminal law, etiology of crime, social reaction to crime, and the functioning of law enforcement agencies and the penal institutions. It can be broadly said that criminology directs its inquiries along three lines: first, it investigates the nature of criminal law and its administration and conditions under which it develops; second, it analyzes the causation of crime and the personality of criminals; and third, it studies the control of crime and the rehabilitation of offenders. Thus, criminology encompasses the activities of legislative bodies, law-enforcement agencies, judicial institutions, correctional institutions, and educational, private, and public social agencies. == History of academic criminology == Modern academic criminology has direct


id: criminology-20260505-1
file: data/en.wikipedia.org/wiki/Criminology-1.md
category: reference
title: Criminology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Criminology
date_saved: 2026-05-05T06:38:54.233529+00:00
instance: kb-cron
parent: criminology-20260505-0
summary:
=== Positivist === The Positivist school argues that criminal behavior stems from internal and external factors beyond the individual's control. Its key method of thought is that criminals are born as criminals and not made into them; this school of thought also supports theory of nature in the debate between nature versus nurture. They also argue that criminal behavior is innate and within a person. Philosophers in this school applied the scientific method to the study of human behavior. Positivism comprises three segments: biological, psychological and social positivism. Psychological positivism is the view that criminal acts, or the people who commit them, are driven by internal factors. Social positivism, often referred to as Sociological Positivism, posits that criminals are produced by society. This school claims that low-income levels, high poverty/unemployment rates, and poor educational systems create and motivate criminals. ==== Criminal personality ==== The notion of a criminal personality is derived from the school of thought known as psychological positivism. It essentially means that parts of an individual's personality exhibit traits commonly associated with criminals, such as neuroticism, antisocial tendencies, aggressive behavior, and other factors. There is evidence of correlation, but not causation, between these personality traits and criminal actions.


id: criminology-20260505-2
file: data/en.wikipedia.org/wiki/Criminology-2.md
category: reference
title: Criminology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Criminology
date_saved: 2026-05-05T06:38:54.233529+00:00
instance: kb-cron
parent: criminology-20260505-0
summary:
==== Disorganization ==== Social disorganization theory is based on the work of Henry McKay and Clifford R. Shaw of the Chicago School. Social disorganization theory postulates that neighborhoods plagued with poverty and economic deprivation tend to experience high rates of population turnover. This theory suggests that crime and deviance are valued within groups in society, 'subcultures' or 'gangs'. These groups have different values from the social norm. These neighborhoods also tend to have high population heterogeneity. With high turnover, informal social structure often fails to develop, which in turn makes it difficult to maintain social order in a community. ==== Social ecology ==== Since the 1950s, social ecology studies have built on the social disorganization theories. Many studies have found that crime rates are associated with poverty, disorder, high numbers of abandoned buildings, and other signs of community deterioration. As working and middle-class people leave deteriorating neighborhoods, the most disadvantaged portions of the population may remain. William Julius Wilson suggested a poverty "concentration effect", which may cause neighborhoods to be isolated from the mainstream of society and become prone to violence. ==== Strain ==== Strain theory, also known as Mertonian Anomie, advanced by American sociologist Robert Merton, suggests that mainstream


id: criminology-20260505-3
file: data/en.wikipedia.org/wiki/Criminology-3.md
category: reference
title: Criminology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Criminology
date_saved: 2026-05-05T06:38:54.233529+00:00
instance: kb-cron
parent: criminology-20260505-0
summary:
Psychoanalysis is a psychological theory (and therapy) which regards the unconscious mind, repressed memories, and trauma, as the key drivers of behavior, especially deviant behavior. Sigmund Freud discussed how the unconscious desire for pain relates to psychoanalysis in his essay, Beyond the Pleasure Principle. Freud suggested that unconscious impulses such as 'repetition compulsion' and a 'death drive' can dominate a person's creativity, leading to self-destructive behavior. Phillida Rosnick, in the article Mental Pain and Social Trauma, posits a difference in the thoughts of individuals suffering traumatic unconscious pain, which corresponds to them having thoughts and feelings that are not reflections of their true selves. There is enough correlation between this altered state of mind and criminality to suggest causation. Sander Gilman, in the article Freud and the Making of Psychoanalysis, looks for evidence in the physical mechanisms of the human brain and the nervous system and suggests there is a direct link between an unconscious desire for pain or punishment and the impulse to commit crime or deviant acts. === Symbolic interactionism === Symbolic interactionism draws on the phenomenology of Edmund Husserl and George Herbert Mead, as well as subcultural theory and conflict theory. This school of thought focused on


id: criminology-20260505-4
file: data/en.wikipedia.org/wiki/Criminology-4.md
category: reference
title: Criminology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Criminology
date_saved: 2026-05-05T06:38:54.233529+00:00
instance: kb-cron
parent: criminology-20260505-0
summary:
Rational choice theory is based on the utilitarian, classical school philosophies of Cesare Beccaria, which were popularized by Jeremy Bentham. They argued that punishment, if certain, swift, and proportionate to the crime, was a crime deterrent, with the risks outweighing any possible benefits to the offender. In Dei delitti e delle pene (On Crimes and Punishments, 1763–1764), Beccaria advocated a rational penology. Beccaria conceived of punishment as the necessary application of the law for a crime; thus, the judge was to confirm their sentence to the law. Beccaria also distinguished between crime and sin, and advocated against the death penalty, as well as torture and inhumane treatments, as he did not consider them as rational deterrents. This philosophy was replaced by the positivist and Chicago schools and was not revived until the 1970s with the writings of James Q. Wilson, Gary Becker's 1965 article Crime and Punishment and George Stigler's 1970 article The Optimum Enforcement of Laws. Rational choice theory argues that criminals, like other people, weigh costs or risks and benefits when deciding whether to commit crime and think in economic terms. They will also try to minimize risks of crime by considering the time, place, and other situational


id: criminology-20260505-5
file: data/en.wikipedia.org/wiki/Criminology-5.md
category: reference
title: Criminology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Criminology
date_saved: 2026-05-05T06:38:54.233529+00:00
instance: kb-cron
parent: criminology-20260505-0
summary:
In 1968, young British sociologists formed the National Deviance Conference (NDC) group. The group, restricted to academics, consisted of 300 members. Ian Taylor, Paul Walton, and Jock Young – members of the NDC – rejected previous explanations of crime and deviance. Thus, they decided to pursue a new Marxist criminological approach. In The New Criminology, they argued against the biological "positivism" perspective represented by Lombroso, Hans Eysenck and Gordon Trasler. According to the Marxist perspective on crime, "defiance is normal – the sense that men are now consciously involved ... in assuring their human diversity." Thus, Marxist criminologists argued for a society in which the facts of human diversity, whether social or personal, would not be criminalized. They further attributed the processes of crime creation not to genetic or psychological facts, but rather to the material basis of a given society. State crime is a distinct category of crimes studied by Marxist criminology, which considers these crimes among the most costly to society in terms of overall harm/injury. In a Marxist framework, genocides, environmental degradation, and war are not crimes that occur out of contempt for one's fellow man, but are crimes of power. They continue systems of control and


id: criminology-20260505-6
file: data/en.wikipedia.org/wiki/Criminology-6.md
category: reference
title: Criminology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Criminology
date_saved: 2026-05-05T06:38:54.233529+00:00
instance: kb-cron
parent: criminology-20260505-0
summary:
=== Cultural === Cultural criminology views crime and its control within the context of culture. Ferrell believes criminologists can examine the actions of criminals, control agents, media producers, and others to construct the meaning of crime. He discusses these actions as a means to show the dominant role of culture. Kane adds that cultural criminology has three tropes: village, city street, and media, in which males can be geographically influenced by society's views on what is broadcast and accepted as right or wrong. The village is where one engages in available social activities. Linking an individual's history to a location can help determine social dynamics. The city street involves positioning oneself in the cultural area. This is full of those affected by poverty, poor health, and crime, and large buildings that impact the city but not the neighborhoods. Mass media gives an all-around account of the environment and the possible other subcultures that could exist beyond a specific geographical area. It was later that Naegler and Salman introduced feminist theory to cultural criminology and discussed masculinity and femininity, sexual attraction and sexuality, and intersectional themes. Naegler and Salman believed that Ferrell's mold was limited and that they could add to


