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source: "https://en.wikipedia.org/wiki/1970s_in_science_and_technology"
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category: "reference"
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source: "https://en.wikipedia.org/wiki/1980s_in_science_and_technology"
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source: "https://en.wikipedia.org/wiki/1990s_in_science_and_technology"
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source: "https://en.wikipedia.org/wiki/1990s_in_science_and_technology"
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source: "https://en.wikipedia.org/wiki/2000s_in_science_and_technology"
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source: "https://en.wikipedia.org/wiki/2010s_in_science_and_technology"
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category: "reference"
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source: "https://en.wikipedia.org/wiki/2020s_in_science_and_technology"
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source: "https://en.wikipedia.org/wiki/500_Women_Scientists"
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23
data/en.wikipedia.org/wiki/A_Science_on_the_Scales-0.md
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title: "A Science on the Scales"
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source: "https://en.wikipedia.org/wiki/A_Science_on_the_Scales"
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A Science on the Scales: The Rise of Canadian Atlantic Fisheries Biology, 1898-1939 is a 2011 book by Jennifer Hubbard. The book provides an analysis of Canadian fisheries history with the tools of the professional historian, when most earlier works on the topic came from fisheries scientists themselves.
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The book traces the development of fisheries science in Canada in the first decades of the twentieth century. Fisheries biology arose in the mid-1800s in Northern Europe from concerns around the conservation of what appeared to be dwindling stocks in the face of new technology. Marine biological stations were established in Europe in the last decades of the nineteenth century—particularly in Naples (the Stazione Zoologica Anton Dohrn) and Plymouth—but the Canadian Department of Marine and Fisheries, established in 1892, did not see a need for one in Canada, though a floating station on board a scow was eventually set up.
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The book "surveys the tortuous process by which the Biological Board of Canada was established in 1912 to supersede a board of management that had overseen a floating biological station off the Atlantic coast since 1898, and permanent stations at St. Andrews, New Brunswick, and Nanaimo, British Columbia, since 1908." Norwegian zoologist Johan Hjort and American ichthyologist David Starr Jordan were both consulted on Canadian fisheries issues in the early period.
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The Biological Board eventually became the Fisheries Research Board, but Hubbard explains that "its activities [were] perennially challenged and eventually usurped by federal departments" while constantly hampered by conflicting priorities among its federal funders and the academic biologists who volunteered as its staff. The book also explores the conflicts between the academic knowledge of biologists and the local and craft knowledge of fishers.
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While the main focus of the book—the establishment of the structural components of Canadian fisheries biology—ends around 1940, its importance comes in part from its explication of the historical structure of Canadian fisheries science just a decade after the catastrophic collapse of the Atlantic northwest cod fishery in the 1990s, which the book engages in an extended epilogue. The book's explanation of "the complex tensions that result when a problem places science, business, government, and environment at potential cross-purposes" thus provide useful background for understanding the collapse.
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In its analysis of the background of collapse, the book is part of a growing effort in the early twenty-first century to historicize the ocean, and particularly fisheries, following marine biologist Daniel Pauly's identification of the "shifting baseline" concept in the measurement of fish populations over time.
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== See also ==
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All the Fish in the Sea: Maximum Sustainable Yield and the Failure of Fisheries Management by Carmel Finley
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== References ==
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source: "https://en.wikipedia.org/wiki/AcademiaNet"
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source: "https://en.wikipedia.org/wiki/Academic_imperialism"
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source: "https://en.wikipedia.org/wiki/Actor–network_theory"
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source: "https://en.wikipedia.org/wiki/Advanced_Innovation_Design_Approach"
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Advanced Innovation Design Approach (AIDA) is a holistic approach for enhancing the innovative and competitive capabilities of industrial companies. The name Advanced Innovation Design Approach (AIDA) was proposed in the research project "Innovation Process 4.0" run at the University of Applied Sciences Offenburg, Germany in co-operation with 10 German industrial companies in 2015–2019.
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AIDA can be considered as a pioneering mindset, an individually adaptable range of strong innovation techniques such as comprehensive front-end innovation process, advanced innovation methods, best tools and methods of the theory of inventive problem solving TRIZ, organisational measures for accelerating innovation, IT-solutions for Computer-Aided Innovation, and other tools for new product development, elaborated in the recent decade in the industry and academia.
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Initially the AIDA has been conceptualised as a systemic approach including analysis, optimizations and further development of the innovation process and promoting the innovation climate in industrial companies. The innovation process with self-configuration, self-optimization, self-diagnostics and intelligent information processing and communication, is understood as a holistic system comprising following typical phases with feedback loops and simultaneous auxiliary or follow-up processes: uncovering of solution-neutral customer needs, technology and market trends, identification of the needs and problems with high market potential and formulation of the innovation tasks and strategy, systematic idea generation and problem solving, evaluation and enhancement of solution ideas, creation of innovation concepts based on solution ideas, evaluation of the innovation concepts as well as implementation, validation and market launch of chosen innovation concepts.
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The Advanced Innovation Design Approach was refined and further developed for the application in the field of process engineering in the context of the EU research project "Intensified by Design - Platform for the intensification of processes involving solids handling” within international consortium of 22 universities, research institutes and industrial companies under H2020 SPIRE programme. In 2020 the European Commission has placed AIDA on its Innovation Radar as innovation with the high market potential.
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== Principle of completeness ==
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As a holistic innovation approach, AIDA postulates the complete problem analysis and a comprehensive idea generation and problem solving. The problems faced by the industry can not be solved by single eureka idea.
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The principle of completeness in the new product development can be illustrated by following 4 steps.
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Initial complex problem must be segmented into the partial problems. The problem ranking method helps to identify problems crucial for innovation success.
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The strongest TRIZ inventive principles replace the random brainstorming, increasing the quality and quantity of ideas within a short period of time. For each partial problem several ideas must be generated. No relevant idea should be overlooked or lost.
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The complementary solution ideas are combined to the solution concepts. A robust solution concept delivers solutions for all partial problems.
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The solution concepts often have their secondary side effects, like costs, risks or R&D expenditures, which must be limited through concept optimization.
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Another example demonstrates the principle of completeness in the phase of the innovation strategy formulation. For the complete identification of existing and future customer needs or benefits several complementary methods are used simultaneously (Tool 4. Innovation potential analysis):
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Voice-of-the-Customer Methods, e.g. Lead User identification or web-based monitoring.
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Analysis of the customer working process (Process Mapping)
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Prediction of the customer needs.
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Analysis of system functions, and Identification of the new product features and innovation tasks from the patent literature.
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Analysis of market and technological trends, and others.
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== AIDA innovation tools ==
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AIDA tools or apps most frequently used in the practice include:
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Brainstorming 40x40: Generate 40 ideas with enhanced 40 TRIZ Inventive Principles (incl. 160 inventive sub-principles, 2017).
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Inno-Workshop: Tool for systematic problem solving and moderation of innovation workshops with TRIZ.
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TRIZ Inventor: Solving of bottle-neck problems with inventive algorithm ARIZ in its short form.
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Innovation potential analysis: comprehensive identification of innovation opportunities, customer benefits and segments with high market potential.
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New concept development: implementation of the selected innovation tasks (tool 04) into new concepts with high market potential.
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Root-conflict analysis and anticipatory failure identification: tool for elimination harmful effects.
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Systematic and creative cost cutting: for products and processes.
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InnoMonitor: tool for continuous monitoring of innovative capability of companies (80 parameters and 10 key performance indicators).
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Database of 200+ best practice measures for enhancement of innovation capability.
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Rapid Cross Industry Innovation: an easy-to-use method for fast idea generation with the help of analogies and similarity rules (2019).
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AIDA Automatic Idea & IP Generator: a new app for fast and complete automatic idea generation based on 200 inventive principles (2020-23), tuned for applying ChatGPT or other GAI-tools.
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== Advanced Innovation Design Methods ==
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The new Advanced Innovation Methods are the basis for the further development of the AIDA-tools or apps. The following list will be regularly updated:
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Advanced Design (2014) - Methods for the early stage of the innovation process, proposed by the research team of the Politecnico di Milano, Italy.
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Advanced design methods for successful innovation (2013) - new methods in the industrial design from the Dutch research platform Design United, Delft University of Technology
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Root Conflict Analysis RCA+ (2011) - universal method for comprehensive problem and contradiction analysis.
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The Business Model Navigator (2014) - engineering method for systematic business model innovation, containing 55 business models for creative copying and recombination. University of St. Gallen.
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New Product Blueprinting (2012) - The Advanced Innovation and Marketing (AIM) Institute.
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== References ==
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instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
16
data/en.wikipedia.org/wiki/All_the_Fish_in_the_Sea-0.md
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16
data/en.wikipedia.org/wiki/All_the_Fish_in_the_Sea-0.md
Normal file
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|
||||
---
|
||||
title: "All the Fish in the Sea"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/All_the_Fish_in_the_Sea"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:10:35.739874+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
All the Fish in the Sea: Maximum Sustainable Yield and the Failure of Fisheries Management is a 2011 book by Carmel Finley. The book argues that the policies for international fishing and whaling management were essentially locked in place by 1958, and that the United States played a large role in setting them. In the development of the international law covering fisheries, the US supported laws that would protect the US tuna and salmon fisheries while limiting the ability of other nations, and Japan in particular, to fish in US waters. The book thus ties fisheries management inseparably with Cold War politics.
|
||||
In particular, Finley traces the development of the concept of maximum sustainable yield (MSY), arguing that MSY had no scientific basis and thus was a political and economic construct more than a scientific one. The "model did not represent the codification of quantitative, empirical evidence." Once instituted, instead of limiting fishing, MSY's assertion that underfishing wasted oceanic resources meant MSY "was not really a limit, but a goal to be reached," thus encouraging more fishing rather than less. The book engages the myth of the "Tragedy of the Commons" by demonstrating that governmental action and international policy led to overfishing, and not the self-interested actions of individual fishers.
|
||||
Finley argues that to achieve a sustainable future for fisheries, "we need to change the focus of management from estimating harvest to maintaining the population structure of fish stocks and their ecosystems." The book is also important as part of a movement to understand the oceans as a place with a history, rather than an unchanging void around which human history happens.
|
||||
|
||||
|
||||
== References ==
|
||||
40
data/en.wikipedia.org/wiki/Andrew_B._Raupp-0.md
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40
data/en.wikipedia.org/wiki/Andrew_B._Raupp-0.md
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|
||||
---
|
||||
title: "Andrew B. Raupp"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Andrew_B._Raupp"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:10:52.274907+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Andrew B. Raupp (born in Detroit, Michigan) is an American entrepreneur, educator, and leading advocate for science, technology, engineering, and mathematics (STEM) education. He is the founder and executive director of STEM.org Educational Research, a pioneering organization at the forefront of STEM research and quality assurance. Raupp is known for his groundbreaking work in establishing a decentralized framework for validating STEM credentials, leveraging blockchain technology's immutable and transparent characteristics. This initiative underscores his dedication to enhancing accountability and democratizing access to STEM education standards, aligning with the broader decentralization principles central to blockchain and Web3 innovations.
|
||||
Raupp's scholarly contributions extend to the comprehensive documentation and analysis of the STEM education movement's evolution. His research is considered one of the most authoritative and comprehensive chronologies in the field. It has been cited by institutions, including the Smithsonian Science Education Center, which reinforces his status as a significant contributor to 21st-century STEM education.
|
||||
|
||||
|
||||
== Early life and career ==
|
||||
Andrew B. Raupp was born and raised in the United States and also holds citizenship in the Republic of Malta. He attended Divine Child High School in Dearborn, Michigan, graduating in 1998. From 1998 to 2000, he studied microbiology at Wayne State University before earning both an Associate of Science and an Associate of Arts degree from Henry Ford College in 2002. He completed a Bachelor of Science degree at the University of Michigan–Dearborn in 2006.
|
||||
In 2001, Raupp launched Initiative Science, a community outreach project serving youth in southwest Detroit. The initiative later grew into STEM.org Educational Research™, an organization that has expanded its educational quality-assurance network to more than eighty countries. In 2005, he contributed to the development of the framework for the first STEM Congressional Caucus at the request of Congressman Vern Ehlers.
|
||||
Raupp continued his professional development through national fellowship programs. In 2013, he was selected as a Fellow in the Emerging Leaders Program at the Harvard Kennedy School. He later joined the Education Policy Fellowship Program at Michigan State University during the 2017–2018 academic year.
|
||||
In 2017, Raupp was appointed the inaugural Chairman of the EdTech and FinTech subcommittees within the Forbes Technology Council and helped establish the Newsweek Expert Forum. That same year, his K-12 STEM school research appeared in a Newsweek cover feature and authored a widely circulated Forbes article examining blockchain technology and its role in emerging innovation frameworks.
|
||||
He is also an editorial contributor for Fast Company and other national outlets.
|
||||
|
||||
|
||||
== Selected articles ==
|
||||
A STEM state of mind: No magic kit or subscription required
|
||||
Competition versus collaboration in STEM education
|
||||
How Advancements In AI Could Radically Change The Way Children Learn In The Classroom
|
||||
Insights Into Early STEM Learning
|
||||
The Rise Of The STEM Toy
|
||||
Ethics in STEM Education: Going Beyond the Classroom
|
||||
"Know Thyself": A Socratic Approach to Modern STEM Education
|
||||
Safeguarding Ideas In The Age Of Digital Censorship: Lessons From The Gutenberg Revolution
|
||||
Beyond the "Fourth Industrial Revolution": Why We Must Invest in STEM Education
|
||||
|
||||
|
||||
== Awards and recognition ==
|
||||
Service to Education Award by the Qatari Supreme Education Council
|
||||
Emerging Leader by the John F. Kennedy School of Government at Harvard University
|
||||
|
||||
|
||||
== References ==
|
||||
@ -4,7 +4,7 @@ chunk: 1/2
|
||||
source: "https://en.wikipedia.org/wiki/Applied_epistemology"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:36:13.103463+00:00"
|
||||
date_saved: "2026-05-05T07:07:41.742535+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 2/2
|
||||
source: "https://en.wikipedia.org/wiki/Applied_epistemology"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:36:13.103463+00:00"
|
||||
date_saved: "2026-05-05T07:07:41.742535+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Astronaut_for_a_Day"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:55:38.229225+00:00"
|
||||
date_saved: "2026-05-05T07:10:59.026905+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 1/2
|
||||
source: "https://en.wikipedia.org/wiki/Athena_SWAN"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:38:26.970905+00:00"
|
||||
date_saved: "2026-05-05T07:09:17.866164+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 2/2
|
||||
source: "https://en.wikipedia.org/wiki/Athena_SWAN"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:38:26.970905+00:00"
|
||||
date_saved: "2026-05-05T07:09:17.866164+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
66
data/en.wikipedia.org/wiki/Automated_decision-making-0.md
Normal file
66
data/en.wikipedia.org/wiki/Automated_decision-making-0.md
Normal file
@ -0,0 +1,66 @@
|
||||
---
|
||||
title: "Automated decision-making"
|
||||
chunk: 1/3
|
||||
source: "https://en.wikipedia.org/wiki/Automated_decision-making"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:00.240751+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Automated decision-making (ADM) is the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM may involve large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.
|
||||
|
||||
== Overview ==
|
||||
There are different definitions of ADM based on the level of automation involved. Some definitions suggests ADM involves decisions made through purely technological means without human input, such as the EU's General Data Protection Regulation (Article 22). However, ADM technologies and applications can take many forms ranging from decision-support systems that make recommendations for human decision-makers to act on, sometimes known as augmented intelligence or 'shared decision-making', to fully automated decision-making processes that make decisions on behalf of individuals or organizations without human involvement. Models used in automated decision-making systems can be as simple as checklists and decision trees through to artificial intelligence and deep neural networks (DNN).
|
||||
Since the 1950s computers have gone from being able to do basic processing to having the capacity to undertake complex, ambiguous and highly skilled tasks such as image and speech recognition, gameplay, scientific and medical analysis and inferencing across multiple data sources. ADM is now being increasingly deployed across all sectors of society and many diverse domains from entertainment to transport.
|
||||
An ADM system (ADMS) may involve multiple decision points, data sets, and technologies (ADMT) and may sit within a larger administrative or technical system such as a criminal justice system or business process.
|
||||
|
||||
== Data ==
|
||||
Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and financial data for public administration, medical records in health, criminal records in law. This can sometimes involve vast amounts of data and computing power.
|
||||
|
||||
=== Data quality ===
|
||||
The quality of the available data and its ability to be used in ADM systems is fundamental to the outcomes. It is often highly problematic for many reasons. Datasets are often highly variable; corporations or governments may control large-scale data, restricted for privacy or security reasons, incomplete, biased, limited in terms of time or coverage, measuring and describing terms in different ways, and many other issues.
|
||||
For machines to learn from data, large corpora are often required, which can be challenging to obtain or compute; however, where available, they have provided significant breakthroughs, for example, in diagnosing chest X-rays.
|
||||
|
||||
== ADM technologies ==
|
||||
Automated decision-making technologies (ADMT) are software-coded digital tools that automate the translation of input data to output data, contributing to the function of automated decision-making systems. There are a wide range of technologies in use across ADM applications and systems.
|
||||
ADMTs involving basic computational operations
|
||||
|
||||
Search (includes 1-2-1, 1-2-many, data matching/merge)
|
||||
Matching (two different things)
|
||||
Mathematical Calculation (formula)
|
||||
ADMTs for assessment and grouping:
|
||||
|
||||
User profiling
|
||||
Recommender systems
|
||||
Clustering
|
||||
Classification
|
||||
Feature learning
|
||||
Predictive analytics (includes forecasting)
|
||||
ADMTs relating to space and flows:
|
||||
|
||||
Social network analysis (includes link prediction)
|
||||
Mapping
|
||||
Routing
|
||||
ADMTs for processing of complex data formats
|
||||
|
||||
Image processing
|
||||
Audio processing
|
||||
Natural Language Processing (NLP)
|
||||
Other ADMT
|
||||
|
||||
Business rules management systems
|
||||
Time series analysis
|
||||
Anomaly detection
|
||||
Modelling/Simulation
|
||||
|
||||
=== Machine learning ===
|
||||
|
||||
Machine learning (ML) involves training computer programs through exposure to large data sets and examples to learn from experience and solve problems. Machine learning can be used to generate and analyse data as well as make algorithmic calculations and has been applied to image and speech recognition, translations, text, data and simulations. While machine learning has been around for some time, it is becoming increasingly powerful due to recent breakthroughs in training deep neural networks (DNNs), and dramatic increases in data storage capacity and computational power with GPU coprocessors and cloud computing.
|
||||
Machine learning systems based on foundation models run on deep neural networks and use pattern matching to train a single huge system on large amounts of general data such as text and images. Early models tended to start from scratch for each new problem however since the early 2020s many are able to be adapted to new problems. Examples of these technologies include Open AI's DALL-E (an image creation program) and their various GPT language models, and Google's PaLM language model program.
|
||||
|
||||
== Applications ==
|
||||
ADM is being used to replace or augment human decision-making by both public and private-sector organisations for a range of reasons including to help increase consistency, improve efficiency, reduce costs and enable new solutions to complex problems.
|
||||
|
||||
=== Debate ===
|
||||
Research and development are underway into uses of technology to assess argument quality, assess argumentative essays and judge debates. Potential applications of these argument technologies span education and society. Scenarios to consider, in these regards, include those involving the assessment and evaluation of conversational, mathematical, scientific, interpretive, legal, and political argumentation and debate.
|
||||
33
data/en.wikipedia.org/wiki/Automated_decision-making-1.md
Normal file
33
data/en.wikipedia.org/wiki/Automated_decision-making-1.md
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|
||||
---
|
||||
title: "Automated decision-making"
|
||||
chunk: 2/3
|
||||
source: "https://en.wikipedia.org/wiki/Automated_decision-making"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:00.240751+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
=== Law ===
|
||||
In legal systems around the world, algorithmic tools such as risk assessment instruments (RAI), are being used to supplement or replace the human judgment of judges, civil servants and police officers in many contexts. In the United States RAI are being used to generate scores to predict the risk of recidivism in pre-trial detention and sentencing decisions, evaluate parole for prisoners and to predict "hot spots" for future crime. These scores may result in automatic effects or may be used to inform decisions made by officials within the justice system. In Canada ADM has been used since 2014 to automate certain activities conducted by immigration officials and to support the evaluation of some immigrant and visitor applications.
|
||||
|
||||
=== Economics ===
|
||||
Automated decision-making systems are used in certain computer programs to create buy and sell orders related to specific financial transactions and automatically submit the orders in the international markets. Computer programs can automatically generate orders based on predefined set of rules using trading strategies which are based on technical analyses, advanced statistical and mathematical computations, or inputs from other electronic sources.