id: criminology-20260505-7
file: data/en.wikipedia.org/wiki/Criminology-7.md
category: reference
title: Criminology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Criminology
date_saved: 2026-05-05T06:38:54.233529+00:00
instance: kb-cron
parent: criminology-20260505-0
summary:
Ultra-realism is a 21st-century theoretical school and research programme that straddles criminology and zemiology. Ultra-realists revisit the fundamental question that underpins both disciplines: why, rather than seeking solidarity and cooperation, do specific individuals, groups, or institutions choose to risk harming others in pursuit of their own interests? Early proponents of the ultra-realist perspective are Steve Hall and Simon Winlow. The original ultra-realist concepts of the pseudo-pacification process, special liberty, and objectless anxiety first emerged from the mid-1990s onwards in a series of articles and books. The ultra-realist framework began to take a clearly defined shape in three later works. Ultra-realist researchers operate in research fields such as deviant leisure and consumer culture; crime, harm and place; crime, harm, work and employment; Covid, lockdown and social harm; riots and far-right politics; violence and masculinity; crime, harm and glocal markets; policing and corruption; homicide and serial murder; crime, harm and mass media; crime, corruption and compliance; history and violence; crime, corruption and sport; hate crime; technology, harm and crime; the crime decline; child abuse; military studies; subjectivity and investment fraud; crime, harm and drugs; the criminology of borders. == Types and definitions of crime == Both the positivist and classical schools take


id: cultural-studies-20260505-0
file: data/en.wikipedia.org/wiki/Cultural_studies-0.md
category: reference
title: Cultural studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cultural_studies
date_saved: 2026-05-05T06:38:55.506561+00:00
instance: kb-cron
parent: cultural-studies-20260505-0
summary:
Cultural studies or cultural theory is an academic field that explores the dynamics of contemporary culture (including the politics of popular culture) and its social and historical foundations. Cultural studies researchers investigate how cultural practices relate to wider systems of power associated with, or operating through, social phenomena. These include ideology, class structures, national formations, ethnicity, sexual orientation, gender, and generation. Employing cultural analysis, cultural studies views cultures not as fixed, bounded, stable, and discrete entities, but rather as constantly interacting and changing sets of practices and processes. British Marxist academics initially developed cultural studies in the late 1950s, 1960s, and 1970s, and it has since been taken up and transformed by scholars from many different disciplines around the world. Cultural studies is avowedly, and even radically, interdisciplinary and can sometimes be seen as anti-disciplinary. A key concern for cultural studies practitioners is the examination of the forces within and through which socially organized people conduct and participate in the construction of their everyday lives. Cultural studies combines a variety of politically engaged critical approaches including semiotics, Marxism, feminist theory, ethnography, post-structuralism, postcolonialism, social theory, political theory, history, philosophy, literary theory, media theory, film/video studies, communication studies, political economy, translation


id: cultural-studies-20260505-1
file: data/en.wikipedia.org/wiki/Cultural_studies-1.md
category: reference
title: Cultural studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cultural_studies
date_saved: 2026-05-05T06:38:55.506561+00:00
instance: kb-cron
parent: cultural-studies-20260505-0
summary:
=== Stuart Hall's directorship of CCCS === Beginning in 1964, after the initial appearance of the founding works of British Cultural Studies in the late 1950s, Stuart Hall's pioneering work at CCCS, along with that of his colleagues and postgraduate students, gave shape and substance to the field of cultural studies. This would include such people as Paul Willis, Dick Hebdige, David Morley, Charlotte Brunsdon, John Clarke, Richard Dyer, Judith Williamson, Richard Johnson, Iain Chambers, Dorothy Hobson, Chris Weedon, Tony Jefferson, Michael Green, and Angela McRobbie. Many cultural studies scholars employed Marxist methods of analysis, exploring the relationships between cultural forms (i.e., the superstructure) and that of the political economy (i.e., the base). By the 1970s, the work of Louis Althusser had radically rethought the Marxist account of base and superstructure, significantly influencing the "Birmingham School". Much of the work done at CCCS examined youth subcultural expressions of antagonism toward "respectable" middle-class British culture in the post-WWII period. Also during the 1970s, the politically formidable British working classes were in decline. Britain's manufacturing industries, while continuing to grow in output and value, were decreasing in share of GDP and numbers employed, and union rolls were shrinking. Millions of working-class Britons


id: cultural-studies-20260505-2
file: data/en.wikipedia.org/wiki/Cultural_studies-2.md
category: reference
title: Cultural studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cultural_studies
date_saved: 2026-05-05T06:38:55.506561+00:00
instance: kb-cron
parent: cultural-studies-20260505-0
summary:
== Developments outside the UK == In the US, before the emergence of British cultural studies, several versions of cultural analysis had emerged largely from pragmatic and liberal-pluralist philosophical traditions. However, in the late 1970s and 1980s, when British Cultural Studies began to spread internationally, and to engage with feminism, post-structuralism, postmodernism, and race, critical cultural studies (i.e., Marxist, feminist, poststructuralist, etc.) expanded tremendously in American universities in fields such as communication studies, education, sociology, and literature. Cultural Studies, the flagship journal of the field, has been based in the US since its founding editor, John Fiske, brought it there from Australia in 1987. A thriving cultural studies scene has existed in Australia since the late 1970s, when several key CS practitioners emigrated there from the UK, bringing British cultural studies with them, after Margaret Thatcher became Prime Minister of the UK in 1979. A school of cultural studies known as cultural policy studies is one of the distinctive Australian contributions to the field, though it is not the only one. Australia also gave birth to the world's first professional cultural studies association (now known as the Cultural Studies Association of Australasia) in 1990. Cultural studies journals based in Australia


id: cultural-studies-20260505-3
file: data/en.wikipedia.org/wiki/Cultural_studies-3.md
category: reference
title: Cultural studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cultural_studies
date_saved: 2026-05-05T06:38:55.506561+00:00
instance: kb-cron
parent: cultural-studies-20260505-0
summary:
=== Structure and agency === The development of hegemony theory in cultural studies was, in some ways, consonant with work in other fields exploring agency, a theoretical concept that emphasizes the active, critical capacities of subordinated people (e.g., the working classes, colonized peoples, women). As Stuart Hall famously argued in his 1981 essay, "Notes on Deconstructing 'the Popular'": "ordinary people are not cultural dopes." Insistence on accounting for the agency of subordinated people run counter to the work of traditional structuralists. Some analysts have, however, been critical of certain work in cultural studies that they feel overstates the significance of, or even romanticizes, some forms of popular cultural agency. Cultural studies often concerns itself with agency at the level of everyday practices, and approaches such research from a standpoint of radical contextualism. In other words, cultural studies rejects universal accounts of cultural practices, meanings, and identities. Judith Butler, an American feminist theorist whose work is often associated with cultural studies, wrote that: the move from a structuralist account in which capital is understood to structure social relations in relatively homologous ways to a view of hegemony in which power relations are subject to repetition, convergence, and rearticulation brought the question


id: cultural-studies-20260505-4
file: data/en.wikipedia.org/wiki/Cultural_studies-4.md
category: reference
title: Cultural studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cultural_studies
date_saved: 2026-05-05T06:38:55.506561+00:00
instance: kb-cron
parent: cultural-studies-20260505-0
summary:
== Integration of popular culture in CS and education == The integration of popular culture in classrooms has influenced educational practices in cultural studies. By analyzing TV series, movies, memes, and other cultural materials, educators can encourage media literacy, critical thinking, and a deeper understanding of social issues. Incorporating popular culture into education through cultural studies helps students critically engage with the world around them, fostering media literacy and critical thinking. Educators can use cultural texts to discuss societal issues, challenge norms, and prepare students for active participation in a media-dominated world. Popular culture can be an effective tool for critical pedagogy. Evan Faidley explores how TV shows, movies, and memes can be used in the classroom to discuss topics like social justice and identity. Shows like South Park allow students to evaluate societal norms and political issues, using a pedagogy of resistance. Cultural studies encourage students to analyze intertextuality. Patricia Duff discusses how popular culture incorporates with academic discourse to build media literacy, which helps students critically engage with the media they consume daily. Kathy Mills also highlights the importance of multiliteracies, which encourages students to utilize a variety of communication media outside of the standard text, including digital


id: cultural-studies-20260505-5
file: data/en.wikipedia.org/wiki/Cultural_studies-5.md
category: reference
title: Cultural studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cultural_studies
date_saved: 2026-05-05T06:38:55.506561+00:00
instance: kb-cron
parent: cultural-studies-20260505-0
summary:
In 1996, physicist Alan Sokal expressed his opposition to cultural studies by submitting a hoax article to a cultural studies journal, Social Text. The article, crafted as a parody of what Sokal called the "fashionable nonsense" of postmodernism, was accepted by the journal's editors, who at the time did not practice peer review. When the paper appeared in print, Sokal published a second article in a self-described "academic gossip" magazine, Lingua Franca, revealing his hoax on Social Text. Sokal stated that his motivation stemmed from his rejection of contemporary critiques of scientific rationalism: Politically, I'm angered because most (though not all) of this silliness is emanating from the self-proclaimed Left. We're witnessing here a profound historical volte-face. For most of the past two centuries, the Left has been identified with science and against obscurantism; we have believed that rational thought and the fearless analysis of objective reality (both natural and social) are incisive tools for combating the mystifications promoted by the powerful – not to mention being desirable human ends in their own right. The recent turn of many "progressive" or "leftist" academic humanists and social scientists toward one or another form of epistemic relativism betrays this worthy heritage and


id: distributive-justice-20260505-0
file: data/en.wikipedia.org/wiki/Distributive_justice-0.md
category: reference
title: Distributive justice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Distributive_justice
date_saved: 2026-05-05T06:38:57.982793+00:00
instance: kb-cron
parent: distributive-justice-20260505-0
summary:
Distributive justice concerns the socially just allocation of resources, goods, opportunity in a society. It is concerned with how to allocate resources fairly among members of a society, taking into account factors such as wealth, income, and social status. Often contrasted with just process and formal equal opportunity, distributive justice concentrates on outcomes (substantive equality). This subject has been given considerable attention in philosophy and the social sciences. Theorists have developed widely different conceptions of distributive justice. These have contributed to debates around the arrangement of social, political and economic institutions to promote the just distribution of benefits and burdens within a society. Most contemporary theories of distributive justice rest on the precondition of material scarcity. From that precondition arises the need for principles to resolve competing interest and claims concerning a just or at least morally preferable distribution of scarce resources. In social psychology, distributive justice is defined as perceived fairness of how rewards and costs are shared by (distributed across) group members. For example, when some workers work more hours but receive the same pay, group members may feel that distributive justice has not occurred. To determine whether distributive justice has taken place, individuals often turn to the


id: distributive-justice-20260505-1
file: data/en.wikipedia.org/wiki/Distributive_justice-1.md
category: reference
title: Distributive justice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Distributive_justice
date_saved: 2026-05-05T06:38:57.982793+00:00
instance: kb-cron
parent: distributive-justice-20260505-0
summary:
==== Basic principles of a just distribution ==== In this original position, the main concern will be to secure the goods that are most essential for pursuing the goals of each individual, regardless of what this specific goal might be. With this in mind, Rawls theorizes two basic principles of just distribution. The first principle, the liberty principle, is the equal access to basic rights and liberties for all. With this, each person should be able to access the most extensive set of liberties that is compatible with similar schemes of access by other citizens. Thereby, it is not only a question of positive individual access but also of negative restrictions so as to respect others' basic rights and liberties. The second principle, the difference principle, addresses how the arrangement of social and economic inequalities, and thus the just distribution should look. Firstly, Rawls argues that such distribution should be based on a reasonable expectation of advantage for all, but also to the greatest benefit of the least advantaged in society. Secondly, the offices and positions attached to this arrangement should be open to all. These principles of justice are then prioritised according to two additional principles: === Utilitarianism ===


id: distributive-justice-20260505-2
file: data/en.wikipedia.org/wiki/Distributive_justice-2.md
category: reference
title: Distributive justice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Distributive_justice
date_saved: 2026-05-05T06:38:57.982793+00:00
instance: kb-cron
parent: distributive-justice-20260505-0
summary:
=== Outcomes === Recent research has introduced probabilistic models, such as the Boltzmann Fair Division, which apply statistical and thermodynamic principles to the allocation of resources in society. These models provide a flexible and unbiased approach to distributive justice, allowing parameters to be tuned for equality, merit, or need. The Boltzmann fair division framework has been shown to bridge classical theories and practical policy applications, enabling fair and efficient distributions across diverse settings. Distributive justice also affects organizational performance when efficiency and productivity are involved. Improving perceptions of justice increases performance. Organizational citizenship behaviors (OCBs) are employee actions in support of the organization that are outside the scope of their job description. Such behaviors depend on the degree to which an organization is perceived to be distributively just. As organizational actions and decisions are perceived as more just, employees are more likely to engage in OCBs. Perceptions of distributive justice are also strongly related to the withdrawal of employees from the organization. === Wealth === Distributive justice considers whether the distribution of goods among the members of society at a given time is subjectively acceptable. Not all advocates of consequentialist theories are concerned with an equitable society. What unites them


id: distributive-justice-20260505-3
file: data/en.wikipedia.org/wiki/Distributive_justice-3.md
category: reference
title: Distributive justice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Distributive_justice
date_saved: 2026-05-05T06:38:57.982793+00:00
instance: kb-cron
parent: distributive-justice-20260505-0
summary:
=== Friedrich von Hayek === Within the context of Western liberal democracies in the post-WWII decades, Friedrich von Hayek was one of the most famous opposers of the idea of distributive justice. For him, social and distributive justice were meaningless and impossible to attain, on the grounds of being within a system where the outcomes are not determined deliberately by the people but contrarily spontaneity is the norm. Therefore, distributive justice, redistribution of wealth, and the demands for social justice in a society ruled by an impersonal process such as the market are in this sense incompatible with that system. In his book The Road to Serfdom, there can be found considerations about social assistance from the state. There, in talking about the importance of a restrictive kind of security (the one against physical privation) in front of one that necessarily needs to control or abolish the market, Hayek poses that "there can be no doubt that some minimum of food, shelter, and clothing, sufficient to preserve health and the capacity to work, can be assured to everybody". Providing this type of security is for Hayek compatible with individual freedom as it does not involve planning. But already in this