|
||||
|
||||
=== Business ===
|
||||
|
||||
==== Continuous auditing ====
|
||||
Continuous auditing uses advanced analytical tools to automate auditing processes. It can be utilized in the private sector by business enterprises and in the public sector by governmental organizations and municipalities. As artificial intelligence and machine learning continue to advance, accountants and auditors may make use of increasingly sophisticated algorithms which make decisions such as those involving determining what is anomalous, whether to notify personnel, and how to prioritize those tasks assigned to personnel.
|
||||
|
||||
=== Media and entertainment ===
|
||||
Digital media, entertainment platforms, and information services increasingly provide content to audiences via automated recommender systems based on demographic information, previous selections, collaborative filtering or content-based filtering. This includes music and video platforms, publishing, health information, product databases and search engines. Many recommender systems also provide some agency to users in accepting recommendations and incorporate data-driven algorithmic feedback loops based on the actions of the system user.
|
||||
Large-scale machine learning language models and image creation programs being developed by companies such as OpenAI and Google in the 2020s have restricted access however they are likely to have widespread application in fields such as advertising, copywriting, stock imagery and graphic design as well as other fields such as journalism and law.
|
||||
|
||||
=== Advertising ===
|
||||
Online advertising is closely integrated with many digital media platforms, websites and search engines and often involves automated delivery of display advertisements in diverse formats. 'Programmatic' online advertising involves automating the sale and delivery of digital advertising on websites and platforms via software rather than direct human decision-making. This is sometimes known as the waterfall model which involves a sequence of steps across various systems and players: publishers and data management platforms, user data, ad servers and their delivery data, inventory management systems, ad traders and ad exchanges. There are various issues with this system including lack of transparency for advertisers, unverifiable metrics, lack of control over ad venues, audience tracking and privacy concerns. Internet users who dislike ads have adopted counter measures such as ad blocking technologies which allow users to automatically filter unwanted advertising from websites and some internet applications. In 2017, 24% of Australian internet users had ad blockers.
|
||||
|
||||
=== Health ===
|
||||
Deep learning AI image models are being used for reviewing x-rays and detecting the eye condition macular degeneration.
|
||||
|
||||
=== Social services ===
|
||||
Governments have been implementing digital technologies to provide more efficient administration and social services since the early 2000s, often referred to as e-government. Many governments around the world are now using automated, algorithmic systems for profiling and targeting policies and services including algorithmic policing based on risks, surveillance sorting of people such as airport screening, providing services based on risk profiles in child protection, providing employment services and governing the unemployed. A significant application of ADM in social services relates to the use of predictive analytics – eg predictions of risks to children from abuse/neglect in child protection, predictions of recidivism or crime in policing and criminal justice, predictions of welfare/tax fraud in compliance systems, predictions of long term unemployment in employment services. Historically these systems were based on standard statistical analyses, however from the early 2000s machine learning has increasingly been developed and deployed. Key issues with the use of ADM in social services include bias, fairness, accountability and explainability which refers to transparency around the reasons for a decision and the ability to explain the basis on which a machine made a decision. For example Australia's federal social security delivery agency, Centrelink, developed and implemented an automated processes for detecting and collecting debt which led to many cases of wrongful debt collection in what became known as the RoboDebt scheme.
|
||||
59
data/en.wikipedia.org/wiki/Automated_decision-making-2.md
Normal file
59
data/en.wikipedia.org/wiki/Automated_decision-making-2.md
Normal file
@ -0,0 +1,59 @@
|
||||
---
|
||||
title: "Automated decision-making"
|
||||
chunk: 3/3
|
||||
source: "https://en.wikipedia.org/wiki/Automated_decision-making"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:00.240751+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
=== Transport and mobility ===
|
||||
Connected and automated mobility (CAM) involves autonomous vehicles such as self-driving cars and other forms of transport which use automated decision-making systems to replace various aspects of human control of the vehicle. This can range from level 0 (complete human driving) to level 5 (completely autonomous). At level 5 the machine is able to make decisions to control the vehicle based on data models and geospatial mapping and real-time sensors and processing of the environment. Cars with levels 1 to 3 are already available on the market in 2021. In 2016 The German government established an 'Ethics Commission on Automated and Connected Driving' which recommended connected and automated vehicles (CAVs) be developed if the systems cause fewer accidents than human drivers (positive balance of risk). It also provided 20 ethical rules for the adaptation of automated and connected driving. In 2020 the European Commission strategy on CAMs recommended that they be adopted in Europe to reduce road fatalities and lower emissions however self-driving cars also raise many policy, security and legal issues in terms of liability and ethical decision-making in the case of accidents, as well as privacy issues. Issues of trust in autonomous vehicles and community concerns about their safety are key factors to be addressed if AVs are to be widely adopted.
|
||||
|
||||
=== Surveillance ===
|
||||
Automated digital data collections via sensors, cameras, online transactions and social media have significantly expanded the scope, scale, and goals of surveillance practices and institutions in government and commercial sectors. As a result there has been a major shift from targeted monitoring of suspects to the ability to monitor entire populations. The level of surveillance now possible as a result of automated data collection has been described as surveillance capitalism or surveillance economy to indicate the way digital media involves large-scale tracking and accumulation of data on every interaction.
|
||||
|
||||
== Ethical and legal issues ==
|
||||
There are many social, ethical and legal implications of automated decision-making systems. Concerns raised include lack of transparency and contestability of decisions, incursions on privacy and surveillance, exacerbating systemic bias and inequality due to data and algorithmic bias, intellectual property rights, the spread of misinformation via media platforms, administrative discrimination, risk and responsibility, unemployment and many others. As ADM becomes more ubiquitous there is greater need to address the ethical challenges to ensure good governance in information societies.
|
||||
ADM systems are often based on machine learning and algorithms which are not easily able to be viewed or analyzed, leading to concerns that they are 'black box' systems which are not transparent or accountable.
|
||||
A report from Citizen Lab in Canada argues for a critical human rights analysis of the application of ADM in various areas to ensure the use of automated decision-making does not result in infringements on rights, including the rights to equality and non-discrimination; freedom of movement, expression, religion, and association; privacy rights and the rights to life, liberty, and security of the person.
|
||||
Legislative responses to ADM include:
|
||||
|
||||
The European General Data Protection Regulation (GDPR), introduced in 2016, is a regulation in EU law on data protection and privacy in the European Union (EU). Article 22(1) enshrines the right of data subjects not to be subject to decisions, which have legal or other significant effects, being based solely on automatic individual decision making. GDPR also includes some rules on the right to explanation however the exact scope and nature of these is currently subject to pending review by the Court of Justice of the European Union. These provisions were not first introduced in the GDPR, but have been present in a similar form across Europe since the Data Protection Directive in 1995, and the 1978 French law, the loi informatique et libertés. Similarly scoped and worded provisions with varying attached rights and obligations are present in the data protection laws of many other jurisdictions across the world, including Uganda, Morocco and the US state of Virginia.
|
||||
Rights for the explanation of public sector automated decisions forming 'algorithmic treatment' under the French loi pour une République numérique.
|
||||
|
||||
=== Bias ===
|
||||
ADM may incorporate algorithmic bias arising from:
|
||||
|
||||
Data sources, where data inputs are biased in their collection or selection
|
||||
Technical design of the algorithm, for example where assumptions have been made about how a person will behave
|
||||
Emergent bias, where the application of ADM in unanticipated circumstances creates a biased outcome
|
||||
|
||||
=== Explainability ===
|
||||
Questions of biased or incorrect data or algorithms and concerns that some ADMs are black box technologies, closed to human scrutiny or interrogation, has led to what is referred to as the issue of explainability, or the right to an explanation of automated decisions and AI. This is also known as Explainable AI (XAI), or Interpretable AI, in which the results of the solution can be analysed and understood by humans. XAI algorithms are considered to follow three principles - transparency, interpretability and explainability.
|
||||
|
||||
=== Information asymmetry ===
|
||||
Automated decision-making may increase the information asymmetry between individuals whose data feeds into the system and the platforms and decision-making systems capable of inferring information from that data. On the other hand it has been observed that in financial trading the information asymmetry between two artificial intelligent agents may be much less than between two human agents or between human and machine agents. A research validated Daniel Kahneman's theory on noisy decisions by human experts in finance. It demonstrates the inherent inconsistencies in human judgments, which consequently affect the outcomes of automated decisions made by AI decision-support systems.
|
||||
|
||||
== Research fields ==
|
||||
Many academic disciplines and fields are increasingly turning their attention to the development, application and implications of ADM including business, computer sciences, human computer interaction (HCI), law, public administration, and media and communications. The automation of media content and algorithmically driven news, video and other content via search systems and platforms is a major focus of academic research in media studies.
|
||||
The ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) was established in 2018 to study transparency and explainability in the context of socio-technical systems, many of which include ADM and AI.
|
||||
Key research centres investigating ADM include:
|
||||
|
||||
Algorithm Watch, Germany
|
||||
ARC Centre of Excellence for Automated Decision-Making and Society, Australia
|
||||
Citizen Lab, Canada
|
||||
Informatics Europe
|
||||
|
||||
== See also ==
|
||||
Automated decision support
|
||||
Algorithmic bias
|
||||
Decision-making software
|
||||
Decision Management
|
||||
Ethics of artificial intelligence
|
||||
Government by algorithm
|
||||
Machine learning
|
||||
Recommender systems
|
||||
|
||||
== References ==
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Balfour_Biological_Laboratory_for_Women"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:38:28.178724+00:00"
|
||||
date_saved: "2026-05-05T07:09:19.187218+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Bank_of_Sweden_Tercentenary_Foundation"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:35:02.655614+00:00"
|
||||
date_saved: "2026-05-05T07:06:50.110061+00:00"
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||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Bathurst_studentship"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:38:29.360126+00:00"
|
||||
date_saved: "2026-05-05T07:09:20.508736+00:00"
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||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Benoît_Godin"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:36:44.616227+00:00"
|
||||
date_saved: "2026-05-05T07:08:03.923772+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Betz_mystery_sphere"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:39:45.358913+00:00"
|
||||
date_saved: "2026-05-05T07:10:17.321825+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Beyond_Curie"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:38:30.629931+00:00"
|
||||
date_saved: "2026-05-05T07:09:21.844711+00:00"
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||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Biology_and_political_science"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:36:14.832004+00:00"
|
||||
date_saved: "2026-05-05T07:07:42.979382+00:00"
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||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Bold_hypothesis"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:36:16.007133+00:00"
|
||||
date_saved: "2026-05-05T07:07:44.231722+00:00"
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||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Bold_hypothesis"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:36:16.007133+00:00"
|
||||
date_saved: "2026-05-05T07:07:44.231722+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Bold_hypothesis"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:36:16.007133+00:00"
|
||||
date_saved: "2026-05-05T07:07:44.231722+00:00"
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||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Bold_hypothesis"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:36:16.007133+00:00"
|
||||
date_saved: "2026-05-05T07:07:44.231722+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Boundary-work"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:36:22.248543+00:00"
|
||||
date_saved: "2026-05-05T07:07:45.435606+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Boundary_object"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:35:21.317357+00:00"
|
||||
date_saved: "2026-05-05T07:07:04.153145+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Burden_of_knowledge"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:26:16.955166+00:00"
|
||||
date_saved: "2026-05-05T07:11:01.486346+00:00"
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||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/CERN_ritual_hoax"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:04:42.637712+00:00"
|
||||
date_saved: "2026-05-05T07:10:18.545454+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
39
data/en.wikipedia.org/wiki/Camille_Noûs-0.md
Normal file
39
data/en.wikipedia.org/wiki/Camille_Noûs-0.md
Normal file
@ -0,0 +1,39 @@
|
||||
---
|
||||
title: "Camille Noûs"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Camille_Noûs"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:02.692330+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Camille Noûs is a fictional name assumed by the French collective academic project RogueESR. The character of "Camille Noûs" was created in 2020 when the group signed an open letter concerning French science policy. In 2021, "Camille Noûs" was listed as an author on 180 journal papers in several scientific disciplines.
|
||||
|
||||
|
||||
== Background ==
|
||||
The intent of the Camille Noûs character has been described as a protest against changes in research culture, including the rise of temporary academic positions, the decline in researcher job security and tenure protections, and research assessment metrics that emphasise citation numbers.
|
||||
The Camille Noûs campaign has been criticized as "ethically questionable" due to concerns about responsibility and accountability for research results. There has also been criticism that has arisen from some researchers including Camille Noûs on the author list without informing journal editors that it is a fictitious name.
|
||||
The intention of the Camille Noûs character is to protest individualism in scientific research, and to promote collaboration over individual accomplishment.
|
||||
In April 2021, a manifesto by Camille Noûs was published on the science blog 3 Quarks Daily.
|
||||
|
||||
|
||||
== See also ==
|
||||
Citation impact
|
||||
Scientific citation
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== Sources ==
|
||||
|
||||
|
||||
== Further reading ==
|
||||
RogueESR, . & Noûs, C. (2023). Comment Camille devint Noûs. La Pensée, 414, 108-111. https://doi.org/10.3917/lp.414.0108
|
||||
Camille Noûs and Collective of Journals in Struggle. (2019). Why French Academic Journals are Protesting. European Journal of Turkish Studies, 29. https://doi.org/10.4000/ejts.6441
|
||||
|
||||
|
||||
== External links ==
|
||||
Who is Camille Noûs, the fictitious French researcher with nearly 200 papers?
|
||||
We, Camille Noûs -Research As A Common
|
||||
41
data/en.wikipedia.org/wiki/Casanova's_Lottery-0.md
Normal file
41
data/en.wikipedia.org/wiki/Casanova's_Lottery-0.md
Normal file
@ -0,0 +1,41 @@
|
||||
---
|
||||
title: "Casanova's Lottery"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Casanova's_Lottery"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:03.929410+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Casanova's Lottery: The History of a Revolutionary Game of Chance is a history book about the French Loterie by historian and statistician Stephen M. Stigler.
|
||||
|
||||
|
||||
== Content ==
|
||||
It is easy to associate statistics with death, thanks to actuarial tables and life expectancies, but the history of statistics also contains its libidinal opposite. Amid frequency distributions and tables of payout odds, Casanova's Lottery reminds us that the history of statistics is also a history of dreams, sex, and hope.
|
||||
Stigler's Casanova's Lottery: The History of a Revolutionary Game of Chance tells how, thanks to the direct involvement of the Venetian Giacomo Casanova, the French Loterie was established, lasting from 1758 to 1836 – with a four-year interruption during the French Revolution in 1793–1797.
|
||||
A quarter of a billion tickets were sold over that period through lottery offices all over France, thanks to which the state budget received "millions of livres (and then francs)".
|
||||
The Loterie was unique because, unlike a raffle, the maximum possible winning to be disbursed by the state to the winners was not known in advance. Thus, it was considerably riskier for the French administration, be it monarchical, republican, or imperial.
|
||||
|
||||
At each drawing, the state was at risk of losing a large amount; what is more, that risk was precisely calculable, generally well understood, and yet taken on by the state with little more than a mathematical theory to protect it.
|
||||
Bets were made on the drawing without replacement of five random numbers out of a total of 90 numbers, each number being associated with a woman's name to make fraud more difficult. Bettors wagered on one, two or three (and later four) numbers.
|
||||
The book describes the various stages of the Loterie. Initially established to fund the French École militaire – the discussions leading to the establishment of the school saw the direct involvement of the creator of the École militaire, Joseph Pâris Duverney, as well as of the French academician Jean d'Alembert, of Madame Pompadour and of Casanova – the lottery was subsequently conducted under the responsibility of the ministry of finance.
|
||||
Among the interesting stories told in the volume is how Voltaire won a fortune of several million francs by participating to a scheme in an earlier French state lottery that aimed at reimbursing state debtors.
|
||||
The interest of the book also comes from the author's statistical expertise. Stigler relies on Menut de Saint Mesmin's Almanach Romain sur la loterie de France, published in 1834, and intended as a guide for bettors. Since the Almanach reports the winning numbers drawn since 1758 as well as the prizes paid out, it constitutes for Stigler
|
||||
|
||||
a precisely randomized survey of the French betting public [...] "more than a century before randomized surveys were invented".
|
||||
Two appendices describe respectively the formulae for the calculus of probabilities relative to the lottery and a theorem from Pierre-Simon Laplace on the distribution of the number of draws needed for all 90 numbers to appear at least once.
|
||||
The book won the 2023 Neumann Prize of the British Society for the History of Mathematics.
|
||||
|
||||
|
||||
== See also ==
|
||||
Lottery
|
||||
Raffle
|
||||
Game of chance
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Casanova's Lottery page at Chicago University Press
|
||||
@ -0,0 +1,19 @@
|
||||
---
|
||||
title: "Center on Organizational Innovation"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Center_on_Organizational_Innovation"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:05.117117+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The Center on Organizational Innovation (COI) is a research center at Columbia University's Institute for Social and Economic Research and Policy. The center, established in 2000 and directed by sociologist David Stark, promotes research in the areas of organizational studies, science and technology studies and economic sociology, with an emphasis on innovation and reflexivity.[1]
|
||||
|
||||
|
||||
== References ==
|
||||
^Stark, D. (2008). "Searching questions: The Center on Organizational Innovation at Columbia University". European Management Review. 5 (4): 275–280. doi:10.1057/emr.2008.24.
|
||||
|
||||
|
||||
== External links ==
|
||||
Center on Organizational Innovation
|
||||
@ -0,0 +1,20 @@
|
||||
---
|
||||
title: "Centre de Sociologie de l'Innovation"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Centre_de_Sociologie_de_l'Innovation"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:06.339150+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The Centre de Sociologie de l'Innovation (CSI; "Center for the Sociology of Innovation") is a research center at the Mines Paris – PSL, France, and a research unit affiliated to the French National Centre for Scientific Research.
|
||||
The CSI was created in 1967 and is known for its members' contributions to the field of science and technology studies and to actor–network theory.[1] Prominent past and current members include academics such as Bruno Latour and Michel Callon.
|
||||
|
||||
|
||||
== References ==
|
||||
^ Dosse, François (1999). Empire of Meaning: The Humanization of the Social Sciences. University of Minnesota Press. ISBN 978-0-8166-2964-0.
|
||||
|
||||
|
||||
== External links ==
|
||||
Centre de Sociologie de l'Innovation
|
||||
@ -4,7 +4,7 @@ chunk: 1/3
|
||||
source: "https://en.wikipedia.org/wiki/Chemtrail_conspiracy_theory"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:39:47.833953+00:00"
|
||||
date_saved: "2026-05-05T07:10:19.960091+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 2/3
|
||||
source: "https://en.wikipedia.org/wiki/Chemtrail_conspiracy_theory"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:39:47.833953+00:00"
|
||||
date_saved: "2026-05-05T07:10:19.960091+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 3/3
|
||||
source: "https://en.wikipedia.org/wiki/Chemtrail_conspiracy_theory"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:39:47.833953+00:00"
|
||||
date_saved: "2026-05-05T07:10:19.960091+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
20
data/en.wikipedia.org/wiki/Claude_Langlois-0.md
Normal file
20
data/en.wikipedia.org/wiki/Claude_Langlois-0.md
Normal file
@ -0,0 +1,20 @@
|
||||
---
|
||||
title: "Claude Langlois"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Claude_Langlois"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:10:37.018024+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Claude Langlois (c. 1700 – 1756) was a French maker of precision scientific instruments and the foremost among them in the period. His instruments included draughtsman's tools like an improved pantograph, measuring instruments, and six-foot quadrants for astronomical angle measurement. He was appointed official instrument maker for French astronomers Cassini II, Cassini de Thury, Le Monnier, Maupertuis, and the Abbé de Lacaille; and held the official position of ingénieur en instruments de mathématiques for the French Académie des Sciences in 1740.
|
||||
Little is known of Langlois' life but he was considered the most famous maker of scientific instruments between 1730 and 1756 and many of his instruments are known from his name on them. This was a period when English instrument makers were leading with master instrument makers like Nicolas Bion and Michael Butterfield. Langlois' earliest known contract was for a six-foot wall quadrant for the Paris observatory with markings that indicate that he worked at the Niveau on the Quai de l'Horloge. He also produced instruments for use in labs (including those of Lavosier), by surveyors, navigators and astronomers. His improved pantograph design was sent to the Académie des Sciences for approval. His instruments were sent on geodesic expeditions to Peru and Lapland in 1733-35 which included measuring standards for the toise (the length standard then in use). In 1744 he was in charge of restoring a gnomon at the Church of St Sulpice, Paris. After his death, his nephew Jacques Canivet produced eighty copies of the Toise. His position at the Academy was taken by a pupil of his, Lennel.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Description et usage du pantographe, autrement appelé singe, changé & perfectionné par C. Langlois, ingénieur du Roi & de l'Académie royale des sciences pour les instrumens de mathématiques (1744)
|
||||
A set of instruments in the History of Science Museum
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Coded_Bias"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:58:30.410648+00:00"
|
||||
date_saved: "2026-05-05T07:09:24.254730+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Computer_Engineer_Barbie"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T04:38:34.331879+00:00"
|
||||
date_saved: "2026-05-05T07:09:25.455441+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
60
data/en.wikipedia.org/wiki/Concept_testing-0.md
Normal file
60
data/en.wikipedia.org/wiki/Concept_testing-0.md
Normal file
@ -0,0 +1,60 @@
|
||||
---
|
||||
title: "Concept testing"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Concept_testing"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:07.601429+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Concept testing (to be distinguished from pre-test markets and test markets which may be used at a later stage of product development research) is the process of using surveys (and sometimes qualitative methods) to evaluate consumer acceptance of a new product idea prior to the introduction of a product to the market. It is important not to confuse concept testing with advertising testing, brand testing and packaging testing, as is sometimes done. Concept testing focuses on the basic product idea, without the embellishments and puffery inherent in advertising.