id: doctor-of-social-science-20260505-0
file: data/en.wikipedia.org/wiki/Doctor_of_Social_Science-0.md
category: reference
title: Doctor of Social Science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Doctor_of_Social_Science
date_saved: 2026-05-05T06:38:59.215817+00:00
instance: kb-cron
parent: doctor-of-social-science-20260505-0
summary:
The Doctor of Social Science (DSocSci, SScD, Dr. rer. soc. or DSS) degree is the highest degree offered by some universities in the field of social sciences, for which other universities confer a Ph.D. Like the PhD, it is recognized as a terminal research degree that requires a substantial original thesis. In North America, the only universities to offer a Doctor of Social Science are Royal Roads University in British Columbia, Canada, and Wilmington University in New Castle, Delaware, United States. == Argentina == Provided by the National University of Luján == Australia == Provided by the University of Queensland Provided by the University of Sydney == Canada == Provided by Royal Roads University == Finland == Provided by the University of Tampere Provided by the University of Helsinki and at which, somewhat infelicitously, the terminal degree in Practical philosophy is awarded as a Doctor of Social Science (DSS), rather than a Doctor of Philosophy (Ph.D.). == Germany == Provided e.g. by the University of Mannheim and by the Ludwig-Maximilians-Universität München. == Holy See == Provided by the Pontifical Gregorian University. == Hong Kong == Provided by the University of Hong Kong. == Ireland == The DSocSci is offered as


id: efficient-voter-rule-20260505-0
file: data/en.wikipedia.org/wiki/Efficient_Voter_Rule-0.md
category: reference
title: Efficient Voter Rule
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Efficient_Voter_Rule
date_saved: 2026-05-05T06:39:00.536186+00:00
instance: kb-cron
parent: efficient-voter-rule-20260505-0
summary:
In the study of voter behavior, the efficient voter rule speaks to the desirability of voter-driven outcomes. It applies to situations involving negative externalities such as pollution and crime, and positive externalities such as education. Related efforts to achieve socially optimal quantities of externalities have long been a focus of microeconomic research, most famously by Ronald Coase and Arthur Pigou. Externality problems persist despite past remedies, which makes newer approaches such as the efficient voter rule important. In the context of negative externalities, the efficient voter rule states that when individuals who receive the same harm from a problem vote on whether to eliminate that problem at a uniform cost per individual, the outcome will be efficient, regardless of each individual’s contribution to the problem. The Rule applies similarly to positive externalities, as exemplified by the solar panel example below. The efficient voter rule indicates that voting on a collective action or policy change should lead to an efficient outcome. Possible applications include policy decisions about clean energy, noise pollution, over-fishing, mandatory immunizations, smoking bans, zoning, septic systems, and fuel economy standards. In the context of crime, recent applications include votes on the strict enforcement of traffic laws. The vote


id: enchantment-social-sciences-20260505-0
file: data/en.wikipedia.org/wiki/Enchantment_(social_sciences)-0.md
category: reference
title: Enchantment (social sciences)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Enchantment_(social_sciences)
date_saved: 2026-05-05T06:39:01.882101+00:00
instance: kb-cron
parent: enchantment-social-sciences-20260505-0
summary:
Enchantment is a term widely used to describe something delightful, possibly magical, that causes a feeling of wonder. It has been adapted by a range of scholars across multiple disciplines, especially anthropology and sociology, and then later urban studies, to describe the ways in which people create moments of wonder in the midst of everyday life. British anthropologist Alfred Gell described art as "the technology of enchantment," permitting us to "see the real world in an enchanted form". American sociologist George Ritzer uses the term enchantment in his analysis of consumerism and McDonaldization, making the argument that "enchantment of the consumer is necessary for the spell to work". == Enchantment engineers == A different strand was developed by Belgian scholar Yves Winkin, who highlighted the element of collusion between "enchantment engineers" (the people who create something requiring the willing suspension of disbelief) and visitors to locations where the illusion or wonder has been created to delight them. Enchantment here is about experiencing a moment of unexpected pleasure or happiness, focusing not on times it occurs randomly, but when it results from someone's deliberate efforts. In these cases, enchantment refers to "places and landscapes created with the intention of inducing a


id: force-field-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Force-field_analysis-0.md
category: reference
title: Force-field analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Force-field_analysis
date_saved: 2026-05-05T06:39:03.122947+00:00
instance: kb-cron
parent: force-field-analysis-20260505-0
summary:
In social science, force-field analysis provides a framework for looking at the factors ("forces") that influence a situation, originally social situations. It looks at forces that are either driving the movement toward a goal (helping forces) or blocking movement toward a goal (hindering forces). The principle, developed by Kurt Lewin, is a significant contribution to the fields of social science, psychology, social psychology, community psychology, communication, organizational development, process management, and change management. == History == Lewin, a social psychologist, believed the "field" to be a Gestalt psychological environment existing in an individual's (or in the collective group) mind at a certain point in time that can be mathematically described in a topological constellation of constructs. The "field" is very dynamic, changing with time and experience. When fully constructed, an individual's "field" (Lewin used the term "life space") describes that person's motives, values, needs, moods, goals, anxieties, and ideals. Lewin believed that changes of an individual's "life space" depend upon that individual's internalization of external stimuli (from the physical and social world) into the "life space". Although Lewin did not use the word "experiential" (see experiential learning), he nonetheless believed that interaction (experience) of the "life space" with "external stimuli"


id: health-survival-paradox-20260505-0
file: data/en.wikipedia.org/wiki/Health_survival_paradox-0.md
category: reference
title: Health survival paradox
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Health_survival_paradox
date_saved: 2026-05-05T06:39:05.453531+00:00
instance: kb-cron
parent: health-survival-paradox-20260505-0
summary:
The male-female health survival paradox, also known as the morbidity-mortality paradox or gender paradox, is the phenomenon in which female humans experience more medical conditions and disability during their lives, but live longer than males. The observation that females experience greater morbidity (diseases) but lower mortality (death) in comparison to males is paradoxical since it is expected that experiencing disease increases the likelihood of death. However, in this case, the part of the population that experiences more disease and disability is the one that lives longer. == Background and history == The male-female health survival paradox has been most reliably reported in literature and documented as far back as the 18th century in European historical records. Some of the last records of European men outliving women are from the Netherlands in 1860 and Italy in 1889. The earliest records of European women outliving men were from Sweden in 1751, Denmark in 1835, and both England and Wales in 1841. While women were documented to outlive men in Europe, data from 1887 through 1930 showed that females between ages 5 and 25 in Massachusetts disproportionately faced mortality due to infectious diseases. With improvements in infectious disease prevention, treatment, and eradication of


id: health-survival-paradox-20260505-1
file: data/en.wikipedia.org/wiki/Health_survival_paradox-1.md
category: reference
title: Health survival paradox
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Health_survival_paradox
date_saved: 2026-05-05T06:39:05.453531+00:00
instance: kb-cron
parent: health-survival-paradox-20260505-0
summary:
==== Genetic factors ==== The female sex has two X chromosomes that can protect against expression of recessive genes and allows a female survival advantage. A research study conducted on flies indicated that the alleles that contribute to male inclusive fitness also harm female health, and thus contribute to the paradox. ==== Physiological factors ==== It is also a possibility that the female hormone, estrogen, contributes to the female survival advantage. In cutaneous melanoma, estrogen was evaluated to determine its effect on a steroid hormone-sensitive cancer. While no difference in survival was concluded between two genders due to limited data, women tend to have better prognosis due to the presence of estrogen receptor beta. However, this is a continued study that may be due to biological factors—such as immune response, inflammation, pharmacokinetics, or hormones—or from social factors—such as women tending to have more ultraviolet protection and frequent medical visits. Although studies have shown the protective effects of estrogen on cardiovascular health (i.e. by lowering LDL and increasing HDL) and brain cell health, there are doubts about the role of hormones due to mixed results in hormone replacement therapy studies on elderly women. For instance, although lower levels of LDL may