|
||||
|
||||
|
||||
== Questionnaires ==
|
||||
It is important that the instruments (questionnaires) to test the product have a high quality themselves. Otherwise, results from data gathered surveys may be biased by measurement error. That makes the design of the testing procedure more complex. Empirical tests provide insight into the quality of the questionnaire. This can be done by:
|
||||
|
||||
conducting cognitive interviewing. By asking a faction of potential-respondents about their interpretation of the questions and use of the questionnaire, a researcher can verify the viability of the cognitive interviewing.
|
||||
carrying out a small pretest of the questionnaire, using a small subset of target respondents. Results can inform a researcher of errors such as missing questions, or logical and procedural errors.
|
||||
estimating the measurement quality of the questions. This can be done for instance using test-retest, quasi-simplex, or mutlitrait-multimethod models.
|
||||
predicting the measurement quality of the question. This can be done using the software Survey Quality Predictor (SQP).
|
||||
|
||||
|
||||
== Concept testing ==
|
||||
Concept testing in the new product development (NPD) process is the concept generation stage. The concept generation stage of concept testing can take on many forms. Sometimes concepts are generated incidentally, as the result of technological advances. At other times concept generation is deliberate: examples include brain-storming sessions, problem detection surveys and qualitative research. While qualitative research can provide insights into the range of reactions consumers may have, it cannot provide an indication of the likely success of the new concept; this is better left to quantitative concept-test surveys.
|
||||
In the early stages of concept testing, a large field of alternative concepts might exist, requiring concept-screening surveys. Concept-screening surveys provide a quick means to narrow the field of options; however they provide little depth of insight and cannot be compared to a normative database due to interactions between concepts. For greater insight and to reach decisions on whether or not pursue further product development, monadic concept-testing surveys must be conducted.
|
||||
|
||||
|
||||
== Presentation modes ==
|
||||
Frequently concept testing surveys are described as either monadic, sequential monadic, comparative, or proto-monadic. The terms mainly refer to how the concepts are displayed:
|
||||
|
||||
Monadic. The concept is evaluated in isolation.
|
||||
Sequential monadic. Multiple concepts are evaluated in sequence (often randomized order).
|
||||
Comparative. Concepts are shown next to each other.
|
||||
Proto-monadic. Concepts are first shown in sequence, and then next to each other.
|
||||
"Monadic testing is the recommended method for most concept testing. Interaction effects and biases are avoided. Results from one test can be compared to results from previous monadic tests. A normative database can be constructed." However, each has its specific uses and it depends on the research objectives. The decision as to which method to use is best left to experience research professionals to decide, as there are numerous implications in terms of how the results are interpreted.
|
||||
|
||||
|
||||
== Evaluating concept-test scores ==
|
||||
Traditionally concept-test survey results are compared to 'norms databases'. These are databases of previous new-product concept tests. These must be 'monadic' concept tests, to prevent interaction effects. To be fair, it is important that these databases contain 'new' concept test results, not ratings of old products that consumers are already familiar with; since once consumers become familiar with a product the ratings often drop. Comparing new concept ratings to the ratings for an existing product already on the market would result in an invalid comparison, unless special precautions are taken by researchers to reduce or adjust for this effect quantitatively. Additionally, the concept is usually only compared to norms from the same product category, and the same country.
|
||||
Companies that specialize in this area, tend to have developed their own unique systems, each with its own standards. Keeping to these standards consistently is important to preventing contamination of the results.
|
||||
Perhaps one of the famous concept-test systems is the Nielsen Bases system, which comes in different versions. Other well-known products include Decision Analyst's 'Concept Check', Acupoll's 'Concept Optimizer', Ipsos Innoquest and GFK. Examples of smaller players include Skuuber and Acentric Express Test.
|
||||
|
||||
|
||||
== Determining the importance of concept attributes as purchase drivers ==
|
||||
The simplest approach to determining attribute importance is to ask direct open-ended questions. Alternatively checklists or ratings of the importance of each product attribute may be used.
|
||||
However, various debates have existed over whether or not consumers could be trusted to directly indicate the level of importance of each product attribute. As a result, correlation analysis and various forms of multiple regression have often been used for identifying importance - as an alternative to direct questions.
|
||||
A complementary technique to concept testing, is conjoint analysis (also referred to as discrete choice modelling). Various forms of conjoint analysis and discrete choice modelling exist. While academics stress the differences between the two, in practice there is often little difference.
|
||||
These techniques estimate the importance of product attributes indirectly, by creating alternative products according to an experimental design, and then using consumer responses to these alternatives (usually ratings of purchase likelihood or choices made between alternatives) to estimate importance. The results are often expressed in the form of a 'simulator' tool which allows clients to test alternative product configurations and pricing.
|
||||
Volumetric concept testing
|
||||
Volumetric concept testing falls somewhere between traditional concept testing and pre-test market models (simulated test market models are similar but emphasize greater realism) in terms of the level of complexity. The aim is to provide 'approximate' sales volume forecasts for the new concept prior to launch. They incorporate other variables beyond just input from the concept test survey itself, such as the distribution strategy.
|
||||
Examples of volumetric forecasting methodologies include 'Acupoll Foresight' and Decision Analyst's 'Conceptor'.
|
||||
Some models (more properly referred to as 'pre-test market models' or 'simulated test markets') gather additional data from a follow-up product testing survey (especially in the case of consumer packaged goods as repeat purchase rates need to be estimated). They may also include advertisement testing component that aims to assess advertising quality. Some such as Decision Analyst, include discrete choice models / conjoint analysis.
|
||||
|
||||
|
||||
== See also ==
|
||||
marketing research
|
||||
proof of concept
|
||||
|
||||
|
||||
== References ==
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Conquest_of_Abundance"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:21:08.391191+00:00"
|
||||
date_saved: "2026-05-05T07:07:46.746472+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 1/2
|
||||
source: "https://en.wikipedia.org/wiki/Consilience"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:27:16.914985+00:00"
|
||||
date_saved: "2026-05-05T07:07:48.342671+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 2/2
|
||||
source: "https://en.wikipedia.org/wiki/Consilience"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:27:16.914985+00:00"
|
||||
date_saved: "2026-05-05T07:07:48.342671+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 1/2
|
||||
source: "https://en.wikipedia.org/wiki/Consilience_(book)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:17:20.115159+00:00"
|
||||
date_saved: "2026-05-05T07:07:49.498304+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 2/2
|
||||
source: "https://en.wikipedia.org/wiki/Consilience_(book)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:17:20.115159+00:00"
|
||||
date_saved: "2026-05-05T07:07:49.498304+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
27
data/en.wikipedia.org/wiki/Conversation_theory-0.md
Normal file
27
data/en.wikipedia.org/wiki/Conversation_theory-0.md
Normal file
@ -0,0 +1,27 @@
|
||||
---
|
||||
title: "Conversation theory"
|
||||
chunk: 1/5
|
||||
source: "https://en.wikipedia.org/wiki/Conversation_theory"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:08.773848+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Conversation theory is a pedagogical, dialectical, and cybernetic framework that examines conversation, cognition and learning in the context of two conversational participants who attempt to establish what is meant by a topic during a conversation. In effect, each participant attempts to converge towards a common understanding with the other as to the significance of the said topic between themselves. The theory provides a formal dialectical framework that examines how conversational participants may modulate the conceptualisation processes of each participant throughout a conversational interaction.
|
||||
A conversational interaction is characterised as a shared learning process: Each participant takes turns within the interaction—switching at intervals between the role of a student or the role of a teacher—to achieve a state of mutual understanding between themselves. In this sense, the theory concerns itself with how two a priori asynchronous cognitive systems—either mechanical or organic in nature—are able to converse and conceptualise about a topic, in such a way that each system achieves the a posteriori synchronization of each cognitive system's particular perspective or attitude towards said topic. This resultant synchronization allows each conversational participant to satisfy some shared task or goal.
|
||||
Conversation theory provides an experimental framework that heavily utilizes both human-computer interaction frameworks and computer-theoretic models, in order to create a testable framework explaining how conversational interactions may lead to the emergence of shared knowledge between participants. The theory was developed by Gordon Pask, who worked with Bernard Scott, Dionysius Kallikourdis, Robin McKinnon-Wood, and others during its initial development and implementation. With Ranulph Glanville and Paul Pangaro also contributing to the application and implementation of the framework in subsequent periods.
|
||||
|
||||
== Overview ==
|
||||
Conversation theory is a formal theory—with practical applications—describing how conversation modulates conceptualisation processes between conversational participants. Both experimentation and learning exercises are enacted in the practical application of the theory, in order to verify if a consensus has been established between conversational participants in relation to some aim or goal. The theory prioritizes analysing learning and teaching approaches related to education, rather than other concerns about conversational forms. Historically, Gordon Pask and his associates at System Research Ltd., attempted to examine the learning and intellectual development of conversational participants by means of human-machine interactions; thereby sufficiently providing a psychological framework with educational technological applications. Pask's initial motivation for developing conversation theory, was to study and evaluate the nature of cybernetic inquiry via a cybernetic framework.
|
||||
|
||||
=== Conversation ===
|
||||
A conversation—in the context of conversation theory—involves a learning exchange between conversational participants. Because of this, participants engaging in a discussion about a subject matter make their knowledge claims explicit through the means of various conversational transactions. Since meanings are agreed during the course of a conversation, and since purported agreements can be illusory—whereby each conversational participant may assume a shared understanding of a given topic with their partner which may then be verified as false—an empirical approach to the study of conversation would require stable reference points during such conversational exchanges between peers so as to permit reproducible results.
|
||||
Using computer theoretical models of cognition, conversation theory can document these intervals of understanding that arise in the conversations between two participating individuals, such that the development of individual and collective understandings can be analysed rigorously. In this way, Pask has been argued to have been an early pioneer in AI-based educational approaches: Having proposed that advances in computational media may enable conversational forms of interactions to take place between man and machine.
|
||||
|
||||
==== Strict Conversation ====
|
||||
The types of languages that conversation theory demarcates in its approach include (i) language regarding the experiment itself and (ii) languages used by the experimental participants during the course of an experiment. Within conversation theory, a dialogue is said to display different modalities: This is based on how an observer chooses to frame a language under observation within the context of an experiment. The types of languages considered in conversation theory are as follows: Natural languages used for general discussions outside of the experiment; object languages which are the subject of inquiry during an experiment, and finally the metalanguage which is used to talk about the design, management, and results on an experiment.
|
||||
For the purposes of experimentation and observation, all conversational transactions within a given interaction under observation take the form of a strict conversation: That is, a conversation where (i) there is an agreement to limit the conversational engagement to a finite number of topics, and (ii) there exists some experimenter who documents each iteration of a participant's ongoing understanding of a given topic during a conversation. Such strict conversations are monitored though a cooperative externalisation technique (CET) which involves participants agreeing to externalise their—normally private—cognitive events within the context of a strictly observable experiment.
|
||||
Each experiment operates under the principle of there existing a CET heuristic. This heuristic refers to the assumption—held by both the experimenter and each participant of the strict conversation—that (i) one participant is able to correctly interpret another participant's understanding of a topic through constructing an explanation of said topic, and (ii) that such an understanding persist for the remainder of a given conversational interaction.
|
||||
|
||||
==== Object Language ====
|
||||
153
data/en.wikipedia.org/wiki/Conversation_theory-1.md
Normal file
153
data/en.wikipedia.org/wiki/Conversation_theory-1.md
Normal file
@ -0,0 +1,153 @@
|
||||
---
|
||||
title: "Conversation theory"
|
||||
chunk: 2/5
|
||||
source: "https://en.wikipedia.org/wiki/Conversation_theory"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:08.773848+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The object language
|
||||
|
||||
|
||||
|
||||
L
|
||||
|
||||
|
||||
{\displaystyle L}
|
||||
|
||||
differs from most formal languages, by virtue of being "a command and question language[,] not an assertoric language like [a] predicate calculus". It priorities the sematic and pragmatic aspects of language rather than the syntactical features of propositions and their corresponding truth or falsity values. It is often formally illustrated and embodied in the form of a conversational skeleton within conversation theory, which forms the basic unit of a conversational interactions between a collection of conversational participants
|
||||
|
||||
|
||||
|
||||
Z
|
||||
=
|
||||
⟨
|
||||
A
|
||||
,
|
||||
B
|
||||
⟩
|
||||
|
||||
|
||||
{\displaystyle Z=\langle A,B\rangle }
|
||||
|
||||
. This unit is contextualized as an extended learning system whose internal states are changed and modulated through the course of the conversation.
|
||||
Since conversation theory specifically focuses on learning and development within agents capable of cognition and conversation, the object language is separated into two distinct modes of conversing; a likeness of which, resembles the distinction between procedural knowledge and declarative knowledge in epistemology. Firstly, there is conversation which happens at the level of
|
||||
|
||||
|
||||
|
||||
|
||||
L
|
||||
|
||||
0
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle L^{0}}
|
||||
|
||||
of an object language, which is concerned with "how to “do” a topic: how to recognize it, construct it, maintain it and so on". Meanwhile, the
|
||||
|
||||
|
||||
|
||||
|
||||
L
|
||||
|
||||
1
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle L^{1}}
|
||||
|
||||
level of an object language is "concerned with explaining or justifying what a topic means in terms of other topics". This is expressed by the ordered-pair
|
||||
|
||||
|
||||
|
||||
L
|
||||
=
|
||||
⟨
|
||||
|
||||
L
|
||||
|
||||
0
|
||||
|
||||
|
||||
,
|
||||
|
||||
L
|
||||
|
||||
1
|
||||
|
||||
|
||||
⟩
|
||||
|
||||
|
||||
{\displaystyle L=\langle L^{0},L^{1}\rangle }
|
||||
|
||||
.
|
||||
The conversational architecture which embodies an object language consists of the following components: Firstly, there are topic relations which may be discussed; secondly, there are speech-acts or descriptions that may be used to converse about said topics; thirdly, a series of procedural repertoires which act to modulate conversational and activity-based input and outputs, and finally a modelling facility whereby conversational participants may collaboratively build working models of a topic with each other. These components—when put together in the form of a conversational skeleton—provide the most basic unit of conversation within conversation theory.
|
||||
|
||||
==== Command and Question Language ====
|
||||
The object language is also a language of commands and questions within conversation theory. In essence, this framing merely highlights another modality of the object language: One that frames the object language in terms of the type of speech acts that may be used when enacting different levels of itself. In a conversation between
|
||||
|
||||
|
||||
|
||||
A
|
||||
|
||||
|
||||
{\displaystyle A}
|
||||
|
||||
and
|
||||
|
||||
|
||||
|
||||
B
|
||||
|
||||
|
||||
{\displaystyle B}
|
||||
|
||||
, if the later participant acknowledges the former participant's statements of intent as corresponding to a command or question if they view that behaviour as legal within the confines of the conversational interaction.
|
||||
On level
|
||||
|
||||
|
||||
|
||||
|
||||
L
|
||||
|
||||
0
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle L^{0}}
|
||||
|
||||
of the command and question language, the use of both commands and questions are active: Commands at this level are intended to direct the recipient to act in ways that may generate solutions to a problem, while the use of questions is intended to generate explanations from the recipient regarding what they are doing in a given activity. On level
|
||||
|
||||
|
||||
|
||||
|
||||
L
|
||||
|
||||
1
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle L^{1}}
|
||||
|
||||
of the language meanwhile, the use of both commands and questions are reflective: The intent of commands is to get the recipient to construct a model that describes what they have done, while the use of questions is intended to get the recipient to provide a step-by-step explanation as to how it was solved and to check if any errors of incompatible relations occurred in the construction of the model.
|
||||
There are three basic conversational transaction types within the language, which include: Commands, questions, and explanations. There also exist cognitive procedures simply known as executions. Such cognitive procedures operate and are enacted upon a modelling facility, which may allow conversational participants to co-design via the synthesis of partial models and some working model. Meanwhile, if such cognitive procedures are to be converted to a speech-act or description
|
||||
|
||||
|
||||
|
||||
D
|
||||
|
||||
|
||||
{\displaystyle D}
|
||||
|
||||
of some topic relation, then such an utterance would also be composed of linguistic expressions that help compose its sense.
|
||||
Below is a table of operation types that may be enacted within a conversational interaction:
|
||||
|
||||
The operations are characterised above as either (i) conversational transactions which are used to induce conceptualisation, or (ii) cognitive procedures which enact conceptualisation procedures so that the contents of such conceptualisation can be applied in the form of some speech-act or utterance. Both conversational transactions and cognitive procedures have transformational structures that are intended by a conversational participant to induce a given behaviour in the other.
|
||||
415
data/en.wikipedia.org/wiki/Conversation_theory-2.md
Normal file
415
data/en.wikipedia.org/wiki/Conversation_theory-2.md
Normal file
@ -0,0 +1,415 @@
|
||||
---
|
||||
title: "Conversation theory"
|
||||
chunk: 3/5
|
||||
source: "https://en.wikipedia.org/wiki/Conversation_theory"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:08.773848+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Both commands and questions of the type
|
||||
|
||||
|
||||
|
||||
D
|
||||
(
|
||||
R
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle D(R)}
|
||||
|
||||
have the following basic form, which is taken from Nicholas Rescher's formalisation of The Logic of Commands (1966):
|
||||
|
||||
|
||||
|
||||
D
|
||||
(
|
||||
R
|
||||
)
|
||||
|
||||
=
|
||||
|
||||
<
|
||||
Z
|
||||
!
|
||||
|
||||
X
|
||||
|
||||
|
||||
|
|
||||
|
||||
|
||||
Y
|
||||
|
||||
>
|
||||
|
||||
|
||||
{\displaystyle D(R)\ =\ <Z!\ X\ |\ Y\ >}
|
||||
|
||||
Where
|
||||
|
||||
|
||||
|
||||
Z
|
||||
!
|
||||
|
||||
|
||||
{\displaystyle Z!}
|
||||
|
||||
obligates the addressee or receiver of the command or question to act in accordance with the intention of the messenger, and
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
||||
|
||||
|
||||
|
||||
{\displaystyle |}
|
||||
|
||||
is a conditional relation. Therefore, the expression
|
||||
|
||||
|
||||
|
||||
X
|
||||
|
||||
|
||||
|
|
||||
|
||||
|
||||
Y
|
||||
|
||||
|
||||
{\displaystyle X\ |\ Y}
|
||||
|
||||
can be read as "Do X, given Y". In terms of speech-acts which take the form of commands and questions, they may sufficiently take the form of the below expressions:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
C
|
||||
o
|
||||
m
|
||||
m
|
||||
|
||||
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|
||||
|
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|
||||
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|
||||
|
||||
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|
||||
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|
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|
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|
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|
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||||
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|
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||||
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|
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|
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||||
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|
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|
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|
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|
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|
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||||
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||||
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||||
|
||||
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|
||||
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|
||||
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|
||||
|
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|
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|
||||
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|
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|
||||
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|
||||
|
||||
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|
||||
|
||||
|
||||
|
||||
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|
||||
|
||||
|
||||
|
|
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|
||||
|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle {\begin{array}{lcl}Comm\ i&=&<Z!\ Exec_{Z}\ i\ |\ Precon\ >\\EQuest\ i&=&<Z!\ Expl_{Z}\ i\ |\ Precon\ >\end{array}}}
|
||||
|
||||
For example, if some conversational participant
|
||||
|
||||
|
||||
|
||||
A
|
||||
|
||||
|
||||
{\displaystyle A}
|
||||
|
||||
issues a command to conversational participant
|
||||
|
||||
|
||||
|
||||
B
|
||||
|
||||
|
||||
{\displaystyle B}
|
||||
|
||||
then the utterance would take the form of "Attention Addressee! Do such-and-such, in relation to so-and-so" or "Attention Z! Do such-and-such, given this parameter". However, not all operations in the command and question language are prescriptive. For example, providing explanations to others, or executing actions within a modelling facility do not prescribe that the other participant act but are processes that build a conceptual model or attempt to convey it within a conversation.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
E
|
||||
x
|
||||
p
|
||||
l
|
||||
|
||||
i
|
||||
|
||||
|
||||
=
|
||||
|
||||
|
||||
<
|
||||
α
|
||||
,
|
||||
β
|
||||
,
|
||||
γ
|
||||
,
|
||||
.