id: health-survival-paradox-20260505-2
file: data/en.wikipedia.org/wiki/Health_survival_paradox-2.md
category: reference
title: Health survival paradox
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Health_survival_paradox
date_saved: 2026-05-05T06:39:05.453531+00:00
instance: kb-cron
parent: health-survival-paradox-20260505-0
summary:
Records of the female survival advantage can be traced back to the 18th century, but the phenomenon gained popularity and caught the eyes of researchers in the 19th century. Females outlive males for all age groups and every year for which reliable records exist. Specifically, in "contemporary industrialized countries", female survival is 1.5-2.0 times higher than that of males. A female survival advantage is found in some, but not all species. Various explanations for this have been proposed but none are strongly supported. Most species studied for differences in morbidity and mortality between sexes show conditional sex differences in life span, with both male and females experiencing advantage depending on the species. In humans, females appear to have a consistent survival advantage. Females outlive males in 176 of 178 countries for which records are available, both at age 5 and at age 50. In a study in the UK, males scoring higher "femininity scores", when compared to their more stereotypical "masculine" male counterparts, had lower death rates from heart disease, suggesting that masculine behavior increases the risk of premature mortality. The female survival advantage holds true among humans, but the same can not be said for baboons and birds. In


id: heortology-20260505-0
file: data/en.wikipedia.org/wiki/Heortology-0.md
category: reference
title: Heortology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Heortology
date_saved: 2026-05-05T06:39:06.697014+00:00
instance: kb-cron
parent: heortology-20260505-0
summary:
Heortology or eortology is a science that deals with the origin and development of religious festivals, and more specifically the study of the history and criticism of liturgical calendars and martyrologies. == Etymology == The noun eortology comes from the ancient Greek compound of the term ἑορτή "feast" and the suffix -logia which means "study". Thus, eortology is the study of festivals, especially their history and meaning in the church year. == History == Christian heortology dates back at least to Johann Adam Trummerer's Eortologia Anagrammaike published in 1607. German Lutheran theologian Michael Lilienthal published a German-language heortology in 1724 to show the origin of Christian celebrations. Wilhelm Dibelius published the second eortological study of the Christian rite in German in 1841. In parallel with modern archaeology, studies also examined the question of the rites of Greek and Roman antiquity, following the work of August Mommsen, published in Leipzig in 1864 under the title of Heortologie: Antiquarische Untersuchungen über die städtischen Feste der Athener. Christian heortology particularly developed at the instigation of the Jesuit Nikolaus Nilles and the liturgical movement from the end of the 19th century. Prominent heortologists were the Sulpician Pierre Batiffol who published History of the Roman


id: humanities-arts-and-social-sciences-20260505-0
file: data/en.wikipedia.org/wiki/Humanities,_arts,_and_social_sciences-0.md
category: reference
title: Humanities, arts, and social sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Humanities,_arts,_and_social_sciences
date_saved: 2026-05-05T06:39:07.929978+00:00
instance: kb-cron
parent: humanities-arts-and-social-sciences-20260505-0
summary:
Humanities, Arts, and Social Sciences (HASS) (or Arts, Humanities and Social Sciences, AHSS), also known as social studies, is a broad term that groups together the academic disciplines of humanities, arts and social sciences. It is viewed as an academic counterpart to Science, Technology, Engineering, and Mathematics (STEM) in the United States, Canada, India, Australia, and other countries. HASS graduates comprise the majority of the workforce in many developed countries (e.g. 64% in Australia). However, HASS courses often receive lower governmental funding and may have lower reputations within universities. There is a measured relationship between citizens' HASS awareness with more accurate threat perceptions, high community activity, and cultural engagement at the local level. In recent years, a return to a holistic reintegration of HASS and STEM disciplines has been promoted in the U.S. by the National Academies of Sciences, Engineering, and Medicine. Some of the disciplines it covers are philosophy, theology, sociology, communication sciences, social psychology, human geography, demography, anthropology, social work, linguistics, history, archaeology, politics, economics, law, pedagogy, journalism, literature, musicology, administration, accounting, commerce, music, dance, painting, sculpture, graphic design, theater, cinematography, interior design, industrial design, criticism, art history, cultural studies, tourism, gastronomy, among others. In the Philippines, a


id: international-order-20260505-0
file: data/en.wikipedia.org/wiki/International_order-0.md
category: reference
title: International order
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/International_order
date_saved: 2026-05-05T06:39:19.016159+00:00
instance: kb-cron
parent: international-order-20260505-0
summary:
In international relations, international order consists of patterned or structured relationships (such as polarity) between actors on the international level. == Definition == David Lake, Lisa Martin and Thomas Risse define "order" as "patterned or structured relationships among units". Michael Barnett defines an international order as "patterns of relating and acting" derived from and maintained by rules, institutions, law and norms. International orders have both a material and social component. Legitimacy (the generalized perception that actions are desirable, proper or appropriate) is essential to political orders. George Lawson has defined an international order as "regularized practices of exchange among discrete political units that recognize each other to be independent." John Mearsheimer defines an international order "an organized group of international institutions that help govern the interactions among the member states." In After Victory (2001), John Ikenberry defines a political order as "the governing arrangements among a group of states, including its fundamental rules, principles and institutions." The United Nations has been characterized as a proxy for how states broadly perceive the international order. Jeff Colgan has characterized international order as entailing multiple subsystems. These subsystems can experience drastic change without fundamentally changing the international order. == Liberal international order ==


id: jurisprudence-20260505-0
file: data/en.wikipedia.org/wiki/Jurisprudence-0.md
category: reference
title: Jurisprudence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jurisprudence
date_saved: 2026-05-05T06:39:20.330338+00:00
instance: kb-cron
parent: jurisprudence-20260505-0
summary:
Jurisprudence, also known as theory of law or philosophy of law, is the examination in a general perspective of what law is and what it ought to be. It investigates issues such as the definition of law; legal validity; legal norms and values; and the relationship between law and other fields of study, including economics, ethics, history, sociology, and political philosophy. Modern jurisprudence began in the 18th century and was based on the first principles of natural law, civil law, and the law of nations. Contemporary philosophy of law addresses problems internal to law and legal systems, as well as problems of law as a social institution that relate to the larger political and social context in which it exists. Jurisprudence can be divided into categories both by the type of question scholars seek to answer and by the theories of jurisprudence, or schools of thought, regarding how those questions are best answered: Natural law holds that there are rational, objective limits to the power of rulers, the foundations of law are accessible through reason, and it is from these laws of nature that human laws gain force. Analytic jurisprudence attempts to describe what law is. The two historically dominant


id: jurisprudence-20260505-1
file: data/en.wikipedia.org/wiki/Jurisprudence-1.md
category: reference
title: Jurisprudence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jurisprudence
date_saved: 2026-05-05T06:39:20.330338+00:00
instance: kb-cron
parent: jurisprudence-20260505-0
summary:
Ancient jurisprudence begins with various Dharmaśāstra texts of India. Dharmasutras of Āpastaṃba and Baudhāyana are examples. In Ancient China, the Daoists, Confucians, and Legalists all had competing theories of jurisprudence. Jurisprudence in ancient Rome had its origins with the periti—experts in the jus mos maiorum (traditional law), a body of oral laws and customs. Praetors established a working body of laws by judging whether or not singular cases were capable of being prosecuted either by the edicta, the annual pronunciation of prosecutable offences, or in extraordinary situations, additions made to the edicta. A iudex (originally a magistrate, later a private individual appointed to judge a specific case) would then prescribe a remedy according to the facts of the case. The sentences of the iudex were supposed to be simple interpretations of the traditional customs, but—apart from considering what traditional customs applied in each case—soon developed a more equitable interpretation, coherently adapting the law to newer social exigencies. The law was then adjusted to reflect evolving institutiones (legal concepts) while remaining in the traditional mode. Praetors were replaced in the 3rd century BC by a laical body of prudentes. Admission to this body was conditional upon proof of competence or experience.