|
||||
.
|
||||
.
|
||||
,
|
||||
R
|
||||
>
|
||||
|
||||
|
||||
|
||||
|
||||
E
|
||||
x
|
||||
e
|
||||
c
|
||||
|
||||
i
|
||||
|
||||
|
||||
=
|
||||
|
||||
|
||||
<
|
||||
a
|
||||
,
|
||||
b
|
||||
,
|
||||
c
|
||||
,
|
||||
.
|
||||
.
|
||||
.
|
||||
,
|
||||
M
|
||||
>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle {\begin{array}{lcl}Expl\ i&=&<\alpha ,\beta ,\gamma ,...,R>\\Exec\ i&=&<a,b,c,...,M>\end{array}}}
|
||||
|
||||
Where for the first operator the Greek letters—e.g.,
|
||||
|
||||
|
||||
|
||||
α
|
||||
,
|
||||
β
|
||||
,
|
||||
γ
|
||||
|
||||
|
||||
{\displaystyle \alpha ,\beta ,\gamma }
|
||||
|
||||
—designate a series of linguistic expressions that help compose the sense of a topic relation within an explanation, and where for the second operator there exist a series of partial models—e.g.,
|
||||
|
||||
|
||||
|
||||
a
|
||||
,
|
||||
b
|
||||
,
|
||||
c
|
||||
|
||||
|
||||
{\displaystyle a,b,c}
|
||||
|
||||
—and the derived working model
|
||||
|
||||
|
||||
|
||||
M
|
||||
|
||||
|
||||
{\displaystyle M}
|
||||
|
||||
that form the process of some execution.
|
||||
|
||||
=== Concepts ===
|
||||
The term concept in conversation theory is contextualised differently to other uses of the term concept. It has been argued that in conversation theory a concept is not a mental representation nor an abstract idea nor is it strictly speaking an ability—as commonly articulated within the subfield of conceptual ontology. While it has properties which intersect with all these ontologies, a concept is treated in conversation theory in terms of a non-localised process which attempt to maintain the coherence of some set of relations with themselves in the face of incompatibilities.
|
||||
A concept is not the product of such a process—such as a symbol or mental coordinate—but an ongoing process of feedback which produces, maintains and modulates some set of topics. It is also not a class, nor description of a class, nor a stored description: Instead, a concept is specifically used to reconstruct, reproduce or stabilize relations. In this sense, they may be sufficiently conceived as mental organisations or working models that hold a hypothesis and seek to test said hypothesis in order to confirm or deny its validity.
|
||||
|
||||
==== Formal Structure ====
|
||||
Strictly speaking, a concept in conversation theory—as described by Pask—is conceived of as the production, reproduction, and maintenance of a given topic relation
|
||||
|
||||
|
||||
|
||||
|
||||
R
|
||||
|
||||
i
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle R_{i}}
|
||||
|
||||
via other topic relations belonging some conversational domain
|
||||
|
||||
|
||||
|
||||
R
|
||||
|
||||
|
||||
{\displaystyle R}
|
||||
|
||||
. This implies that all topic relations—within some area of a conversational domain—must mutually entail each other through a cyclic process of reciprocation.
|
||||
|
||||
Now if we let
|
||||
|
||||
|
||||
|
||||
|
||||
R
|
||||
|
||||
H
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle R_{H}}
|
||||
|
||||
denote some arbitrary head topic relation that may be discussed, then it is considered identical to a procedural working model of said relation that produces it.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
R
|
||||
|
||||
H
|
||||
|
||||
|
||||
=
|
||||
P
|
||||
R
|
||||
O
|
||||
C
|
||||
(
|
||||
|
||||
R
|
||||
|
||||
H
|
||||
|
||||
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle R_{H}=PROC(R_{H})}
|
||||
|
||||
|
||||
In which a working model of said relation is treated as identical to its concept:
|
||||
|
||||
|
||||
|
||||
|
||||
P
|
||||
R
|
||||
O
|
||||
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|
||||
(
|
||||
|
||||
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|
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|
||||
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|
||||
|
||||
|
||||
)
|
||||
=
|
||||
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|
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
|
||||
H
|
||||
|
||||
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle PROC(R_{H})=CON(R_{H})}
|
||||
|
||||
234
data/en.wikipedia.org/wiki/Conversation_theory-3.md
Normal file
234
data/en.wikipedia.org/wiki/Conversation_theory-3.md
Normal file
@ -0,0 +1,234 @@
|
||||
---
|
||||
title: "Conversation theory"
|
||||
chunk: 4/5
|
||||
source: "https://en.wikipedia.org/wiki/Conversation_theory"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:08.773848+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
And where a concept is defined as an operation that is substitutable for the below ordered-pair:
|
||||
|
||||
|
||||
|
||||
C
|
||||
O
|
||||
N
|
||||
≜
|
||||
P
|
||||
R
|
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O
|
||||
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|
||||
=
|
||||
⟨
|
||||
P
|
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R
|
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|
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|
||||
,
|
||||
I
|
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|
||||
T
|
||||
E
|
||||
R
|
||||
⟩
|
||||
|
||||
|
||||
{\displaystyle CON\triangleq PROC=\langle PROG,INTER\rangle }
|
||||
|
||||
Whose contents are defined as:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
I
|
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|
||||
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|
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|
||||
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|
||||
|
||||
|
||||
≜
|
||||
|
||||
|
||||
λ
|
||||
(
|
||||
x
|
||||
)
|
||||
|
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|
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|
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|
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|
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|
||||
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|
||||
G
|
||||
|
||||
|
||||
≜
|
||||
|
||||
|
||||
π
|
||||
(
|
||||
x
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle {\begin{array}{lcl}INTER&\triangleq &\lambda (x)\\PROG&\triangleq &\pi (x)\end{array}}}
|
||||
|
||||
Such that a program is defined as a calculus or set of rules
|
||||
|
||||
|
||||
|
||||
π
|
||||
|
||||
|
||||
{\displaystyle \pi }
|
||||
|
||||
that operates on some input, and an interpretation is conceived of as a compiler
|
||||
|
||||
|
||||
|
||||
λ
|
||||
|
||||
|
||||
{\displaystyle \lambda }
|
||||
|
||||
that attempts to compile said input into an executable form. A concept—as thus defined—is considered by Pask to be an
|
||||
|
||||
|
||||
|
||||
L
|
||||
|
||||
|
||||
{\displaystyle L}
|
||||
|
||||
-procedure or process, which is embodied by its underlying processor called an
|
||||
|
||||
|
||||
|
||||
L
|
||||
|
||||
|
||||
{\displaystyle L}
|
||||
|
||||
-processor. When said form has been compiled by said processor, then the concept may be executed to produce a topic relation, which may be sufficiently written as:
|
||||
|
||||
|
||||
|
||||
A
|
||||
P
|
||||
(
|
||||
λ
|
||||
(
|
||||
π
|
||||
(
|
||||
|
||||
R
|
||||
|
||||
H
|
||||
|
||||
|
||||
)
|
||||
)
|
||||
)
|
||||
=
|
||||
A
|
||||
P
|
||||
(
|
||||
C
|
||||
O
|
||||
N
|
||||
(
|
||||
|
||||
R
|
||||
|
||||
H
|
||||
|
||||
|
||||
)
|
||||
)
|
||||
⇒
|
||||
|
||||
R
|
||||
|
||||
H
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle AP(\lambda (\pi (R_{H})))=AP(CON(R_{H}))\Rightarrow R_{H}}
|
||||
|
||||
Which represents one such representation of the basic logical form of a concept within conversation theory. In later work, Pask envisaged conceptualisation as a parallel rather than a serial process: That within a conversational system, conversations may or may not be taking place at any given time. Because of this, he demarcates the application operator
|
||||
|
||||
|
||||
|
||||
A
|
||||
P
|
||||
|
||||
|
||||
{\displaystyle AP}
|
||||
|
||||
and the execution operator
|
||||
|
||||
|
||||
|
||||
&
|
||||
|
||||
|
||||
{\displaystyle \&}
|
||||
|
||||
to indicate when the activation of a concept is necessarily activated or when it is sufficiently activated within a conversational system.
|
||||
|
||||
==== Protologic ====
|
||||
The protologic or protolanguage (simplified often as Lp), is a sister theory of conversation theory which contains a system of rules of inference governing conceptual formulation and modulation. The rules and principles governing conceptualisation within the protologic are often illustrated as entailment structures, which are representational snapshots that may help visualize an organized and publicly available collection of resultant knowledge within conversation theory. The entailment structure a graph composed of a series of nodes and arrows representing a series of topic relations and the derivations of such topic relations.
|
||||
|
||||
===== Entailment Derivation =====
|
||||
|
||||
===== Collective Derivation =====
|
||||
|
||||
===== Disjunct Derivation =====
|
||||
|
||||
===== Analogy Derivation =====
|
||||
|
||||
=== Cognitive Reflector ===
|
||||
From conversation theory, Pask developed what he called a "Cognitive Reflector". This is a virtual machine for selecting and executing concepts or topics from an entailment mesh shared by at least a pair of participants. It features an external modelling facility on which agreement between, say, a teacher and pupil may be shown by reproducing public descriptions of behaviour. We see this in essay and report writing or the "practicals" of science teaching.
|
||||
|
||||
Lp was Pask's protolanguage which produced operators like Ap which concurrently executes the concept, Con, of a Topic, T, to produce a Description, D. Thus:
|
||||
|
||||
|
||||
|
||||
A
|
||||
P
|
||||
(
|
||||
C
|
||||
O
|
||||
N
|
||||
)
|
||||
⇒
|
||||
D
|
||||
(
|
||||
T
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle AP(CON)\Rightarrow D(T)}
|
||||
|
||||
where => stands for produces.
|
||||
A succinct account of these operators is presented in Pask Amongst many insights he points out that three indexes are required for concurrent execution, two for parallel and one to designate a serial process. He subsumes this complexity by designating participants A, B, etc.
|
||||
In Commentary toward the end of Pask, he states:
|
||||
|
||||
The form not the content of the theories (conversation theory and interactions of actors theory) return to and is congruent with the forms of physical theories; such as wave particle duality (the set theoretic unfoldment part of conversation theory is a radiation and its reception is the interpretation by the recipient of the descriptions so exchanged, and vice versa). The particle aspect is the recompilation by the listener of what a speaker is saying. Theories of many universes, one at least for each participant A and one to participant B- are bridged by analogy. As before this is the truth value of any interaction; the metaphor for which is culture itself.
|
||||
|
||||
=== Learning strategies ===
|
||||
|
||||
In order to facilitate learning, Pask argued that subject matter should be represented in the form of structures which show what is to be learned. These structures exist in a variety of different levels depending upon the extent of the relationships displayed. The critical method of learning according to Conversation Theory is "teachback" in which one person teaches another what they have learned.
|
||||
Pask identified two different types of learning strategies:
|
||||
51
data/en.wikipedia.org/wiki/Conversation_theory-4.md
Normal file
51
data/en.wikipedia.org/wiki/Conversation_theory-4.md
Normal file
@ -0,0 +1,51 @@
|
||||
---
|
||||
title: "Conversation theory"
|
||||
chunk: 5/5
|
||||
source: "https://en.wikipedia.org/wiki/Conversation_theory"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:08.773848+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Serialists – Progress through a structure in a sequential fashion
|
||||
Holists – Look for higher order relations
|
||||
The ideal is the versatile learner who is neither vacuous holist "globe trotter" nor serialist who knows little of the context of his work.
|
||||
In learning, the stage where one converges or evolves, many Cyberneticians describe the act of understanding as a closed-loop. Instead of simply “taking in” new information, one goes back to look at their understandings and pulls together information that was “triggered” and forms a new connection. This connection becomes tighter and one's understanding of a certain concept is solidified or “stable” (Pangaro, 2003). Furthermore, Gordon Pask emphasized that conflict is the basis for the notion of “calling for'' additional information (Pangaro, 1992).
|
||||
According to Entwistle, experiments which lead to the investigation of phenomenon later denoted by the term learning strategy came about through the implementation of a variety of learning tasks. Initially, this was done through utilising either CASTE, INTUITION, or the Clobbits pseudo-taxonomy. However, given issues resulting from either the time-consuming nature or operating experiments or inexactness of experimental conditions, new tests were created in the form of the Spy Ring History test and the Smuggler's test. The former test involved a participant having to learn the history of a fictitious spy ring (in other words, the history of a fictitious espionage network); the participant, having to learn about the history of five spies in three countries over the period of five years. The comprehension learning component of the test involved learning the similarities and differences between a set of networks; whereas the operation learning aspect of the test involved learning the role each spy played and what sequence of actions that spy played over a given year.
|
||||
While Entwistle noted difficulties regarding the length of such tests for groups of students who were engaged in the Spy Ring History test, the results of the test did seem to correspond with the type of learning strategies discussed. However, it has been noted that while Pask and associates work on learning styles has been influential in both the development of conceptual tools and methodology, the Spy Ring History test and Smuggler's test may have been biased towards STEM students than humanities in its implementation, with Entwistle arguing that the "rote learning of formulae and definitions, together with a positive reaction to solving puzzles and problems of a logical nature, are characteristics more commonly found in science than arts student".
|
||||
|
||||
== Applications ==
|
||||
One potential application of conversation theory that has been studied and developed is as an alternative approach to common types of search engine Information retrieval algorithms. Unlike PageRank-like algorithms, which determine the priority of a search result based on how many hyperlinks on the web link to them, conversation theory has been used to apply a discursive approach to web search requests.
|
||||
ThoughtShuffler is an attempt to build a search engine utilizing design principles from conversation theory: In this approach, terms that are input into a search request yield search results relating to other terms that derive or help provide context to the meaning of the first in a way that mimics derivations of topics in an entailment structure. For example, given the input of a search term, a neighbourhood of corresponding terms that comprise the meaning of the first term may be suggested for the user to explore. In doing this, the search engine interface highlights snippets of webpages corresponding to a neighbourhood terms that help provide meaning to the first.
|
||||
The aim of this design, is to provide just enough information for a user to become curious about a topic in order to induce the intention to explore other subtopics related to the main term input into the search engine.
|
||||
|
||||
== See also ==
|
||||
Conversational constraints theory
|
||||
Analogy § Cybernetics
|
||||
Gordon Pask § Interactions of Actors Theory
|
||||
Integrative learning
|
||||
Text and conversation theory
|
||||
|
||||
== Footnotes ==
|
||||
|
||||
== References ==
|
||||
|
||||
=== Citation Sources ===
|
||||
|
||||
== Further reading ==
|
||||
Ranulph Glanville and Karl H. Muller (eds.), Gordon Pask, Philosopher Mechanic- An Introduction to the Cybernetician's Cybernetician edition echoraum 2007 ISBN 978-3-901941-15-3
|
||||
Aleksej Heinze, Chris Procter, "Use of conversation theory to underpin blended learning", in: International Journal of Teaching and Case Studies (2007) – Vol. 1, No.1/2 pp. 108 – 120
|
||||
W. R. Klemm, Software Issues for Applying Conversation Theory For Effective Collaboration Via the Internet, Manuscript 2002.
|
||||
Gordon Pask, Conversation, cognition and learning. New York: Elsevier, 1975.
|
||||
Gordon Pask, The Cybernetics of Human Learning and Performance, Hutchinson. 1975
|
||||
Gordon Pask, Conversation Theory, Applications in Education and Epistemology, Elsevier, 1976.
|
||||
Gordon Pask, Heinz von Foerster's Self-Organisation, the Progenitor of Conversation and Interaction Theories, 1996.
|
||||
Scott, B. (ed. and commentary) (2011). "Gordon Pask: The Cybernetics of Self-Organisation, Learning and Evolution Papers 1960-1972" pp 648 Edition Echoraum (2011).
|
||||
|
||||
== External links ==
|
||||
PDFs of Pask's books and key papers at pangaro.com
|
||||
Conversation Theory – Gordon Pask overview from web.cortland.edu.
|
||||
Cybernetics And Conversation by Paul Pangaro, 1994–2000.
|
||||
Conversation Theory: Reasoning about significance and mutuality by Mike Martin and John Dobson,
|
||||
Conversation Theory developed by the cybernetician Gordon Pask by Yitzhak I. Hayut-Man ea, 1995.
|
||||
31
data/en.wikipedia.org/wiki/Creativity-0.md
Normal file
31
data/en.wikipedia.org/wiki/Creativity-0.md
Normal file
@ -0,0 +1,31 @@
|
||||
---
|
||||
title: "Creativity"
|
||||
chunk: 1/14
|
||||
source: "https://en.wikipedia.org/wiki/Creativity"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:10.173371+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Creativity is the ability to generate novel and valuable ideas or works through the exercise of imagination. The products of creativity may be classified as either intangible or physical. Intangible products of creativity include ideas, scientific theories, literary works, musical compositions, and jokes. Physical products of creativity include inventions, dishes or meals, pieces of jewelry, costumes, and paintings.
|
||||
Creativity may also describe the ability to find new solutions to problems or new methods to accomplish a goal. Therefore, creativity enables people to solve problems in new ways.
|
||||
Most ancient cultures (including Ancient Greece, Ancient China, and Ancient India) lacked the concept of creativity, seeing art as a form of discovery rather than a form of creation. In the Judeo-Christian-Islamic tradition, creativity is seen as the sole province of God, and human creativity was considered an expression of God's work; the modern conception of creativity came about during the Renaissance, influenced by humanist ideas.
|
||||
Scholarly interest in creativity is found in several disciplines, primarily psychology, business studies, and cognitive science. It is also present in education and the humanities (including philosophy and the arts).
|
||||
|
||||
== Etymology ==
|
||||
The English word "creativity" comes from the Latin term creare (meaning "to create"). Its derivational suffixes also come from Latin, such as the etymological root “crescere”, which means “to let things grow”. This aspect of creativity is emphasized more in indigenous and Eastern concepts of creativity. The word "create" appeared in English as early as the 14th century—notably in Chaucer's The Parson's Tale to indicate divine creation. The modern meaning of creativity in reference to human creation did not emerge until after the Age of Enlightenment.
|
||||
|
||||
== Definition ==
|
||||
In a summary of scientific research into creativity, psychology professor Michael Mumford wrote, "We seem to have reached a general agreement that creativity involves the production of novel, useful products." Similarly, in psychologist Robert Sternberg's words, creativity produces "something original and worthwhile".
|
||||
Authors have diverged dramatically in their precise definitions beyond these general commonalities: social geographer Peter Meusburger estimated that over a hundred different definitions can be found in the literature. One definition given by Dr. E. Paul Torrance in the context of assessing an individual's creative ability is "a process of becoming sensitive to problems, deficiencies, gaps in knowledge, missing elements, disharmonies; identifying the difficulty; searching for solutions, making guesses, or formulating hypotheses about the deficiencies: testing and retesting these hypotheses and possibly modifying and retesting them; and finally communicating the results."
|
||||
Philosophy professor Ignacio L. Götz, following the etymology of the word, argued that creativity is not necessarily "making". He confined it to the act of creating without thinking about the end product. While many definitions of creativity seem almost synonymous with originality, Götz also emphasized the difference between creativity and originality. Götz asserted that one can be creative without necessarily being original. When someone creates something, they are certainly creative at that point, but they may not be original in the sense that their creation is not something new.
|
||||
Creativity in general is usually distinguished from innovation in particular, where the emphasis is on implementation. Academics and authors Teresa Amabile and Michael Pratt defined creativity as the production of novel and useful ideas and innovation as the implementation of creative ideas, while the OECD and Eurostat stated that "innovation is more than a new idea or an invention; an innovation requires implementation, either by being put into active use or by being made available for use by other parties, firms, individuals, or organizations."
|
||||
There is also emotional creativity, which is described as a pattern of cognitive abilities and personality traits related to originality and appropriateness in emotional experience.
|
||||
From an interdisciplinary point a view, creativity can serve to increase neuronal connectivity, cognitive and emotional coherence as well as social cohesion. In this respect, creativity can help to cope with despair, hate and violence.
|
||||
|
||||
== Conceptual history ==
|
||||
|
||||
=== Ancient ===
|
||||
Most ancient cultures, including Ancient Greece, Ancient China, and Ancient India, lacked the concept of creativity, seeing art as a form of discovery and not creation. The ancient Greeks had no terms for "to create" or "creator" except for the expression poiein (to make), which only applied to poiesis (poetry) and to the poietes (poet, or "maker", who made it). Plato did not believe in art as a form of creation. He asks in the Republic, "Will we say of a painter that he makes something?" He answers, "Certainly not, he merely imitates."