id: jurisprudence-20260505-2
file: data/en.wikipedia.org/wiki/Jurisprudence-2.md
category: reference
title: Jurisprudence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jurisprudence
date_saved: 2026-05-05T06:39:20.330338+00:00
instance: kb-cron
parent: jurisprudence-20260505-0
summary:
Aristotle is often said to be the father of natural law. Like his philosophical forefathers Socrates and Plato, Aristotle posited the existence of natural justice or natural right (dikaion physikon, δικαίον φυσικόν, Latin ius naturale). His association with natural law stems largely from his interpretation by Thomas Aquinas. This was based on Aquinas' conflation of natural law and natural right, the latter of which Aristotle posits in Book V of the Nicomachean Ethics (Book IV of the Eudemian Ethics). Aquinas's influence was such that it affected several early translations of these passages, though more recent translations render them more literally. Aristotle's theory of justice is bound up in his idea of the golden mean. Indeed, his treatment of what he calls "political justice" derives from his discussion of "the just" as a moral virtue derived as the mean between opposing vices, just like every other virtue he describes. The longest discussion of his theory of justice occurs in Nicomachean Ethics and begins by asking what sort of mean a just act is. He argues that the term "justice" actually refers to two different but related ideas: general justice and particular justice. When a person's actions toward others are completely virtuous


id: jurisprudence-20260505-3
file: data/en.wikipedia.org/wiki/Jurisprudence-3.md
category: reference
title: Jurisprudence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jurisprudence
date_saved: 2026-05-05T06:39:20.330338+00:00
instance: kb-cron
parent: jurisprudence-20260505-0
summary:
Writing after World War II, Lon L. Fuller defended a secular and procedural form of natural law. He emphasised that the (natural) law must meet certain formal requirements (such as being impartial and publicly knowable). To the extent that an institutional system of social control falls short of these requirements, Fuller argued, we are less inclined to recognise it as a system of law, or to give it our respect. Thus, the law must have a morality that goes beyond the societal rules under which laws are made. === John Finnis === Sophisticated positivist and natural law theories sometimes resemble each other and may have certain points in common. Identifying a particular theorist as a positivist or a natural law theorist sometimes involves matters of emphasis and degree, as well as the specific influences on the theorist's work. The natural law theorists of the distant past, such as Aquinas and John Locke, made no distinction between analytic and normative jurisprudence. In contrast, modern natural law theorists, such as John Finnis, who identify as positivists, still argue that law is moral by nature. In his book Natural Law and Natural Rights (1980, 2011), John Finnis provides a restatement of natural law


id: jurisprudence-20260505-4
file: data/en.wikipedia.org/wiki/Jurisprudence-4.md
category: reference
title: Jurisprudence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jurisprudence
date_saved: 2026-05-05T06:39:20.330338+00:00
instance: kb-cron
parent: jurisprudence-20260505-0
summary:
Legal positivism is the view that the content of law is dependent on social facts and that a legal system's existence is not constrained by morality. Within legal positivism, theorists agree that law's content is a product of social facts, but theorists disagree whether law's validity can be explained by incorporating moral values. Legal positivists who argue against the incorporation of moral values to explain law's validity are labeled exclusive (or hard) legal positivists. Joseph Raz's legal positivism is an example of exclusive legal positivism. Legal positivists who argue that law's validity can be explained by incorporating moral values are labeled inclusive (or soft) legal positivists. The legal positivist theories of H. L. A. Hart and Jules Coleman are examples of inclusive legal positivism. Legal positivism has traditionally been associated with three doctrines: the pedigree thesis, the separability thesis, and the discretion thesis. The pedigree thesis says that the right way to determine whether a directive is law is to look at the directive's source. The thesis claims that the fact that the directive was issued by the proper official within a legitimate government, for example, determines the directive's legal validity—not its moral or practical merits. The separability thesis states


id: jurisprudence-20260505-5
file: data/en.wikipedia.org/wiki/Jurisprudence-5.md
category: reference
title: Jurisprudence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jurisprudence
date_saved: 2026-05-05T06:39:20.330338+00:00
instance: kb-cron
parent: jurisprudence-20260505-0
summary:
In the English-speaking world, the most influential legal positivist of the twentieth century was H. L. A. Hart, professor of jurisprudence at Oxford University. Hart argued that the law should be understood as a system of social rules. In The Concept of Law, Hart rejected Kelsen's views that sanctions were essential to law and that a normative social phenomenon, like law, cannot be grounded in non-normative social facts. Hart claimed that law is the union of primary rules and secondary rules. Primary rules require individuals to act or not act in certain ways and create duties for the governed to obey. Secondary rules are rules that confer authority to create new primary rules or modify existing ones. Secondary rules are divided into rules of adjudication (how to resolve legal disputes), rules of change (how laws are amended), and the rule of recognition (how laws are identified as valid). The validity of a legal system derives from the "rule of recognition", a customary practice among officials (especially barristers and judges) who identify certain acts and decisions as sources of law. In 1981, Neil MacCormick wrote a pivotal book on Hart (second edition published in 2008), which further refined and offered some


id: jurisprudence-20260505-6
file: data/en.wikipedia.org/wiki/Jurisprudence-6.md
category: reference
title: Jurisprudence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jurisprudence
date_saved: 2026-05-05T06:39:20.330338+00:00
instance: kb-cron
parent: jurisprudence-20260505-0
summary:
=== Critical legal studies === Critical legal studies is a jurisprudential theory that has developed since the 1970s. In 1977, a group of members of the Law and Society Association struck out on a new theoretical direction. The legal ideas of Peter Gabel, Morton Horwitz, Duncan Kennedy, Karl Klare, Mark Tushnet, and Roberto Unger have now found influence in many law schools. The theory can generally be traced to American legal realism and is considered "the first movement in legal theory and legal scholarship in the United States to have espoused a committed Left political stance and perspective". It holds that the law is largely contradictory, and can be best analyzed as an expression of the policy goals of a dominant social group. Roberto Mangabeira Unger and other authors in the movement contrast critical legal studies as a method, critical in approach, from the impersonal purposes and principles made necessary in legal reasoning, such as formalism. He writes that it was "consequently also by rejecting judges as the chief addressees of legal analysis, and refusing to take the question—how should judges decide cases?—as the defining problem in jurisprudence." According to Unger the new American legal analysis will unlock the democratic


id: jurisprudence-20260505-7
file: data/en.wikipedia.org/wiki/Jurisprudence-7.md
category: reference
title: Jurisprudence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jurisprudence
date_saved: 2026-05-05T06:39:20.330338+00:00
instance: kb-cron
parent: jurisprudence-20260505-0
summary:
John Rawls was an American philosopher; a professor of political philosophy at Harvard University; and author of A Theory of Justice (1971), Political Liberalism, Justice as Fairness: A Restatement, and The Law of Peoples. He is widely considered one of the most important English-language political philosophers of the 20th century. His theory of justice uses a method called "original position" to ask us which principles of justice we would choose to regulate the basic institutions of our society if we were behind a "veil of ignorance". Imagine we do not know who we are—our race, sex, wealth, status, class, or any distinguishing feature—so that we would not be biased in our own favour. Rawls argued from this "original position" that we would choose the same political liberties for everyone, like freedom of speech, the right to vote, and so on. Also, we would choose a system with only inequality because it provides sufficient incentives for the economic well-being of all of society, especially the poorest. This is Rawls's famous "difference principle". Justice is fairness, in the sense that the fairness of the original position of choice guarantees the fairness of the principles chosen in that position. There are many other


id: macro-social-work-20260505-0
file: data/en.wikipedia.org/wiki/Macro_social_work-0.md
category: reference
title: Macro social work
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Macro_social_work
date_saved: 2026-05-05T06:39:21.705429+00:00
instance: kb-cron
parent: macro-social-work-20260505-0
summary:
Macro social work is the use of social work skills training and perspective to produce large scale social change or social justice of some kind. Unlike micro or mezzo social work, which deals with individual and small group issues, macro social work aims to address societal problems at their roots; however, it has recently not received the same level of importance. == Professional roles and functions == Macro social workers work in a variety of public institutions, including legislative (as elected officials and advocates), executive (as administrators, managers, researchers and experts) or judicial (as expert witnesses in courts) on federal, state, or local level. Macro social workers are also found in the private sector usually in executive positions in their respective organizations. Historically, social work included both micro and macro practice, but there has been a generational trend towards micro practice, which focuses on therapeutic work with people, families, and groups. == Scope and importance == Macro social work is a branch of social work that focuses on large-scale social processes and systemic issues. It involves interventions and strategies that target communities, organizations, and policy to promote social change, justice, and the well-being of populations. Macro social work encompasses a


id: macro-social-work-20260505-1
file: data/en.wikipedia.org/wiki/Macro_social_work-1.md
category: reference
title: Macro social work
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Macro_social_work
date_saved: 2026-05-05T06:39:21.705429+00:00
instance: kb-cron
parent: macro-social-work-20260505-0
summary:
At the community level, it involves planning and policy development, community organizing, and development strategies such as community economic and social development, focusing on participation, empowerment, and leadership. Organizational level methods include building and managing nonprofit agencies and social enterprises, emphasizing social entrepreneurship and effective social administration, including program planning and organizational change. At the societal level, macro social workers engage in advocacy and social action, using policy advocacy and social movements to drive systemic change and promote social justice. Globally, they address issues through international social work, collaborating with NGOs to tackle poverty, human rights, and refugee crises, and supporting global advocacy efforts for sustainable development. These methods integrate traditional and innovative approaches to empower communities, foster leadership, and achieve equitable social change. Today, macro social work is a dynamic field that adapts to contemporary social issues. Practitioners often employ a variety of strategies to achieve their goals, including community organizing, which involves mobilizing community members to take collective action on issues affecting them. Policy practice is another critical area, where social workers engage in the analysis, development, and implementation of social policies. == Future Directions in macro social work == Macro social work plays a vital role in


id: methodological-nationalism-20260505-0
file: data/en.wikipedia.org/wiki/Methodological_nationalism-0.md
category: reference
title: Methodological nationalism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Methodological_nationalism
date_saved: 2026-05-05T06:39:23.020865+00:00
instance: kb-cron
parent: methodological-nationalism-20260505-0
summary:
In social science, methodological nationalism is an intellectual orientation and pattern in scholarly research that conceives of the nation-state as the sole unit of analysis or as a container for social processes. This concept has largely been developed by Andreas Wimmer and Nina Glick Schiller, who specifically define it as "the assumption that the nation/state/society is the natural social and political form of the modern world". Methodological nationalism has been identified in many social science subfields, such as anthropology, sociology, and the interdisciplinary field of migration studies. Methodological nationalism, as a practice within social science, has been further critiqued by scholars such as Saskia Sassen, who contends that the nation-state and its borders are an insufficient unit of analysis and that the national is at times the "terrains of the global". == Three types of methodological nationalism == According to Andreas Wimmer and Nina Glick Schiller, there are three types of methodological nationalism in social science scholarship: ignoring or disregarding the importance of nationalism for modern societies, naturalization, and confining studies to geopolitical boundaries of a particular nation-state. These types may co-occur or occur separately. When they co-occur, they may reinforce each other. Speranta Dumitru distinguished between other three different


id: mobilities-20260505-0
file: data/en.wikipedia.org/wiki/Mobilities-0.md
category: reference
title: Mobilities
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mobilities
date_saved: 2026-05-05T06:39:24.670542+00:00
instance: kb-cron
parent: mobilities-20260505-0
summary:
Mobilities is a contemporary paradigm in the social sciences that explores the movement of people (human migration, individual mobility, travel, transport), ideas (see e.g. meme) and things (transport), as well as the broader social implications of those movements. Mobility can also be thought as the movement of people through social classes, social mobility or income, income mobility. A mobility "turn" (or transformation) in the social sciences began in the 1990s in response to the increasing realization of the historic and contemporary importance of movement on individuals and society. This turn has been driven by generally increased levels of mobility and new forms of mobility where bodies combine with information and different patterns of mobility. The mobilities paradigm incorporates new ways of theorizing about how these mobilities lie "at the center of constellations of power, the creation of identities and the microgeographies of everyday life." (Cresswell, 2011, 551) The mobility turn arose as a response to the way in which the social sciences had traditionally been static, seeing movement as a black box and ignoring or trivializing "the importance of the systematic movements of people for work and family life, for leisure and pleasure, and for politics and protest" (Sheller and


id: mobilities-20260505-1
file: data/en.wikipedia.org/wiki/Mobilities-1.md
category: reference
title: Mobilities
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mobilities
date_saved: 2026-05-05T06:39:24.670542+00:00
instance: kb-cron
parent: mobilities-20260505-0
summary:
== Origins == Sheller and Urry (2006, 215) place mobilities in the sociological tradition by defining the primordial theorist of mobilities as Georg Simmel (1858–1918). Simmel's essays, "Bridge and Door" (Simmel, 1909 / 1994) and "The Metropolis and Mental Life" (Simmel, 1903 / 2001) identify a uniquely human will to connection, as well as the urban demands of tempo and precision that are satisfied with mobility. The more immediate precursors of contemporary mobilities research emerged in the 1990s (Cresswell 2011, 551). Historian James Clifford (1997) advocated for a shift from deep analysis of particular places to the routes connecting them. Marc Augé (1995) considered the philosophical potential of an anthropology of "non-places" like airports and motorways that are characterized by constant transition and temporality. Sociologist Manuel Castells outlined a "network society" and suggested that the "space of places" is being surpassed by a "space of flows." Feminist scholar Caren Kaplan (1996) explored questions about the gendering of metaphors of travel in social and cultural theory. The contemporary paradigm under the moniker "mobilities" appears to originate with the work of sociologist John Urry. In his book, Sociology Beyond Societies: Mobilities for the Twenty-First Century, Urry (2000, 1) presents a "manifesto for


id: mobilities-20260505-2
file: data/en.wikipedia.org/wiki/Mobilities-2.md
category: reference
title: Mobilities
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mobilities
date_saved: 2026-05-05T06:39:24.670542+00:00
instance: kb-cron
parent: mobilities-20260505-0
summary:
Mobilities often links science and social science to the humanities. Mobilities often links across different scales of movement, while traditional transportation geography tends to focus on particular forms of movement at only one scale (such as local traffic studies or household travel surveys). Mobilities encompasses the movement of people, objects, and ideas, rather than narrowly focusing on areas like passenger modal shift or freight logistics. Mobilities considers both motion and "stopping, stillness and relative immobility." Mobilities incorporates mobile theorization and methodologies to avoid the privileging of "notions of boundedness and the sedentary." Mobilities often embraces the political and differential politics of mobility, as opposed to the apolitical, "objective" stance often sought by researchers associated with engineering disciplines Mobilities can be seen as a postmodern descendant of modernist transportation studies, with the influence of the spatial turn corresponding to a "post-structuralist agnosticism about both naturalistic and universal explanations and about single-voiced historical narratives, and to the concomitant recognition that position and context are centrally and inescapably implicated in all constructions of knowledge" (Cosgrove, 1999, 7; Warf and Arias, 2009). Despite these ontological and epistemological differences, Shaw and Hesse (2010, 207) have argued that mobilities and transport geography represent points on a