|
||||
It is commonly argued that the notion of "creativity" originated in Western cultures through Christianity, as a matter of divine inspiration. According to scholars, the earliest Western conception of creativity was the Biblical story of the creation given in Genesis. However, this is not creativity in the modern sense, which did not arise until the Renaissance. In the Judeo-Christian-Islamic tradition, creativity was the sole province of God; humans were not considered to have the ability to create something new except as an expression of God's work. A similar concept existed in Greek culture, where the Muses were seen as mediating inspiration from the gods. Romans and Greeks invoked the concept of an external creative "daemon" (Greek) or "genius" (Latin), linked to the sacred or the divine. However, none of these views are similar to the modern concept of creativity, and the rejection of creativity in favor of discovery and the belief that individual creation was a conduit of the divine would dominate the West until the Renaissance and even later.
|
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|
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|
||||
=== Renaissance ===
|
||||
It was during the Renaissance that creativity was first conceived not as a conduit from the divine, but as arising from the abilities of "great men." This could be attributed to the leading intellectual movement of the time, aptly named humanism, which developed an intensely anthropocentric outlook on the world, valuing the intellect and achievement of the individual. From this philosophy arose the Renaissance man (or polymath), an individual who embodies the principles of humanism in their ceaseless courtship with knowledge and creation. One of the most well-known and immensely accomplished examples is Leonardo da Vinci.
|
||||
|
||||
=== From the 17th to the 19th centuries ===
|
||||
However, the shift from divine inspiration to the abilities of the individual was gradual and would not become immediately apparent until the Age of Enlightenment. By the 18th century, creativity (notably in aesthetics) linked with the concept of imagination became more frequent. In the writing of Thomas Hobbes, imagination became a key element of human cognition. William Duff was one of the first to identify imagination as a quality of genius, typifying the separation being made between talent (productive, but not breaking new ground) and genius.
|
||||
As an independent topic of study, creativity received little attention until the 19th century. Psychologist Mark Runco and Robert Albert argue that creativity as the subject of proper study began seriously to emerge in the late 19th century with the increased interest in individual differences inspired by the arrival of Darwinism. In particular, they refer to the work of Francis Galton, who, through his eugenicist outlook, took a keen interest in the heritability of intelligence, with creativity taken as an aspect of genius.
|
||||
|
||||
=== Modern ===
|
||||
In the late 19th and early 20th centuries, leading mathematicians and scientists such as Hermann von Helmholtz (1896) and Henri Poincaré (1908) began to reflect on and publicly discuss their creative processes. The insights of Poincaré and von Helmholtz inspired the accounts of the creative process by pioneering theorists such as Graham Wallas and Max Wertheimer. In his work Art of Thought, published in 1926, Wallas presented one of the first models of the creative process. In the Wallas model, creative insights and illuminations may be explained by a process consisting of five stages:
|
||||
|
||||
preparation (preparatory work on a problem that focuses the individual's mind on the problem and explores the problem's dimensions),
|
||||
incubation (in which the problem is internalized into the unconscious mind although nothing appears externally to be happening),
|
||||
intimation (the creative person gets a "feeling" that a solution is on its way),
|
||||
illumination or insight (in which the creative idea bursts forth from its preconscious processing into conscious awareness);
|
||||
verification (in which the idea is consciously verified, elaborated, and then applied).
|
||||
Wallas's model is also often treated as four stages, with "intimation" seen as a sub-stage.
|
||||
Wallas considered creativity to be a legacy of the evolutionary process, which allowed humans to quickly adapt to rapidly changing environments. Simonton provides an updated perspective on this view in his book, Origins of Genius: Darwinian Perspectives on Creativity.
|
||||
In 1927, mathematician and philosopher Alfred North Whitehead gave the Gifford Lectures at the University of Edinburgh, later published as Process and Reality. He is credited with having coined the term "creativity" to serve as the ultimate category of his metaphysical scheme.
|
||||
Although psychometric studies of creativity had been conducted by The London School of Psychology as early as 1927 with the work of H.L. Hargreaves into the Faculty of Imagination. The formal psychometric measurement of creativity, from the standpoint of orthodox psychological literature, is usually considered to have begun with psychologist J.P. Guilford's address to the American Psychological Association in 1950. That address helped to popularize the study of creativity and to focus attention on scientific approaches to conceptualizing creativity. Statistical analyzes led to the recognition of creativity as an aspect of human cognition separate from IQ-type intelligence, under the study of which it had previously been subsumed. Guilford's work suggested that above a threshold level of IQ, the relationship between creativity and classically measured intelligence broke down.
|
||||
|
||||
=== Across cultures ===
|
||||
Creativity is viewed differently in different countries. For example, cross-cultural research centered in Hong Kong found that Westerners view creativity more in terms of the individual attributes of a person, such as their aesthetic taste, while Chinese people view creativity more in terms of the social influence of creative people (i.e. what they can contribute to society). Mpofu, et al., surveyed 28 African languages and found that 27 had no word which directly translated to "creativity", with Arabic being the exception. The linguistic relativity hypothesis (i.e. that language can affect thought) suggests that the lack of an equivalent word for "creativity" may affect the views of creativity among speakers of such languages. However, more research would be needed to establish this, and there is certainly no suggestion that this linguistic difference makes people any less—or more—creative. Nevertheless, it is true that there has been very little research on creativity in Africa and Latin America. Creativity has been more thoroughly researched in the northern hemisphere, but there are cultural differences between northern countries. In Scandinavia, creativity is seen as an individual attitude that helps people cope with life's challenges, while in Germany, creativity is seen more as a process that can be applied to help solve problems.
|
||||
|
||||
== Classification ==
|
||||
|
||||
=== "Four C" model ===
|
||||
Psychologists James Kaufman and Ronald Beghetto introduced a "four C" model of creativity. The four "C's" are:
|
||||
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|
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|
||||
==== Vandervert model ====
|
||||
Vandervert described how the brain's frontal lobes and the cognitive functions of the cerebellum collaborate to facilitate creativity and innovation. Vandervert's explanation rests on considerable evidence that all processes of working memory (responsible for processing all thought) are adaptively modeled for increased efficiency by the cerebellum. The cerebellum (consisting of 100 billion neurons, which is more than the in the entirety of the rest of the brain) also adaptively models all bodily movement for efficiency. The cerebellum's adaptive models of working memory processing are then fed back to especially frontal lobe working memory control processes, where creative and innovative thoughts arise. (Apparently, creative insight or the "aha" experience is then triggered in the temporal lobe.)
|
||||
According to Vandervert, the details of creative adaptation begin in "forward" cerebellar models, which are anticipatory/exploratory controls for movement and thought. These cerebellar processing and control architectures have been termed Hierarchical Modular Selection and Identification for Control (HMOSAIC). New, hierarchically-arranged levels of the cerebellar control architecture (HMOSAIC) develop as mental mulling in working memory is extended over time. These new levels of the control architecture are fed forward to the frontal lobes. Since the cerebellum adaptively models all movement and all levels of thought and emotion, Vandervert's approach helps explain creativity and innovation in sports, art, music, the design of video games, technology, mathematics, the child prodigy, and thought in general.
|
||||
Vandervert argues that when a person is confronted with a challenging new situation, visual-spatial working memory and speech-related working memory are decomposed and re-composed (fractionated) by the cerebellum and then blended in the cerebral cortex in an attempt to deal with the new situation. With repeated attempts to deal with challenging situations, the cerebro-cerebellar blending process continues to optimize the efficiency of how working memory deals with the situation or problem. He also argues that this is the same process (only involving visual-spatial working memory and pre-language vocalization) that led to the evolution of language in humans. Vandervert and Vandervert–Weathers have pointed out that this blending process, because it continuously optimizes efficiencies, constantly improves prototyping attempts toward the invention or innovation of new ideas, music, art, or technology. Prototyping, they argue, not only produces new products, it trains the cerebro-cerebellar pathways involved to become more efficient at prototyping itself. Furthermore, Vandervert and Vandervert-Weathers believe that this repetitive "mental prototyping", or mental rehearsal involving the cerebellum and the cerebral cortex, explains the success of the self-driven, individualized patterning of repetitions initiated by the teaching methods of the Khan Academy.
|
||||
The model proposed by Vandervert has, however, received incisive critique from several authors.
|
||||
|
||||
==== Flaherty model ====
|
||||
In 2005, Alice Flaherty presented a three-factor model of the creative drive. Drawing from evidence in brain imaging, drug studies, and lesion analysis, she described the creative drive as resulting from an interaction of the frontal lobes, the temporal lobes, and dopamine from the limbic system. The frontal lobes may be responsible for idea generation, and the temporal lobes for idea editing and evaluation. Abnormalities in the frontal lobe (such as depression or anxiety) generally decrease creativity, while abnormalities in the temporal lobe often increase creativity. High activity in the temporal lobe typically inhibits activity in the frontal lobe, and vice versa. High dopamine levels increase general arousal and goal-directed behaviors and reduce latent inhibition, with all three effects increasing the drive to generate ideas.
|
||||
|
||||
==== Lin and Vartanian model ====
|
||||
In 2018, Lin and Vartanian proposed a neuroeconomic framework that precisely describes norepinephrine's role in creativity and modulating large-scale brain networks associated with creativity. This framework describes how neural activity in different brain regions and networks, such as the default mode network, track utility or subjective values of ideas.
|
||||
|
||||
=== Economics ===
|
||||
Economic approaches to creativity have focused on three aspects – the impact of creativity on economic growth, methods of modeling markets for creativity, and the maximization of economic creativity (innovation).
|
||||
In the early 20th century, Joseph Schumpeter introduced the economic theory of creative destruction to describe the way in which old ways of doing things are destroyed and replaced by the new. Some economists (such as Paul Romer) view creativity as an important element in the recombination of elements to produce new technologies and products and, consequently, economic growth. Creativity leads to capital, and creative products are protected by intellectual property laws.
|
||||
Mark A. Runco and Daniel Rubenson have tried to describe a "psychoeconomic" model of creativity. In such a model, creativity is the product of endowments and active investments in creativity; the costs and benefits of bringing creative activity to market determine the supply of creativity. Such an approach has been criticized for its view of creativity consumption as always having positive utility, and for the way it prematurely analyzes the value of future innovations.
|
||||
In his 2002 book, The Rise of the Creative Class, economist Richard Florida popularized the notion that regions with "3 T's of economic development: Technology, Talent, and Tolerance" also have high concentrations of creative professionals and tend to have a higher level of economic development.
|
||||
|
||||
=== Sociology ===
|
||||
Creativity research for most of the twentieth century was dominated by psychology and business studies, with little work done in sociology. Since the turn of the millennium, there has been more attention paid by sociological researchers, but sociology has yet to establish creativity as a specific research field, with reviews of sociological research into creativity a rarity in high-impact literature.
|
||||
While psychology has tended to focus on the individual as the locus of creativity, sociological research is directed more at the structures and context within which creative activity takes place, primarily based in sociology of culture, which finds its roots in the works of Marx, Durkheim, and Weber. This has meant a focus on the cultural and creative industries as sociological phenomena. Such research has covered a variety of areas, including the economics and production of culture, the role of creative industries in development, and the rise of the "creative class".
|
||||
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|
||||
|
||||
=== Education ===
|
||||
For those who view the conventional system of schooling as stifling creativity, an emphasis is made (particularly in the preschool/kindergarten and early school years) to provide a creativity-friendly, rich, imagination-fostering environment for young children. Researchers have seen this as important because technology is advancing at an unprecedented rate and creative problem-solving will be needed to cope with these challenges as they arise. In addition to helping with problem solving, creativity also helps students identify problems where others have failed to do so. The Waldorf School is an example of an education program that promotes creative thought.
|
||||
Promoting intrinsic motivation and problem solving are two areas where educators can foster creativity in students. Students are more creative when they see a task as intrinsically motivating, valued for its own sake. To promote creative thinking, educators need to identify what motivates their students and to structure teaching around it. Providing students with a choice of activities allows them to become more intrinsically motivated and therefore creative in completing the tasks.
|
||||
Teaching students to solve problems that do not have well-defined answers is another way to foster their creativity. This is accomplished by allowing students to explore problems and redefine them, possibly drawing on knowledge that at first may seem unrelated to the problem in order to solve it. In adults, mentoring individuals is another way to foster their creativity. However, the benefits of mentoring creativity apply only to creative contributions considered great in a given field, not to everyday creative expression.
|
||||
Musical creativity is a gateway to the flow state, which is conducive to spontaneity, improvisation, and creativity. Studies show that it is beneficial to emphasize students' creative side and integrate more creativity into their curriculums, with a notable strategy being through music. One reason for this is that students are able to express themselves through musical improvisation in a way that taps into higher order brain regions, while connecting with their peers and allowing them to go beyond typical pattern generation. In this sense, improvisation is a form of self-expression that can generate connectivity between peers and surpass the age-old rudimentary aspects of school.
|
||||
|
||||
==== Scotland ====
|
||||
In the Scottish education system, creativity is identified as a core skillset for learning, life, and work, and is defined as "a process which generates ideas that have value to the individual. It involves looking at familiar things with a fresh eye, examining problems with an open mind, making connections, learning from mistakes, and using imagination to explore new possibilities." The need to develop a shared language and understanding of creativity and its role across every aspect of learning, teaching, and continuous improvement was identified as a necessary aim; and a set of four skills is used to allow educators to discuss and develop creativity across all subjects and sectors of education – curiosity, open-mindedness, imagination, and problem solving. Distinctions are made between creative learning (when learners are using their creativity skills), creative teaching (when educators are using their own creativity skills), and creative change (when creativity skills are applied to planning and improvement). Scotland's national Creative Learning Plan supports the development of creativity skills in all learners and of educators' expertise in developing creativity skills. A range of resources has been created to support and assess this, including a national review of creativity learning by Her Majesty's Inspectorate for Education.
|
||||
|
||||
==== China ====
|
||||
China recognizes that creativity is crucial for national security, social development, and generally benefitting the people. Measures have been proposed to enhance creative ability in the country.
|
||||
|
||||
==== European Union ====
|
||||
The European Union sees creativity as important for the development of basic skills, and has declared 2009 the Year of Creativity and Innovation. Countries such as France, Germany, Italy, and Spain have made the encouragement of creativity a part of their educational and economic policies.
|
||||
|
||||
== Organizational creativity ==
|
||||
|
||||
Various research studies set out to establish that organizational effectiveness depends to a large extent on the creativity of the workforce. For any given organization, measures of effectiveness vary, depending upon the organization's mission, environmental context, nature of work, the product or service it produces, and customer demands. Thus, the first step in evaluating organizational effectiveness is to understand the organization itself – how it functions, how it is structured, and what it emphasizes.
|
||||
Similarly, social psychologists, organizational scientists, and management scientists (who research factors that influence creativity and innovation in teams and organizations) have developed integrative theoretical models that emphasize the elements of team composition, team processes, and organizational culture. These theoretical models also emphasize the mutually reinforcing relationships between those elements in promoting innovation.
|
||||
Research studies of the knowledge economy may be classified into three levels: macro, meso, and micro. Macro studies are at a societal or transnational level. Meso studies focus on organizations. Micro investigations center on the working of workers. There is also an interdisciplinary dimension when researching business, economics, education, human resource management, knowledge and organizational management, sociology, psychology, knowledge economy-related sectors – especially software, and advertising.
|
||||
|
||||
=== Organizational culture ===
|
||||
Supportive and motivational environments that create psychological safety, encourage risk-taking, and tolerate mistakes increase team creativity. Organizations in which help-seeking, help-giving, and collaboration are rewarded promote innovation by providing opportunities and contexts in which team processes that lead to collective creativity can occur. Additionally, leadership styles that downplay hierarchies or power differences within an organization, and empower people to speak up about their ideas or opinions, also help to create cultures that are conducive to creativity.
|
||||
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|
||||
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|
||||
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|
||||
|
||||
=== Team composition ===
|
||||
The diversity of team members' backgrounds and knowledge can increase team creativity by expanding the collection of unique information that is available to the team and by introducing different perspectives that can be integrated in novel ways. The Millennium Conferences on Creativity, a two-year-long Canadian review of the subject, for example, advocated for new linkages between the arts and science communities and targeted funding for multidisciplinary research. However, under some conditions, diversity can also decrease team creativity by making it more difficult for team members to communicate about ideas and causing interpersonal conflicts between those with different perspectives. Thus, the potential advantages of diversity must be supported by appropriate team processes and organizational cultures in order to enhance creativity.
|
||||
|
||||
=== Team processes ===
|
||||
Team communication norms, such as respecting others' expertise, paying attention to others' ideas, expecting information sharing, tolerating disagreements, negotiating, remaining open to others' ideas, learning from others, and building on each other's ideas, increase team creativity by facilitating the social processes involved with brainstorming and problem solving. Through these processes, team members can access their collective pool of knowledge, reach shared understandings, identify new ways of understanding problems or tasks, and make new connections between ideas. Engaging in these social processes also promotes positive team affect, which facilitates collective creativity.
|
||||
|
||||
=== Constraints ===
|
||||
|
||||
There is a long-standing debate on how material constraints (e.g., lack of money, materials, or equipment) affect creativity. In psychological and managerial research, there are two competing views. In one view, scholars propose a negative effect of material constraints on innovation and claim that material constraints starve creativity. Proponents argue that adequate material resources are needed to engage in creative activities such as experimenting with new solutions and idea exploration. In an opposing view, scholars assert that people tend to stick to established routines or solutions as long as they are not forced to deviate from them by constraints. For example, material constraints facilitated the development of jet engines in World War II.
|
||||
To reconcile these competing views, contingency models were proposed. The rationale behind these models is that certain contingency factors (e.g., creativity climate or creativity-relevant skills) influence the relationship between constraints and creativity. These contingency factors reflect the need for higher levels of motivation and skills when working on creative tasks under constraints. Depending on these contingency factors, there is either a positive or negative relationship between constraints and creativity.
|
||||
|
||||
== Fostering creativity ==
|
||||
|
||||
Several researchers have proposed methods of increasing a person's creativity. Such ideas range from the psychological-cognitive—such as the Osborn-Parnes Creative Problem Solving Process, Synectics, science-based creative thinking, Purdue Creative Thinking Program, and Edward de Bono's lateral thinking—to the highly structured—such as TRIZ (the Theory of Inventive Problem-Solving) and its variant Algorithm of Inventive Problem Solving (developed by the Russian scientist Genrich Altshuller), and Computer-Aided morphological analysis.
|
||||
An empirical synthesis, of which methods work best in enhancing creativity, was published by Haase et al. Summarising the results of 84 studies, the authors found that complex training courses, meditation, and cultural exposure were most effective in enhancing creativity, while the use of cognitive-manipulation drugs was noneffective.
|
||||
|
||||
=== Need for closure ===
|
||||
Experiments suggest the need for closure of task participants, whether as a reflection of personality or induced (through time pressure), negatively impacts creativity. Accordingly, it has been suggested that reading fiction, which can reduce the cognitive need for closure, may encourage creativity.
|
||||
|
||||
== Malevolent creativity ==
|
||||
|
||||
"Malevolent creativity" is the "dark side" of creativity. This type of creativity is not typically accepted within society and is defined by the intention to cause harm to others through original and innovative means. While it is often associated with criminal behavior, it can also be observed in ordinary day-to-day life as lying, cheating, and betrayal.
|
||||
Malevolent creativity should be distinguished from negative creativity in that negative creativity may unintentionally cause harm to others, whereas malevolent creativity is malevolently motivated.
|
||||
|
||||
=== Crime ===
|
||||
Malevolent creativity is a key contributor to crime and in its most destructive form can even manifest as terrorism. As creativity requires deviating from the conventional, there is permanent tension between being creative and going too far—in some cases to the point of breaking the law. Aggression is a key predictor of malevolent creativity, and increased levels of aggression correlate with a higher likelihood of committing crime.
|
||||
|
||||
=== Predictive factors ===
|
||||
Although everyone shows some levels of malevolent creativity under certain conditions, those that have a higher propensity towards it have increased tendencies to deceive and manipulate others for their own gain. While malevolent creativity appears to dramatically increase when an individual is treated unfairly, personality, particularly aggressiveness, is also a key predictor in anticipating levels of malevolent thinking. Researchers Harris and Reiter-Palmon investigated the role of aggression in levels of malevolent creativity, in particular levels of implicit aggression and the tendency to employ aggressive actions in response to problem solving. The personality traits of physical aggression, conscientiousness, emotional intelligence, and implicit aggression all seem to be related with malevolent creativity. Harris and Reiter-Palmon's research showed that when subjects were presented with a problem that designed to trigger malevolent creativity, participants high in implicit aggression and low in premeditation expressed the largest number of malevolently themed solutions. When presented with the more benign problem designed to trigger prosocial motives of helping others and cooperating, those high in implicit aggression, even if they tended to be highly impulsive, were far less destructive in their imagined solutions. The researchers concluded premeditation, more than implicit aggression, controlled an individual's expression of malevolent creativity.