id: modern-studies-20260505-0
file: data/en.wikipedia.org/wiki/Modern_Studies-0.md
category: reference
title: Modern Studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Modern_Studies
date_saved: 2026-05-05T06:39:25.848843+00:00
instance: kb-cron
parent: modern-studies-20260505-0
summary:
Modern Studies is a subject in the Scottish school system, currently taught at National 3 through Advanced Higher. It concerns contemporary social and political issues, and political processes, in Scottish, UK and international contexts. The subject has been taught in Scotland since the 1960s and is credited with increasing students' political literacy. == References ==


id: nudity-20260505-0
file: data/en.wikipedia.org/wiki/Nudity-0.md
category: reference
title: Nudity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nudity
date_saved: 2026-05-05T06:39:27.176954+00:00
instance: kb-cron
parent: nudity-20260505-0
summary:
Nudity is the state of being in which a human is without clothing. While estimates vary, for the first 90,000 years of pre-history, anatomically modern humans were naked, having lost their body hair, living in hospitable climates, and not having developed the crafts needed to make clothing. As humans became behaviorally modern, body adornments such as jewelry, tattoos, body paint and scarification became part of non-verbal communications, indicating a person's social and individual characteristics. Indigenous peoples in warm climates used clothing for decorative, symbolic or ceremonial purposes but were often nude, having neither the need to protect the body from the elements nor any conception of nakedness being shameful. In many societies, both ancient and contemporary, children might be naked until the beginning of puberty and women often do not cover their breasts due to the association with nursing babies more than with sexuality. In the ancient civilizations of the Mediterranean, from Mesopotamia to the Roman Empire, proper attire was required to maintain social standing. The majority might possess a single piece of cloth that was wrapped or tied to cover the lower body; slaves might be naked. However, through much of Western history until the modern era, people of


id: nudity-20260505-1
file: data/en.wikipedia.org/wiki/Nudity-1.md
category: reference
title: Nudity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nudity
date_saved: 2026-05-05T06:39:27.176954+00:00
instance: kb-cron
parent: nudity-20260505-0
summary:
The habitual use of clothing is one of the changes that mark the end of the Neolithic and the beginning of civilization, between 7,000 and 9,000 years ago. Much of what is known about the early history of clothing is from depictions of the higher classes, there being few surviving artifacts. Everyday behaviors are rarely represented in historical records. Clothing and adornment became part of the symbolic communication that marked a person's membership in their society, thus nakedness meant being at the bottom of the social scale, lacking in dignity and status. In each culture, ornamentation represented the wearer's place in society; position of authority, economic class, gender role, and marital status. From the beginning of civilization, there was ambiguity regarding everyday nakedness and the nudity in depictions of deities and heroes indicating positive meanings of the unclothed body. Among ancient civilizations only Abrahamic societies associated nakedness primarily with sin or shame regarding sexuality. === Ancient and classical history === For millennia from Mesopotamia to the Middle Kingdom of Egypt the majority of men and women wore a cloth wrapped or tied to cover the lower part of the body. Both men and women would be bare-chested and barefoot. Complete


id: nudity-20260505-2
file: data/en.wikipedia.org/wiki/Nudity-2.md
category: reference
title: Nudity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nudity
date_saved: 2026-05-05T06:39:27.176954+00:00
instance: kb-cron
parent: nudity-20260505-0
summary:
The revival of pre-colonial culture is asserted in the adoption of traditional dress—young women wearing only beaded skirts and jewelry—in the Umkhosi Womhlanga (Reed Dance) by the Zulu and Swazi. Other examples of cultural tourism reflect the visitor's desire to experience what they imagine being an exotic culture, which includes nudity. === Asia === In Asian countries, rather than always being immoral or shameful, not being properly dressed is perceived as a breach of etiquette (loss of face) in most situations, while nakedness may be part of maintaining purity by public bathing, or expressing rejection of worldliness including clothes. ==== China ==== In contemporary China, while maintaining the traditions of modest dress in everyday life, the use of nudity in magazine advertising indicates the effect of globalization. In much of Asia, traditional dress covers the entire body, similar to Western dress. In stories written in China as early as the fourth century BCE, nudity is presented as an affront to human dignity, reflecting the belief that "humanness" in Chinese society is not innate, but is earned by correct behavior. However, nakedness could also be used by an individual to express contempt for others in their presence. In other stories, the


id: nudity-20260505-3
file: data/en.wikipedia.org/wiki/Nudity-3.md
category: reference
title: Nudity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nudity
date_saved: 2026-05-05T06:39:27.176954+00:00
instance: kb-cron
parent: nudity-20260505-0
summary:
=== Western societies === The Western world inherited contradictory cultural traditions relating to nudity in various contexts. The first tradition came from the ancient Greeks, who saw the naked body as the natural state and as essentially positive. The second is based upon the Abrahamic religions—Judaism, Christianity, and Islam—which view being naked as shameful and essentially negative. The interaction between the Greek classical and later Abrahamic traditions has resulted in Western ambivalence, with nudity acquiring both positive and negative meanings in individual psychology, in social life, and in depictions such as art. The conservative versions of these religions continue to prohibit public and sometimes also private nudity. While public modesty prevails in more recent times, organized groups of nudists or naturists emerged with the stated purpose of regaining a natural connection to the human body and nature, sometimes in private spaces but also in public. Naturism in the United States, meanwhile, remains largely confined to private facilities, with few "clothing optional" public spaces compared to Europe. In spite of the liberalization of attitudes toward sex, Americans remain uncomfortable with complete nudity at the end of the 20th century. A poll in 2025 found that Americans are divided: 25% saying they


id: nudity-20260505-4
file: data/en.wikipedia.org/wiki/Nudity-4.md
category: reference
title: Nudity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nudity
date_saved: 2026-05-05T06:39:27.176954+00:00
instance: kb-cron
parent: nudity-20260505-0
summary:
==== Christianity ==== The meaning of nudity for early Christians was the baptism, which was originally by full immersion and without clothes in a basin attached to every cathedral. Both men and women were baptized naked, deaconesses performing the rite for women to maintain modesty. Until the fifth century CE, pagan nudity was associated with sex, Christian nudity with grace. Jesus was originally depicted nude as would have been the case in Roman crucifixions, but the Christian adoption of the pagan association of the body with sex prompted the clothing of Christ. Some clerics went further, to hatred and fear of the body, insisting that monks sleep fully dressed. Christian theology rarely addresses nudity, but rather proper dress and modesty. Western cultures adopted Greek heritage only with regard to art, the ideal nude. Real naked people remained shameful, and become human only when they cover their nakedness. In one of a series of lectures entitled "Theology of the Body" given in 1979, Pope John Paul II said that the innocent nudity of being before the fall is regained only between loving spouses. In daily life, Christianity requires clothing in public, but with great variation between and within societies as to


id: nudity-20260505-5
file: data/en.wikipedia.org/wiki/Nudity-5.md
category: reference
title: Nudity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nudity
date_saved: 2026-05-05T06:39:27.176954+00:00
instance: kb-cron
parent: nudity-20260505-0
summary:
Historically, men and boys bathed and swam nude in secluded rivers and lakes. In England when sea bathing became popular in the 18th century, beaches were initially male only, but with the easier access of the 19th century due to rail transportation, the mixing of genders became a problem for authorities. The addition of "bathing machines" at seaside resorts was not successful in maintaining standards of decency, men often continuing to be nude while women wore bathing costumes. However, public concern was only regarding adults, it being generally accepted that boys at English beaches would be nude. This prompted complaints by visiting Americans, but Englishmen had no objection to their daughters being fully dressed on the beach with naked boys. In the United States and other Western countries for much of the 20th century, male nudity was the norm in gender segregated activities including summer camps, swimming pools and communal showers based on cultural beliefs that females need more privacy than males. Beginning in 1900, businessmen swam nude at private athletic clubs in New York City, which ended with a 1980 law requiring the admission of women. For younger boys, lack of modesty might include public behavior as in 1909