|
||||
The current measure for malevolent creativity is the 13-item Malevolent Creativity Behaviour Scale (MCBS).
|
||||
|
||||
== Academic journals ==
|
||||
|
||||
== See also ==
|
||||
|
||||
== References ==
|
||||
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||||
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|
||||
|
||||
== Further reading ==
|
||||
Rick Rubin The creative act, A. (2023). The Creative Act: A Way of Being. Penguin Press. ISBN 9780593652886.
|
||||
Craft, A. (2005). Creativity in Schools: tensions and dilemmas. Routledge. ISBN 978-0-415-32414-4.
|
||||
Gielen, P. (2013). Creativity and other Fundamentalisms. Amsterdam: Mondriaan.
|
||||
Glăveanu, Vlad Petre, ed. (2019). The Creativity Reader. Oxford University Press. ISBN 978-0-19-084171-3.
|
||||
Hadamard, Jacques (2017) [1945]. The Psychology of Invention in the Mathematical Field. Dover. ISBN 978-0-486-20107-8.
|
||||
Jeffery, Graham (2005). The Creative College: building a successful learning culture in the arts. Trentham Books.
|
||||
Johnson, D.M. (1972). Systematic introduction to the psychology of thinking. Harper & Row.
|
||||
Jullien, F. (2004). In Praise of Blandness: Proceeding from Chinese Thought and Aesthetics. Translated by Varsano, Paula M. Zone Books, U.S. ISBN 1-890951-41-2.
|
||||
Jung, Carl G. (1981). The Collected Works of C. G. Jung. Vol. 8. The Structure and Dynamics of the Psyche. Princeton University Press. ISBN 0-691-09774-7.
|
||||
Kanigel, Robert (1992). The Man Who Knew Infinity: A Life of the Genius Ramanujan. Washington Square Press. ISBN 0-671-75061-5.
|
||||
Kolp, P.; Lammé, A.; Regnard, Fr. (2009). Rens, J.M. (ed.). "Musique et créativité". Orphée Apprenti. NS (1): 9–119. D/2009/11848/5
|
||||
Kounios, John; Kounios, Yvette (1 March 2025). "The Wonder of Insight: Scientists are finally getting a grasp on the aha! moment-how and when it happens and why it matters". Scientific American. 332 (3): 20. doi:10.1038/scientificamerican032025-3KjUpLkekpevPhRqrCGA6R. PMID 39964892.
|
||||
Kraft, U. (2005). "Unleashing Creativity". Scientific American Mind. April: 16–23. doi:10.1038/scientificamericanmind0405-16.
|
||||
Lehrer, Jonah (2012). Imagine: How Creativity Works.
|
||||
McLaren, R.B. (1999). "Dark Side of Creativity". In Runco, M.A.; Pritzker, S.R. (eds.). Encyclopedia of Creativity. Academic Press.
|
||||
McCrae, R.R. (1987). "Creativity, Divergent Thinking, and Openness to Experience". Journal of Personality and Social Psychology. 52 (6): 1258–1265. doi:10.1037/0022-3514.52.6.1258.
|
||||
Michalko, M. (1998). Cracking Creativity: The Secrets of Creative Genius. Berkeley, Calif.: Ten Speed Press. ISBN 978-0-89815-913-4.
|
||||
National Academy of Engineering (2005). Educating the engineer of 2020: adapting engineering education to the new century. National Academies Press. ISBN 978-0-309-09649-2.
|
||||
Runco, M.A. (2004). "Creativity". Annual Review of Psychology. 55: 657–687. doi:10.1146/annurev.psych.55.090902.141502. PMID 14744230.
|
||||
Sabaneev, Leonid (July 1928). "The Psychology of the Musico-Creative Process". Psyche. 9: 37–54.
|
||||
Smith, S.M.; Blakenship, S.E. (1 April 1991). "Incubation and the persistence of fixation in problem solving". American Journal of Psychology. 104 (1): 61–87. doi:10.2307/1422851. JSTOR 1422851. PMID 2058758.=
|
||||
Stix, Gary (February 2025). "Wiki-Curious". Scientific American. 332 (2): 18. Bibcode:2025SciAm.332b..18S. doi:10.1038/scientificamerican022025-5jyVTYEuXUu0ChH7jK7DC2. PMID 39836581.
|
||||
Taylor, C.W. (1988). "Various approaches to and definitions of creativity". In Sternberg, R.J. (ed.). The nature of creativity: Contemporary psychological perspectives. Cambridge University Press.
|
||||
von Franz, Marie-Louise (1992). Psyche and Matter. Shambhala. ISBN 0-87773-902-1.
|
||||
|
||||
== External links ==
|
||||
The dictionary definition of creativity at Wiktionary
|
||||
Quotations related to Creativity at Wikiquote
|
||||
43
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|
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T07:11:10.173371+00:00"
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|
||||
---
|
||||
|
||||
mini-c ("transformative learning" involving "personally meaningful interpretations of experiences, actions, and insights").
|
||||
little-c (everyday problem-solving and creative expression).
|
||||
Pro-C (exhibited by people who are professionally or vocationally creative, though not necessarily eminent).
|
||||
Big-C (creativity considered great in a given field).
|
||||
This model was intended to help accommodate models and theories of creativity that stressed competence as an essential component and a historic transformation of a creative domain as the highest mark of creativity. It also, the authors argued, made a useful framework for analyzing creative processes in individuals.
|
||||
The contrast signified by the terms "Big C" and "little C" has been widely used. Kozbelt, Beghetto, and Runco used a little-c/Big-C model to review major theories of creativity. Margaret Boden distinguished between h-creativity (historical) and p-creativity (personal).
|
||||
Ken Robinson and Anna Craft focused on creativity in a general population, particularly with respect to education. Craft makes a similar distinction between "high" and "little c" creativity and cites Robinson as referring to "high" and "democratic" creativity. Mihaly Csikszentmihalyi defined creativity in terms of individuals judged to have made significant creative and perhaps domain-changing contributions. Simonton analyzed the career trajectories of eminent creative people in order to map patterns and predictors of creative productivity.
|
||||
|
||||
=== "Four P's" aspects ===
|
||||
Theories of creativity (and empirical investigations of why some people are more creative than others) have focused on a variety of aspects. The dominant factors are usually identified as "the four P's," a framework first put forward by Mel Rhodes:
|
||||
|
||||
Process
|
||||
A focus on process is shown in cognitive approaches that try to describe thought mechanisms and techniques for creative thinking. Theories invoking divergent rather than convergent thinking (such as that of Guilford), or those describing the staging of the creative process (such as that of Wallas) are primarily theories of the creative process.
|
||||
Product
|
||||
A focus on a creative product usually attempts to assess creative output, whether for psychometrics (see below) or to understand why some objects are considered creative. It is from a consideration of product that the standard definition of creativity as the production of something both novel and useful arises.
|
||||
Person
|
||||
A focus on the nature of the creative person considers more general intellectual habits, such as openness, levels of ideation, autonomy, expertise, exploratory behavior, and so on.
|
||||
Press and place
|
||||
A focus on place (or press) considers the circumstances in which creativity flourishes, such as degrees of autonomy, access to resources, and the nature of gatekeepers. Creative lifestyles are characterized by nonconforming attitudes and behaviors, as well as flexibility.
|
||||
|
||||
=== "Five A's" aspects ===
|
||||
In 2013, based on a sociocultural critique of the Four-P's model as individualistic, static, and decontextualized, psychology professor and author Vlad Petre Glăveanu proposed a "Five A's" model consisting of actor, action, artifact, audience, and affordance. In this model, the actor is the person with attributes but who is also located within social networks; action is the process of creativity not only in internal cognitive terms but also external, bridging the gap between ideation and implementation; artifacts emphasize how creative products typically represent cumulative innovations over time rather than abrupt discontinuities; and "press/place" is divided into audience and affordance, which consider the interdependence of the creative individual with the social and material world, respectively. Although not supplanting the Four P's model in creativity research, the Five A's model has exerted influence over the direction of some creativity research, and has been credited with bringing coherence to studies across a number of creative domains.
|
||||
|
||||
== Process theories ==
|
||||
There has been significant research conducted in the fields of psychology and cognitive science towards better understanding the processes by which creativity occurs. The results of these studies have led to several possible explanations of the sources and methods of creativity.
|
||||
|
||||
=== Incubation ===
|
||||
|
||||
"Incubation" is a temporary break from creative problem solving that can result in insight. Empirical research has investigated whether, as the concept of "incubation" in Wallas's model implies, a period of interruption or rest from a problem may aid creative problem-solving. Early work proposed that creative solutions to problems arise mysteriously from the unconscious mind while the conscious mind is occupied with other tasks. This hypothesis is included in Csikszentmihalyi's five-phase model of the creative process, which describes incubation as a time when one's unconscious takes over. This was supposed to allow for unique connections to be made without the conscious mind trying to make logical order out of the problem.
|
||||
Ward listed various hypotheses that have been advanced to explain why incubation may aid creative problem-solving and notes how some empirical evidence is consistent with a different hypothesis: incubation aids creative problems in that it enables "forgetting" of misleading clues. The absence of incubation may lead the problem solver to become fixated on inappropriate problem-solving strategies.
|
||||
|
||||
=== Divergent thinking ===
|
||||
J. P. Guilford drew a distinction between convergent and divergent production, or convergent and divergent thinking. Convergent thinking involves aiming for a single, correct, or best solution to a problem (e.g. "How can we get a crewed rocket to land on the moon safely and within budget?"). Divergent thinking, on the other hand, involves the creative generation of multiple answers to an open-ended prompt (e.g. "How can a chair be used?"). Divergent thinking is sometimes used as a synonym for creativity in psychological literature or is considered the necessary precursor to creativity. However, as Runco pointed out, there is a clear distinction between creative thinking and divergent thinking. Creative thinking focuses on the production, combination, and assessment of ideas to formulate something new and unique, while divergent thinking focuses on conceiving a variety of ideas that are not necessarily new or unique. Other researchers have occasionally used the terms flexible thinking or fluid intelligence, which are also roughly similar to (but not synonymous with) creativity. While convergent and divergent thinking differ greatly in terms of approach to problem solving, it is believed that both are employed to some degree in solving most real-world problems.
|
||||
39
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|
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|
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category: "reference"
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tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:10.173371+00:00"
|
||||
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|
||||
---
|
||||
|
||||
=== Geneplore model ===
|
||||
In 1992, Finke, et al., proposed the "Geneplore model", in which creativity takes place in two phases: a generative phase, where an individual constructs mental representations called "preinventive" structures, and an exploratory phase where those structures are used to come up with creative ideas. Some evidence shows that when people use their imagination to develop new ideas, those ideas are structured in predictable ways in accordance with properties of existing categories and concepts. Weisberg argued, in contrast, that creativity involves ordinary cognitive processes yielding extraordinary results.
|
||||
|
||||
=== Explicit–implicit interaction theory ===
|
||||
Helie and Sun proposed a framework for understanding creativity in problem solving, namely the explicit–implicit interaction (EII) theory of creativity. This theory attempts to provide a more unified explanation of relevant phenomena (in part by reinterpreting/integrating various fragmentary existing theories of incubation and insight).
|
||||
The EII theory relies mainly on five basic principles:
|
||||
|
||||
co-existence of, and the difference between, explicit and implicit knowledge
|
||||
simultaneous involvement of implicit and explicit processes in most tasks
|
||||
redundant representation of explicit and implicit knowledge
|
||||
integration of the results of explicit and implicit processing
|
||||
iterative (and possibly bidirectional) processing
|
||||
A computational implementation of the theory was developed based on the CLARION cognitive architecture and used to simulate relevant human data. This work is an initial step in the development of process-based theories of creativity, encompassing incubation, insight, and various other related phenomena.
|
||||
|
||||
=== Conceptual blending ===
|
||||
|
||||
In The Act of Creation, Arthur Koestler introduced the concept of "bisociation" – that creativity arises as a result of the intersection of two quite different frames of reference. In the 1990s, various approaches in cognitive science that dealt with metaphor, analogy, and structure mapping converged, and a new integrative approach to the study of creativity in science, art, and humor emerged under the label conceptual blending.
|
||||
|
||||
=== Honing theory ===
|
||||
Honing theory, developed principally by psychologist Liane Gabora, posits that creativity arises due to the self-organizing, self-mending nature of a worldview. The creative process is a way by which the individual hones (and re-hones) an integrated worldview. Honing theory places emphasis not only on the externally visible creative outcome but also on the internal cognitive restructuring and repair of the worldview brought about by the creative process. When one is faced with a creatively demanding task, there is an interaction between one's conception of the task and one's worldview. The conception of the task changes through interaction with the worldview, and the worldview changes through interaction with the task. This interaction is reiterated until the task is complete, at which point the task is conceived of differently and the worldview is subtly or drastically transformed, following the natural tendency of a worldview to attempt to resolve dissonance and seek internal consistency amongst its components, whether they be ideas, attitudes, or bits of knowledge. Dissonance in a person's worldview is, in some cases, generated by viewing their peers' creative outputs, and, so, people pursue their own creative endeavors to restructure their worldviews and reduce dissonance. This shift in worldview and cognitive restructuring through creative acts has also been considered as a way to explain possible benefits of creativity for mental health. The theory also addresses challenges not addressed by other theories of creativity, such as the factors guiding restructuring and the evolution of creative works.
|
||||
A central feature of honing theory is the notion of a "potentiality state". Honing theory posits that creative thought proceeds not by searching through and randomly "mutating" predefined possibilities but by drawing upon associations that exist due to overlap in the distributed neural-cell assemblies that participate in the encoding of experiences in memory. Midway through the creative process, one may have made associations between the current task and previous experiences but not yet disambiguated which aspects of those previous experiences are relevant to the current task. Thus, the creative idea may feel "half-baked". At that point, it can be said to be in a potentiality state, because how it will actualize depends on the different internally or externally generated contexts it interacts with.
|
||||
Honing theory is held to explain certain phenomena not dealt with by other theories of creativity—for example, how different works by the same creator exhibit a recognizable style or "voice" even in different creative outlets. This is not predicted by theories of creativity that emphasize chance processes or the accumulation of expertise, but it is predicted by honing theory, according to which personal style reflects the creator's uniquely structured worldview. Another example is the environmental stimulus for creativity. Creativity is commonly considered to be fostered by a supportive, nurturing, and trustworthy environment conducive to self-actualization. In line with this idea, Gabora posits that creativity is a product of culture and that our social interactions evolve our culture in way that promotes creativity.
|
||||
|
||||
=== Everyday imaginative thought ===
|
||||
|
||||
In everyday thought, people often spontaneously imagine alternatives to reality when they think "if only...". Their counterfactual thinking is viewed as an example of everyday creative processes. It has been proposed that the creation of counterfactual alternatives to reality depends on cognitive processes that are similar to rational thought.
|
||||
Imaginative thought in everyday life can be categorized based on whether it involves perceptual or motor-related mental imagery, novel combinatorial processing, or altered psychological states. This classification aids in understanding the neural foundations and practical implications of imagination.
|
||||
Creative thinking is a central aspect of everyday life, encompassing both controlled and undirected processes. This includes divergent thinking and stage models, highlighting the importance of extra- and meta-cognitive contributions to imaginative thought.
|
||||
Brain-network dynamics play a crucial role in creative cognition. The default and executive control networks in the brain cooperate during creative tasks, suggesting a complex interaction between these networks in facilitating everyday imaginative thought.
|
||||
24
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||||
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|
||||
title: "Creativity"
|
||||
chunk: 5/14
|
||||
source: "https://en.wikipedia.org/wiki/Creativity"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:10.173371+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
=== Dialectical theory ===
|
||||
The term "dialectical theory of creativity" dates back to psychoanalyst Daniel Dervin and was later developed into an interdisciplinary theory. This theory starts with the ancient concept that creativity takes place in an interplay between order and chaos. Similar ideas can be found in neuroscience and psychology. Neurobiologically, it can be shown that the creative process takes place in a dynamic interplay between coherence and incoherence that leads to new and usable neuronal networks. Psychology shows how the dialectics of convergent and focused thinking with divergent and associative thinking leads to new ideas and products.
|
||||
Personality traits such as the "Big Five" seem to bedialectically intertwined in the creative process: emotional instability versus stability, extraversion versus introversion, openness versus reserve, agreeableness versus antagonism, and disinhibition versus constraint. The dialectical theory of creativity also applies to counseling and psychotherapy.
|
||||
|
||||
=== Neuroeconomic framework ===
|
||||
Lin and Vartanian developed a neurobiological description of creative cognition. This interdisciplinary framework integrates theoretical principles and empirical results from neuroeconomics, reinforcement learning, cognitive neuroscience, and neurotransmission research on the locus coeruleus system. It describes how decision-making processes studied by neuroeconomists as well as activity in the locus coeruleus system underlie creative cognition and the large-scale brain network dynamics associated with creativity. It suggests that creativity is an optimization and utility maximization problem that requires individuals to determine the optimal way to exploit and explore ideas (e.g., the multi-armed bandit problem). This utility-maximization process is thought to be mediated by the locus coeruleus system, and this creativity framework describes how tonic and phasic locus coeruleus activities work in conjunction to facilitate the exploiting and exploring of creative ideas. This framework not only explains previous empirical results but also makes novel and falsifiable predictions at different levels of analysis (ranging from neurobiological to cognitive and personality differences).
|
||||
|
||||
=== Behaviorism theory ===
|
||||
B.F. Skinner attributed creativity to accidental behaviors that are reinforced by the environment. In behaviorism, creativity can be understood as novel or unusual behaviors that are reinforced if they produce a desired outcome. Spontaneous behaviors by living creatures are thought to reflect past learned behaviors. In this way, a behaviorist may say that prior learning caused novel behaviors to be reinforced many times over, and the individual has been shaped to produce increasingly novel behaviors. A creative person, according to this definition, is someone who has been reinforced more often for novel behaviors than others. Behaviorists suggest that anyone can be creative, they just need to be reinforced to learn to produce novel behaviors.
|
||||
|
||||
=== Investment theory ===
|
||||
The "investment theory of creativity" suggests that many individual and environmental factors must exist in precise ways for extremely high, as opposed to average, levels of creativity to result. In the "investment" sense, a person with their particular characteristics in their particular environment may see an opportunity to devote their time and energy to something that has been overlooked by others. The creative person develops an undervalued or under-recognized idea to the point where it is established as a new and creative idea. Just as in the financial world, some investments are worth the buy-in, while others are less productive and do not generate returns to the extent that the investor expected. This "investment theory of creativity" asserts that creativity might rely to some extent on the right investment of effort being added to a field at the right time in the right way.
|
||||
|
||||
=== Computational creativity ===
|
||||
31
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|
||||
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|
||||
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|
||||
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|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:10.173371+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Jürgen Schmidhuber's formal theory of creativity postulates that creativity, curiosity, and interestingness are by-products of a simple computational principle for measuring and optimizing learning progress.
|
||||
Consider an agent able to manipulate its environment and thus its own sensory inputs. The agent can use a black box optimization method such as reinforcement learning to learn, through informed trial and error, sequences of actions that maximize the expected sum of its future reward signals. There are extrinsic reward signals for achieving externally given goals, such as finding food when hungry. But for Schmidhuber's objective function to be maximized also includes an additional, intrinsic term to model "wow-effects". This non-standard term motivates purely creative behavior of the agent, even when there are no external goals.
|
||||
A wow-effect is formally defined as follows: as the agent is creating and predicting and encoding the continually growing history of actions and sensory inputs, it keeps improving the predictor or encoder, which can be implemented as an artificial neural network, or some other machine learning device, that can exploit regularities in the data to improve its performance over time. The improvements can be measured precisely, by computing the difference in computational costs (storage size, number of required synapses, errors, time) needed to encode new observations before and after learning. This difference depends on the encoder's present subjective knowledge, which changes over time, but the theory formally takes this into account. The cost difference measures the strength of the present wow-effect due to sudden improvements in data compression or computational speed. It becomes an intrinsic reward signal for the action selector. The objective function thus motivates the action optimizer to create action sequences that cause more wow-effects.
|
||||
Irregular, random data (or noise) do not permit any wow-effects or learning progress, and thus are "boring" by nature (providing no reward). Already-known and predictable regularities also are boring. Temporarily interesting are only the initially unknown, novel, regular patterns in both actions and observations. This motivates the agent to perform continual, open-ended, active, creative exploration.
|
||||
Schmidhuber's work is highly influential in intrinsic motivation, which has emerged as a research topic in the study of artificial intelligence and robotics.
|
||||
According to Schmidhuber, his objective function explains the activities of scientists, artists, and comedians. For example, physicists are motivated to create experiments leading to observations that obey previously unpublished physical laws, permitting better data compression. Likewise, composers receive intrinsic reward for creating non-arbitrary melodies with unexpected but regular harmonies that permit wow-effects through data compression improvements. Similarly, a comedian gets an intrinsic reward for "inventing a novel joke with an unexpected punch line, related to the beginning of the story in an initially unexpected but quickly learnable way that also allows for better compression of the perceived data."
|
||||
Schmidhuber augured that computer hardware advances would greatly scale up rudimentary artificial scientists and artists. He used the theory to create low-complexity art and an attractive human face.
|
||||
|
||||
== Personal assessment ==
|
||||
|
||||
|
||||
=== Psychometric approaches ===
|
||||
|
||||
==== History ====
|
||||
J. P. Guilford's group, which pioneered the modern psychometric study of creativity, constructed several performance-based tests to measure creativity in 1967, including asking participants to write original titles for a story with a given plot, asking participants to come up with unusual uses for everyday objects such as bricks, and asking participants to generate a list of consequences of unexpected events, such as the loss of gravity. Guilford was trying to create a model for intellect as a whole, but in doing so, he also created a model for creativity. Guilford assumed that creativity was not an abstract concept, which was an important assumption needed for creativity research. The idea that creativity was a category, rather than a single concept, enabled other researchers to look at creativity from a new perspective.
|
||||
Additionally, Guilford hypothesized one of the first models that specified the components of creativity. He explained that creativity was a result of having three qualities: the ability to recognize problems, "fluency", and "flexibility". "Fluency" encompassed "ideational fluency", or the ability to rapidly produce a variety of ideas fulfilling stated requirements; "associational fluency", or the ability to generate a list of words associated with a given word; and "expressional fluency", or the ability to organize words into larger units such as phrases, sentences, and paragraphs. "Flexibility" encompassed both "spontaneous flexibility", or the general ability to be flexible, and "adaptive flexibility", or the ability to produces responses that are novel and of high quality.
|
||||
This represents the base model which several researchers would alter to produce their own new theories of creativity years later. Building on Guilford's work, tests were developed, sometimes called "divergent thinking" (DT) tests, which have been both praised and criticized. One example is the Torrance Tests of Creative Thinking developed in 1966. These test set forth tasks requiring divergent thinking, as well as other problem-solving skills, the tests being scored according to four categories: "fluency", the total number of meaningful, and relevant, ideas generated; "flexibility", the number of different categories of responses; "originality", the statistical rarity of the responses; and "elaboration", the amount of detail given.
|
||||
|
||||
==== Computer scoring ====
|
||||
Considerable progress has been made in the automated scoring of divergent-thinking tests, using a semantic approach. When compared to human raters, natural language processing (NLP) techniques are reliable and valid for the scoring of originality. Computer programs were able to achieve a correlation to human graders of 0.60 and 0.72.
|
||||
Semantic networks also devise originality scores that yield significant correlations with socio-personal measures. A team of researchers led by James C. Kaufman and Mark A. Runco combined expertise in creativity research, natural language processing, computational linguistics, and statistical data analysis to devise a scalable system for computerized automated testing: the SparcIt Creativity Index Testing system. This system enabled automated scoring of DT tests that is reliable, objective, and scalable, thus addressing most of the issues of DT tests that had been found and reported. The resultant computer system was able to achieve a correlation to human graders of 0.73.
|
||||
32
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||||
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|
||||
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|
||||
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|
||||
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|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:10.173371+00:00"
|
||||
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|
||||
---
|
||||
|
||||
=== Social-personality approaches ===
|
||||
Researchers have taken a social-personality approach by using personality traits such as independence of judgement, self-confidence, attraction to complexity, aesthetic orientation, and risk-taking as measures of personal creativity. Within the framework of the Big Five personality traits, a consistent few of these traits have emerged as being correlated to creativity. Openness to experience is consistently related to a host of different assessments of creativity. Investigation of the other Big Five traits has demonstrated subtle differences between different domains of creativity. Compared to non-artists, artists tend to have higher levels of openness to experience and lower levels of conscientiousness, while scientists are more open to experience, conscientious, and higher in the confidence-dominance facets of extraversion compared to non-scientists.
|
||||
|
||||
=== Self-reporting questionnaires ===
|
||||
Biographical methods use quantitative characteristics, such as the number of publications, patents, or artistic performances that can be credited to a person. While this method was originally developed for highly creative personalities, today it is also available as self-report questionnaires supplemented with frequent, less outstanding creative behaviors such as writing a short story or creating recipes. The self-report questionnaire most frequently used in research is the Creative Achievement Questionnaire, a self-report test that measures creative achievement across ten domains, which was described in 2005 and shown to be reliable when compared to other measures of creativity and to independent evaluations of creative output.
|
||||
|
||||
== Factors ==
|
||||
|
||||
=== Intelligence ===
|
||||
The potential relationship between creativity and intelligence has been of interest since the last half of the twentieth century, when many influential studies extensively studied both. This joint focus highlighted both the theoretical and practical importance of the relationship: researchers were interested in not only if the two qualities were related, but also how and why.
|
||||
There are multiple theories accounting for their relationship, with there being three main theories. Threshold theory states that intelligence is a necessary, but not sufficient, condition for creativity, and that there is a moderate positive relationship between creativity and intelligence until IQ ~120. Certification theory states that creativity is not intrinsically related to intelligence. Instead, individuals are required to meet the requisite level of intelligence in order to gain a certain level of education or work, which in turn offers the opportunity to be creative. In this theory, displays of creativity are moderated by intelligence. Interference theory states, in contrast, that extremely high intelligence might interfere with creative ability.
|
||||
Sternberg and O'Hara proposed a different framework of five possible relationships between creativity and intelligence: that creativity was a subset of intelligence; that intelligence was a subset of creativity; that the two constructs overlapped; that they were both part of the same construct (coincident sets); or that they were distinct constructs (disjoint sets).
|
||||
|
||||
==== Creativity as a subset of intelligence ====
|
||||
A number of researchers include creativity, either explicitly or implicitly, as a key component of intelligence, for example:
|
||||
|
||||
Sternberg's Theory of Successful Intelligence includes creativity as a main component and comprises three sub-theories: contextual (analytic), contextual (practical), and experiential (creative). Experiential sub-theory—the ability to use pre-existing knowledge and skills to solve new and novel problems—is directly related to creativity.
|
||||
The Cattell–Horn–Carroll theory (CHC) includes creativity as a subset of intelligence, associated with the broad group factor of long-term storage and retrieval (Glr). Glr narrows abilities relating to creativity include ideational fluency, associational fluency, and originality/creativity. Silvia et al. conducted a study to look at the relationship between divergent thinking and verbal fluency tests and reported that both fluency and originality in divergent thinking were significantly affected by the broad-level Glr factor. Martindale extended the CHC-theory by proposing that people who are creative are also selective in their processing speed. Martindale argues that in the creative process, larger amounts of information are processed more slowly in the early stages, and as a person begins to understand the problem, the processing speed is increased.
|
||||
The Dual Process Theory of Intelligence posits a two-factor or type model of intelligence. Type 1 is a conscious process and concerns goal-directed thoughts. Type 2 is an unconscious process, and concerns spontaneous cognition, which encompasses daydreaming and implicit learning ability. Kaufman argues that creativity occurs as a result of Type 1 and Type 2 processes working together in combination. Each type in the creative process can be used to varying degrees.
|
||||
|
||||
==== Intelligence as a subset of creativity ====
|
||||
In this relationship model, intelligence is a key component in the development of creativity, for example:
|
||||
41
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|
||||
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|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Creativity"
|
||||
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|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:10.173371+00:00"
|
||||
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|
||||
---
|
||||
|
||||
Sternberg & Lubart's Investment Theory, using the metaphor of a stock market, demonstrates that creative thinkers are like good investors—they buy low and sell high (in their ideas). Like undervalued or low-valued stock, creative individuals generate unique ideas that are initially rejected by other people. The creative individual has to persevere and convince others of the idea's value. After convincing others, and thus increasing the idea's value, the creative individual "sells high" by leaving the idea with the other people and moving on to generate another idea. According to this theory, six distinct, but related elements contribute to successful creativity: intelligence, knowledge, thinking styles, personality, motivation, and environment. Intelligence is just one of the six factors that can, either solely or in conjunction with the other five factors, generate creative thoughts.
|
||||
Amabile's Componential Model of Creativity posits three within-individual components needed for creativity—domain-relevant skills, creativity-relevant processes, and task motivation—and one component external to the individual—their surrounding social environment. Creativity requires the confluence of all components. High creativity will result when a person is intrinsically motivated, possesses both a high level of domain-relevant skills and has high skills in creative thinking, and is working in a highly creative environment.
|
||||
The Amusement Park Theoretical Model is a four-step theory in which domain-specific and generalist views are integrated into a model of creativity. The researchers make use of the metaphor of the amusement park to demonstrate that, within each of the following creative levels, intelligence plays a key role:
|
||||
To get into the amusement park, there are initial requirements (e.g., time and transportation needed to go to the park). Initial requirements (such as intelligence) are necessary, but not sufficient for creativity. They are more like prerequisites for creativity, and if a person does not possess the basic level of the initial requirement (intelligence), then they will not be able to generate creative thoughts and behaviour.
|
||||
Secondly, there are the subcomponents—general thematic areas—that increase in specificity. Like choosing which type of amusement park to visit (e.g., a zoo or a water park), these areas relate to the areas in which someone could be creative (e.g., poetry).
|
||||
Thirdly, there are specific domains. After choosing the type of park to visit (e.g., if one chooses a waterpark, that person has to choose which specific park to go to). For example, within the poetry domain there are many different forms (e.g., free verse, riddles, sonnets, etc.).
|
||||
Lastly, there are micro-domains. These are the specific tasks that reside within each domain (e.g., individual rides at the waterpark equate to individual lines in a poem in free-verse).
|
||||
|
||||
==== Creativity and intelligence as overlapping yet distinct constructs ====
|
||||
These concepts posit creativity and intelligence as distinct, but intersecting constructs, for example:
|
||||
|
||||
In Renzulli's Three-Ring Conception of Giftedness, giftedness is an overlap of above-average intellectual ability, creativity, and task commitment. Under this view, creativity and intelligence are distinct constructs, but they overlap under the correct conditions.
|
||||
In the PASS theory of intelligence, the planning component—the ability to solve problems, make decisions, and take action—strongly overlaps with the concept of creativity.
|
||||
Threshold Theory (TT) derives from a number of previous research findings that suggested that a threshold exists in the relationship between creativity and intelligence—both constructs are moderately positively correlated up to an IQ of ~120. Above this threshold, if there is a relationship at all, it is small and weak. TT posits that a moderate level of intelligence is necessary for creativity.
|
||||
|
||||
==== Creativity and intelligence as coincident sets ====
|
||||
Under this view, researchers posit that there are no differences in the mechanisms underlying creativity from those used in normal problem solving, and in normal problem solving there is no need for creativity. Thus, creativity and intelligence (problem solving) are the same thing. Perkins referred to this as the "nothing-special" view.
|
||||
|
||||
==== Creativity and intelligence as disjoint sets ====
|
||||
In this view, creativity and intelligence are completely different, unrelated constructs. Along with the coincident set view, this is quite a rare position taken within the literature.
|
||||
|
||||
=== Affective influence ===
|
||||
Some theories suggest that creativity may be particularly susceptible to affective influence. The term "affect" in this context refers to liking or disliking key aspects of the subject in question. This work largely follows from findings in psychology regarding the ways in which affective states are involved in human judgment and decision-making.
|
||||
According to Alice Isen, positive affect has three primary effects on cognitive activity. First, it makes additional cognitive material available for processing, increasing the number of cognitive elements available for association. Second, it leads to defocused attention and a more complex cognitive context, increasing the breadth of those elements that are treated as relevant to the problem. Third, it increases cognitive flexibility, increasing the probability that diverse cognitive elements will in fact become associated. Together, these processes enable creativity.
|
||||
Barbara Fredrickson, in her broaden-and-build model, suggests that positive emotions, such as joy and love, broaden a person's available repertoire of cognitions and actions, thus enhancing creativity.
|
||||
According to these researchers, positive emotions increase the number of cognitive elements available for association (attention scope) and the number of elements that are relevant to the problem (cognitive scope). Day-by-day psychological experiences—including emotions, perceptions, and motivation—significantly impact creative performance. Creativity is higher when emotions and perceptions are more positive and when intrinsic motivation is stronger.
|
||||
Some meta-analyses, such as Baas, et al., (2008) analyzing 66 studies of creativity and affect, support the link between creativity and positive affect.
|
||||
|
||||
=== Mental health ===
|
||||
|
||||
Links have been identified between creativity and mood disorders, particularly manic-depressive disorder (a.k.a. bipolar disorder) and depressive disorder (a.k.a. unipolar disorder). However, different artists have described mental illness as having both positive and negative effects on their work. In general, people who have worked in the arts industry throughout history have faced many environmental factors that are associated with, and can sometimes influence, mental illness—things such as poverty, persecution, social alienation, psychological trauma, substance abuse, and high stress.
|
||||
26
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|
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title: "Creativity"
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source: "https://en.wikipedia.org/wiki/Creativity"
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category: "reference"
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tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:10.173371+00:00"
|
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|
||||
---
|
||||
|
||||
==== Studies ====
|
||||
A study by psychologist J. Philippe Rushton found creativity to correlate with intelligence and psychoticism. Another study found creativity to be greater in people with schizotypal personality disorder than in people with either schizophrenia or those without mental health disorders. While divergent thinking was associated with activation of both sides of the prefrontal cortex, schizotypal individuals were found to have much greater activation of their right prefrontal cortex. That study hypothesized that such individuals are better at accessing both hemispheres, allowing them to make novel associations at a faster rate. Consistent with this hypothesis, ambidexterity is also more common in people with schizotypal personality disorder and schizophrenia. Three studies by Mark Batey and Adrian Furnham demonstrated the relationships between schizotypal personality disorder, hypomanic personality, and several different measures of creativity.
|
||||
A study of 300,000 persons with schizophrenia, bipolar disorder, or unipolar depression, and their relatives, found overrepresentation in creative professions of those with bipolar disorder as well as for undiagnosed siblings of those with schizophrenia or bipolar disorder. There was no overall overrepresentation, but overrepresentation for artistic occupations, among those diagnosed with schizophrenia. There was no association for those with unipolar depression or their relatives.
|
||||
Another study, involving more than one million people, conducted by Swedish researchers at the Karolinska Institute, reported a number of correlations between creative occupations and mental illnesses. Writers had a higher risk of anxiety and bipolar disorders, schizophrenia, unipolar depression, and substance abuse, and were almost twice as likely as the general population to kill themselves. Dancers and photographers were also more likely to have bipolar disorder. Those in the creative professions were no more likely to have psychiatric disorders than other people, although they were more likely to have a close relative with a disorder, including anorexia and, to some extent, autism, the Journal of Psychiatric Research reported.
|
||||
Nancy Andreasen was one of the first researchers to carry out a large-scale study of creativity and whether mental illnesses have an impact on someone's ability to be creative. She expected to find a link between creativity and schizophrenia, but her research sample (the book-authors she pooled) had no history of schizophrenia. Her findings instead showed that 80% of the creative group previously had some episode of mental illness in their lifetime. When she performed follow-up studies over a 15-year period, she found that 43% of the authors had bipolar disorder, compared to 1% of the general public.
|
||||
In 1989 another study, by Kay Redfield Jamison, reaffirmed those statistics, with 38% of her sample of authors having a history of mood disorders. Anthony Storr, a prominent psychiatrist, remarked:
|
||||
|
||||
The creative process can be a way of protecting the individual against being overwhelmed by depression, a means of regaining a sense of mastery in those who have lost it, and, to a varying extent, a way of repairing the self-damaged by bereavement or by the loss of confidence in human relationships which accompanies depression from whatever cause.
|
||||
|
||||
==== Bipolar disorders ====
|
||||
People diagnosed with bipolar disorder report themselves as having a larger range of emotional understanding, heightened states of perception, and an ability to connect better with those in the world around them. Other reported traits include higher rates of productivity, higher senses of self-awareness, and greater empathy. Those who have bipolar disorder also understand their own sense of heightened creativity and ability to get immense numbers of tasks done all at once. In one study, of 219 participants (aged 19 to 63) diagnosed with bipolar disorder, 82% of them reported having elevated feelings of creativity during their hypomanic swings.
|
||||
A study done by Shapiro and Weisberg also showed a positive correlation between the manic upswings of the cycles of bipolar disorder and the ability of an individual to be more creative. The data showed, however, that it was not the depressive swing that brings forth dark creative spurts, but the act of climbing out of the depressive episode that sparks creativity. The reason behind this spur of creative genius could come from the type of self-image that the person has during a time of hypomania. A hypomanic person may feel a bolstered sense of self-confidence, creative confidence, and sense of individualism.
|
||||
|
||||
==== Opinions ====
|
||||
Vaitsa Giannouli believes that the creativity a person diagnosed with bipolar disorder feels comes as a form of "stress management". In the realm of music, one might be expressing one's stress or pains through the pieces one writes in order to better understand those same feelings. Famous authors and musicians, along with some actors, would often attribute their wild enthusiasm to something like a hypomanic state. The artistic side of society has been notorious for behaviors that are seen as maladapted to societal norms. Symptoms of bipolar disorder correlate with behaviors in high-profile creative personalities such as alcohol addiction; drug abuse including stimulants, depressants, hallucinogens and dissociatives, opioids, inhalants, and cannabis; difficulties in holding regular occupations; interpersonal problems; legal issues; and a high risk of suicide.
|
||||
Robert Weisberg believes that the state of mania sets "free the powers of a thinker". He implies that not only has the person become more creative, but they have fundamentally changed the kind of thoughts they produce. In a study of poets, who are especially highly afflicted with bipolar disorders, over a period of three years those poets would have cycles of creating really creative and powerful works of poetry. The timelines over the three-year study looked at the poets' personal journals and their clinical records, and found that the timelines between their most powerful poems matched that of their upswings in bipolar disorder.
|
||||
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|
||||
title: "Creativity"
|
||||
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|
||||
source: "https://en.wikipedia.org/wiki/Creativity"
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||||
category: "reference"
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tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T07:11:10.173371+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
=== Personal traits ===
|
||||
Creativity can be expressed in a variety of ways, depending on the uniqueness of people and environments. Theorists have suggested a number of different models of the creative person. However, the creativity-profiling approach must take into account the tension between predicting the creative profile of an individual, as characterized by the psychometric approach, and the evidence that group creativity is founded on diversity and difference.
|
||||
From a personality-traits perspective, there are a number of traits that are associated with creativity in people. Creative people tend to be more open to new experiences, are more self-confident, are more ambitious, self-accepting, impulsive, driven, dominant, and hostile, compared to people who are less creative.
|
||||
|
||||
==== Divergent production ====
|
||||
One characteristic of creative people, as measured by some psychologists, is what is called "divergent production"—the ability of a person to generate a diverse assortment, yet an appropriate amount, of responses to a given situation. One way to measure divergent production is by administering the Torrance Tests of Creative Thinking, which assess the diversity, quantity, and appropriateness of participants' responses to a variety of open-ended questions. Some researchers also emphasize how creative people are better at balancing between divergent and convergent production, which depends on an individual's innate preference or ability to explore and exploit ideas.
|
||||
|
||||
==== Dedication and expertise ====
|
||||
Other researchers of creativity see that what distinguishes creative people as a cognitive process of dedication to problem-solving and developing expertise in the field of their creative expression. Hardworking people study the work of people before them in their milieu, become experts in their fields, and then have the ability to add to and build upon previous information in innovative and creative ways. In a study of projects by design students, students who had more knowledge of their subject on average exhibited greater creativity in carrying out their projects.
|
||||
|
||||
==== Motivation ====
|
||||
A person's motivation may also be predictive of their level of creativity. Motivation stems from two different sources: intrinsic and extrinsic. Intrinsic motivation is an internal drive within a person to participate as a result of personal interest, desires, hopes, goals, etc. Extrinsic motivation is a drive from outside and might take the form of payment, rewards, fame, approval from others, etc. Although intrinsic and extrinsic motivation can both increase creativity in certain cases, strictly extrinsic motivation often impedes creativity in people.
|
||||
|
||||
=== Environment ===
|
||||
In studying exceptionally creative people in history, some common traits in lifestyle and environment are often found. Creative people usually had supportive, but rigid and non-nurturing, parents. Most had an interest in their field at an early age, and most had a highly supportive and skilled mentor in their field of interest. Often the field they chose was relatively uncharted, allowing for their creativity to be expressed more. Most exceptionally creative people devoted almost all of their time and energy into their craft, and after about a decade had a creative breakthrough of fame. Their lives were marked with extreme dedication and a cycle of hard-work and breakthroughs as a result of their determination.
|
||||
|
||||
== In different fields ==
|
||||
|
||||
=== Art ===
|
||||
|
||||
Creativity is a fundamental component of the creative arts and design practice. It allows artists and designers to generate innovative ideas, solve complex problems, create products and experiences that are meaningful and impactful, stay ahead of trends, and anticipate future needs. Author Austin Kleon asserts that all creative work builds on what came before. Embracing influences and educating oneself in the work of others is conducive to creativity.
|
||||
|
||||
=== Neuroscience ===
|
||||
|
||||
The neuroscience of creativity looks at the operation of the brain during creative behavior. One article writes that "creative innovation might require coactivation and communication between regions of the brain that ordinarily are not strongly connected." People who excel at creative innovation tend to differ from others in three ways: first, they have a high level of specialized knowledge; second, they are capable of divergent thinking mediated by the frontal lobe; and, third, they are able to modulate neurotransmitters such as norepinephrine in their frontal lobe. Thus, the frontal lobe appears to be the part of the cortex that is most important for creativity.
|
||||
A 2015 study of creativity found that it involves the interaction of multiple neural networks, including those that support associative thinking, along with other default mode network functions. In 2018, some experiments showed that when the brain suppresses obvious or "known" solutions, the outcome is solutions that are more creative. This suppression is mediated by alpha oscillations in the right temporal lobe and activity in the right frontal pole.
|
||||
|
||||
==== REM sleep ====
|
||||
Creativity involves the forming of associative elements into new combinations that are useful or meet some requirement. Sleep aids this process. REM rather than NREM sleep appears to be responsible. This may be due to changes in cholinergic and noradrenergic neuromodulation that occurs during REM sleep. During this period of sleep, high levels of acetylcholine in the hippocampus suppress feedback from the hippocampus to the neocortex, and lower levels of acetylcholine and norepinephrine in the neocortex encourage the spread of associational activity within neocortical areas without control from the hippocampus. This is in contrast to waking consciousness, during which higher levels of norepinephrine and acetylcholine inhibit recurrent connections in the neocortex. REM sleep may aid creativity by allowing "neocortical structures to reorganize associative hierarchies, in which information from the hippocampus would be reinterpreted in relation to previous semantic representations or nodes."
|
||||
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|
||||
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tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:21:42.179797+00:00"
|
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date_saved: "2026-05-05T07:11:11.499680+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
34
data/en.wikipedia.org/wiki/Cybernetics-0.md
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|
||||
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|
||||
title: "Cybernetics"
|
||||
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|
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source: "https://en.wikipedia.org/wiki/Cybernetics"
|
||||
category: "reference"
|
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|
||||
date_saved: "2026-05-05T07:11:12.709808+00:00"
|
||||
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|
||||
---
|
||||
|
||||
Cybernetics is the transdisciplinary study of circular causal processes such as feedback and recursion, where the effects of a system's actions (its outputs) return as inputs to that system, influencing subsequent actions. It is concerned with general principles that are relevant across multiple contexts, including engineering, ecological, economic, biological, cognitive and social systems and also in practical activities such as designing, learning, and managing. Cybernetics' transdisciplinary character means that it intersects with a number of other fields, resulting in a wide influence and diverse interpretations.
|
||||
The field is named after an example of circular causal feedback—that of steering a ship (the ancient Greek κυβερνήτης (kybernḗtēs) refers to the person who steers a ship). In steering a ship, the position of the rudder is adjusted in continual response to the effect it is observed as having, forming a feedback loop through which a steady course can be maintained in a changing environment, responding to disturbances from cross winds and tide.
|
||||
Cybernetics has its origins in exchanges between numerous disciplines during the 1940s. Initial developments were consolidated through meetings such as the Macy conferences and the Ratio Club. Early focuses included purposeful behaviour, neural networks, heterarchy, information theory, and self-organising systems. As cybernetics developed, it became broader in scope to include work in design, family therapy, management and organisation, pedagogy, sociology, the creative arts and the counterculture.
|
||||
|
||||
== Definitions ==
|
||||
Cybernetics has been defined in a variety of ways, reflecting "the richness of its conceptual base". One of the best known definitions is that of the American scientist Norbert Wiener, who characterised cybernetics as concerned with "control and communication in the animal and the machine". Another early definition is that of the Macy cybernetics conferences, where cybernetics was understood as the study of "circular causal and feedback mechanisms in biological and social systems". Margaret Mead emphasised the role of cybernetics as "a form of cross-disciplinary thought which made it possible for members of many disciplines to communicate with each other easily in a language which all could understand".
|
||||
Other definitions include: "the art of governing or the science of government" (André-Marie Ampère); "the art of steersmanship" (Ross Ashby); "the study of systems of any nature which are capable of receiving, storing, and processing information so as to use it for control" (Andrey Kolmogorov); and "a branch of mathematics dealing with problems of control, recursiveness, and information, focuses on forms and the patterns that connect" (Gregory Bateson).
|
||||
|
||||
== Etymology ==
|
||||
|
||||
The Ancient Greek term κυβερνητικός (kubernētikos, '(good at) steering') appears in Plato's Republic and Alcibiades, where the metaphor of a steersman is used to signify the governance of people. The French word cybernétique was also used in 1834 by the physicist André-Marie Ampère to denote the sciences of government in his classification system of human knowledge.
|
||||
According to Norbert Wiener, the word cybernetics was coined by a research group involving himself and Arturo Rosenblueth in the summer of 1947. It has been attested in print since at least 1948 through Wiener's book Cybernetics: Or Control and Communication in the Animal and the Machine. In the book, Wiener states:
|
||||
|
||||
After much consideration, we have come to the conclusion that all the existing terminology has too heavy a bias to one side or another to serve the future development of the field as well as it should; and as happens so often to scientists, we have been forced to coin at least one artificial neo-Greek expression to fill the gap. We have decided to call the entire field of control and communication theory, whether in the machine or in the animal, by the name Cybernetics, which we form from the Greek κυβερνήτης or steersman.
|
||||
Moreover, Wiener explains, the term was chosen to recognize James Clerk Maxwell's 1868 publication on feedback mechanisms involving governors, noting that the term governor is also derived from κυβερνήτης (kubernḗtēs) via a Latin corruption gubernator. Finally, Wiener motivates the choice by steering engines of a ship being "one of the earliest and best-developed forms of feedback mechanisms".
|
||||
|
||||
== History ==
|
||||
|
||||
=== First wave ===
|
||||
|
||||
The initial focus of cybernetics was on parallels between regulatory feedback processes in biological and technological systems. Two foundational articles were published in 1943: "Behavior, Purpose and Teleology" by Arturo Rosenblueth, Norbert Wiener, and Julian Bigelow – based on the research on living organisms that Rosenblueth did in Mexico – and the paper "A Logical Calculus of the Ideas Immanent in Nervous Activity" by Warren McCulloch and Walter Pitts.
|
||||
The foundations of cybernetics were then developed through a series of transdisciplinary conferences funded by the Josiah Macy, Jr. Foundation, between 1946 and 1953. The conferences were chaired by McCulloch and had participants that included Ross Ashby, Gregory Bateson, Heinz von Foerster, Margaret Mead, John von Neumann, and Norbert Wiener. In the UK, similar focuses were explored by the Ratio Club, an informal dining club of young psychiatrists, psychologists, physiologists, mathematicians and engineers that met between 1949 and 1958. Wiener introduced the neologism cybernetics to denote the study of "teleological mechanisms" and popularized it through the book Cybernetics: Or Control and Communication in the Animal and the Machine.
|
||||
During the 1950s, cybernetics was developed as a primarily technical discipline, such as in Qian Xuesen's 1954 "Engineering Cybernetics". The text was quickly translated into multiple languages and became a foundational text on automation. In the Soviet Union, Cybernetics was initially considered with suspicion but became accepted from the mid to late 1950s.
|
||||
By the 1960s and 1970s, however, cybernetics' transdisciplinarity fragmented, with technical focuses separating into separate fields. Artificial intelligence (AI) was founded as a distinct discipline at the Dartmouth workshop in 1956, differentiating itself from the broader cybernetics field. After some uneasy coexistence, AI gained funding and prominence. Consequently, cybernetic sciences such as the study of artificial neural networks were downplayed. Similarly, computer science became defined as a distinct academic discipline in the 1950s and early 1960s.
|
||||
44
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||||
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|
||||
title: "Cybernetics"
|
||||
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|
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source: "https://en.wikipedia.org/wiki/Cybernetics"
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|
||||
date_saved: "2026-05-05T07:11:12.709808+00:00"
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|
||||
---
|
||||
|
||||
=== Second wave ===
|
||||
The second wave of cybernetics came to prominence from the 1960s onwards, with its focus shifting away from technology toward social, ecological, and philosophical concerns. It was still grounded in biology, notably Maturana and Varela's autopoiesis, and built on earlier work on self-organising systems and the presence of anthropologists Mead and Bateson in the Macy meetings. The Biological Computer Laboratory, founded in 1958 and active until the mid-1970s under the direction of Heinz von Foerster at the University of Illinois at Urbana–Champaign, was a major incubator of this trend in cybernetics research.
|
||||
|
||||
Focuses of the second wave of cybernetics included management cybernetics, such as Stafford Beer's biologically inspired viable system model; work in family therapy, drawing on Bateson; social systems, such as in the work of Niklas Luhmann; epistemology and pedagogy, such as in the development of radical constructivism. Cybernetics' core theme of circular causality was developed beyond goal-oriented processes to concerns with reflexivity and recursion. This was especially so in the development of second-order cybernetics (or the cybernetics of cybernetics), developed and promoted by Heinz von Foerster, which focused on questions of observation, cognition, epistemology, and ethics.
|
||||
The 1960s onwards also saw cybernetics begin to develop exchanges with the creative arts, design, and architecture, notably with the Cybernetic Serendipity exhibition (ICA, London, 1968), curated by Jasia Reichardt, and the unrealised Fun Palace project (London, unrealised, 1964 onwards), where Gordon Pask was consultant to architect Cedric Price and theatre director Joan Littlewood.
|
||||
In 1962, Qian Xuesen recruited Song Jian and Guan Zhaozhi to establish China's first cybernetics laboratory with him.
|
||||
Following the Sino-Soviet split, cybernetics was deemed disreputable in China. The field was again favored in the 1970s and 1980s following Deng Xiaoping's emphasis on modernisation.
|
||||
|
||||
=== Third wave ===
|
||||
From the 1990s onwards, there has been a renewed interest in cybernetics from a number of directions. Early cybernetic work on artificial neural networks has been returned to as a paradigm in machine learning and artificial intelligence. The entanglements of society with emerging technologies has led to exchanges with feminist technoscience and posthumanism. Re-examinations of cybernetics' history have seen science studies scholars emphasising cybernetics' unusual qualities as a science, such as its "performative ontology". Practical design disciplines have drawn on cybernetics for theoretical underpinning and transdisciplinary connections. Emerging topics include how cybernetics' engagements with social, human, and ecological contexts might come together with its earlier technological focus, whether as a critical discourse or a "new branch of engineering".
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== Key concepts and theories ==
|
||||
The central theme in cybernetics is feedback. Feedback is a process where the observed outcomes of actions are taken as inputs for further action in ways that support the pursuit, maintenance, or disruption of particular conditions, forming a circular causal relationship. In steering a ship, the helmsperson maintains a steady course in a changing environment by adjusting their steering in continual response to the effect it is observed as having.
|
||||
Other examples of circular causal feedback include: technological devices such as the thermostat, where the action of a heater responds to measured changes in temperature regulating the temperature of the room within a set range, and the centrifugal governor of a steam engine, which regulates the engine speed; biological examples such as the coordination of volitional movement through the nervous system and the homeostatic processes that regulate variables such as blood sugar; and processes of social interaction such as conversation.
|
||||
Negative feedback processes are those that maintain particular conditions by reducing (hence 'negative') the difference from a desired state, such as where a thermostat turns on a heater when it is too cold and turns a heater off when it is too hot. Positive feedback processes increase (hence 'positive') the difference from a desired state. An example of positive feedback is when a microphone picks up the sound that it is producing through a speaker, which is then played through the speaker, and so on.
|
||||
In addition to feedback, cybernetics is concerned with other forms of circular processes including: feedforward, recursion, and reflexivity.
|
||||
Other key concepts and theories in cybernetics include:
|
||||
|
||||
Autopoiesis
|
||||
Black box
|
||||
Conversation theory
|
||||
Double bind theory: Double binds are patterns created in interaction between two or more parties in ongoing relationships where there is a contradiction between messages at different logical levels that creates a situation with emotional threat but no possibility of withdrawal from the situation and no way to articulate the problem. The theory was first described by Gregory Bateson and colleagues in the 1950s with regard to the origins of schizophrenia, but it is also characteristic of many other social contexts.
|
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Experimental epistemology
|
||||
Good regulator theorem
|
||||
Heterarchy
|
||||
Perceptual control theory: A model of behavior based on the properties of negative feedback (cybernetic) control loops. A key insight of PCT is that the controlled variable is not the output of the system (the behavioral actions), but its input, "perception". The theory came to be known as "perceptual control theory" to distinguish from those control theorists that assert or assume that it is the system's output that is controlled. Method of levels is an approach to psychotherapy based on perceptual control theory where the therapist aims to help the patient shift their awareness to higher levels of perception to resolve conflicts and allow reorganization to take place.
|
||||
Radical constructivism
|
||||
Second-order cybernetics: Also known as the cybernetics of cybernetics, second-order cybernetics is the recursive application of cybernetics to itself and the practice of cybernetics according to such a critique.
|
||||
Schismogenesis
|
||||
Self-organisation
|
||||
Social systems theory
|
||||
Syntegrity
|
||||
Variety and Requisite Variety
|
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Viable system model
|
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== Related fields and applications ==
|
||||
Cybernetics' central concept of circular causality is of wide applicability, leading to diverse applications and relations with other fields. Many of the initial applications of cybernetics focused on engineering, biology, and exchanges between the two, such as medical cybernetics and robotics and topics such as neural networks, heterarchy. In the social and behavioral sciences, cybernetics has included and influenced work in anthropology, sociology, economics, family therapy, cognitive science, and psychology.
|
||||
As cybernetics has developed, it broadened in scope to include work in management, design, pedagogy, and the creative arts, while also developing exchanges with constructivist philosophies, counter-cultural movements, and media studies. The development of management cybernetics has led to a variety of applications, notably to the national economy of Chile under the Allende government in Project Cybersyn, as well as other lesser known projects by Stafford Beer in Latin America. In design, cybernetics has been influential on interactive architecture, human-computer interaction, design research, and the development of systemic design and metadesign practices.
|
||||
|
||||
Cybernetics is often understood within the context of systems science, systems theory, and systems thinking. Systems approaches influenced by cybernetics include critical systems thinking, which incorporates the viable system model; systemic design; and system dynamics, which is based on the concept of causal feedback loops.
|
||||
Many fields trace their origins in whole or part to work carried out in cybernetics, or were partially absorbed into cybernetics when it was developed. These include artificial intelligence, bionics, cognitive science, control theory, complexity science, computer science, information theory and robotics. Some aspects of modern artificial intelligence, particularly the social machine, are often described in cybernetic terms.
|
||||
|
||||
== Journals and societies ==
|
||||
|
||||
Academic journals with focuses in cybernetics include:
|
||||
|
||||
IEEE Transactions on Systems, Man, and Cybernetics: Systems
|
||||
IEEE Transactions on Human-Machine Systems
|
||||
IEEE Transactions on Cybernetics
|
||||
IEEE Transactions on Computational Social Systems
|
||||
Biological Cybernetics
|
||||
Constructivist Foundations
|
||||
Cybernetics and Human Knowing
|
||||
Cybernetics and Systems
|
||||
Enacting Cybernetics. An open access journal published by the Cybernetics Society and hosted by Ubiquity Press.
|
||||
Kybernetes
|
||||
Academic societies primarily concerned with cybernetics or aspects of it include:
|
||||
|
||||
American Society for Cybernetics (ASC), founded in 1964
|
||||
British Cybernetics Society (CybSoc)
|
||||
Metaphorum: The Metaphorum group was set up in 2003 to develop Stafford Beer's legacy in Organizational Cybernetics. The Metaphorum Group was born in a Syntegration in 2003 and have every year after developed a Conference on issues related to Organizational Cybernetics' theory and practice.
|
||||
IEEE Systems, Man, and Cybernetics Society
|
||||
RC51 Sociocybernetics: RC51 is a research committee of the International Sociological Association promoting the development of (socio)cybernetic theory and research within the social sciences.
|
||||
SCiO (Systems and Complexity in Organisation) is a community of systems practitioners who believe that traditional approaches to running organisations are no longer capable of dealing with the complexity and turbulence faced by organisations today and are responsible for many of the problems we see today. SCiO delivers an apprenticeship on masters level and a certification in systems practice.
|
||||
|
||||
== See also ==
|
||||
|
||||
== Notes ==
|
||||
|
||||
== References ==
|
||||
|
||||
== Further reading ==
|
||||
Ascott, Roy (1967). Behaviourist Art and the Cybernetic Vision. Cybernetica, Journal of the International Association for Cybernetics (Namur), 10, pp. 25–56
|
||||
Ashby, William Ross (1956). An introduction to cybernetics (PDF). Chapman & Hall. Retrieved 3 June 2012.
|
||||
Beer, Stafford (1974). Designing freedom. Chichester, West Sussex, England: Wiley. ISBN 978-0471951650.
|
||||
François, Charles (1999). "Systemics and cybernetics in a historical perspective". In: Systems Research and Behavioral Science. Vol 16, pp. 203–219 (1999)
|
||||
George, F. H. (1971). Cybernetics. Teach Yourself Books. ISBN 978-0-340-05941-8.
|
||||
Gerovitch, Slava (2002). From newspeak to cyberspeak : a history of Soviet cybernetics. Cambridge, Massachusetts [u.a.]: MIT Press. ISBN 978-0262-07232-8.
|
||||
Hayles, N. Katherine (1999). How We Became Posthuman: Virtual Bodies in Cybernetics, Literature and Informatics, Chicago: The University of Chicago Press. ISBN 9780226321462
|
||||
Heims, Steve Joshua (1993). Constructing a social science for postwar America : the cybernetics group, 1946–1953 (1st ed.). Cambridge, Massachusetts u.a.: MIT Press. ISBN 9780262581233.
|
||||
Heylighen, Francis, and Cliff Joslyn (2002). "Cybernetics and Second Order Cybernetics", in: R.A. Meyers (ed.), Encyclopedia of Physical Science & Technology (3rd ed.), Vol. 4, (Academic Press, San Diego), p. 155–169.
|
||||
Ilgauds, Hans Joachim (1980), Norbert Wiener, Leipzig.
|
||||
Mariátegui, José-Carlos / Maulen, D. (eds.) Special issue on “Cybernetics in Latin America: Contexts Developments, Perceptions and Impacts", AI & Society, 37, 2022.
|
||||
Medina, Eden (2011). Cybernetic revolutionaries : technology and politics in Allende's Chile. Cambridge, Massachusetts: MIT Press. ISBN 978-0-262-01649-0.
|
||||
Pangaro, Paul. "Cybernetics — A Definition".
|
||||
Pask, Gordon (1972). "Cybernetics". Encyclopædia Britannica. Archived from the original on 28 September 2011. Retrieved 26 September 2007.
|
||||
Pickering, Andrew (2010). The cybernetic brain : sketches of another future ([Online-Ausg.] ed.). Chicago: University of Chicago Press. ISBN 978-0226667898.
|
||||
von Foerster, Heinz, (1995), Ethics and Second-Order Cybernetics Archived 28 January 2014 at the Wayback Machine.
|
||||
Wiener, Norbert (1948). Hermann & Cie (ed.). Cybernetics; or, Control and communication in the animal and the machine. Paris: Technology Press. Retrieved 3 June 2012.
|
||||
Wiener, Norbert (1950). Cybernetics and Society: The Human Use of Human Beings. Houghton Mifflin.
|
||||
|
||||
== External links ==
|
||||
|
||||
General
|
||||
|
||||
Norbert Wiener and Stefan Odobleja – A Comparative Analysis
|
||||
Reading List for Cybernetics
|
||||
Principia Cybernetica Web
|
||||
Web Dictionary of Cybernetics and Systems
|
||||
Glossary Slideshow (136 slides) Archived 5 July 2015 at the Wayback Machine
|
||||
"Basics of Cybernetics". Archived from the original on 11 August 2010. Retrieved 23 January 2016.
|
||||
What is Cybernetics? Livas short introductory videos on YouTube
|
||||
Societies and journals
|
||||
|
||||
American Society for Cybernetics
|
||||
IEEE Systems, Man, & Cybernetics Society
|
||||
International Society for Cybernetics and Systems Research
|
||||
The Cybernetics Society
|
||||
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|
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