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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Complex adaptive system | 1/3 | https://en.wikipedia.org/wiki/Complex_adaptive_system | reference | science, encyclopedia | 2026-05-05T14:35:56.468479+00:00 | kb-cron |
A complex adaptive system (CAS) is a system that is complex in that it is a dynamic network of interactions, but the behavior of the ensemble may not be predictable according to the behavior of the components. It is adaptive in that the individual and collective behavior mutate and self-organize corresponding to the change-initiating micro-event or collection of events. It is a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to adapt to the changing environment and increase their survivability as a macro-structure. The Complex Adaptive Systems approach builds on replicator dynamics. The study of complex adaptive systems, a subset of nonlinear dynamical systems, is an interdisciplinary matter that attempts to blend insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behavior. CAS is increasingly applied to analyze political-economic development and institutional change, where outcomes emerge from interactions among adaptive agents, and may be influenced but not precisely controlled.
== Overview == The term complex adaptive systems, or complexity science, is often used to describe the loosely organized academic field that has grown up around the study of such systems. Complexity science is not a single theory—it encompasses more than one theoretical framework and is interdisciplinary, seeking the answers to some fundamental questions about living, adaptable, changeable systems. Complex adaptive systems may adopt hard or softer approaches. Hard theories use formal language that is precise, tend to see agents as having tangible properties, and usually see objects in a behavioral system that can be manipulated in some way. Softer theories use natural language and narratives that may be imprecise, and agents are subjects having both tangible and intangible properties. Examples of hard complexity theories include complex adaptive systems (CAS) and viability theory, and a class of softer theory is Viable System Theory. Many of the propositional consideration made in hard theory are also of relevance to softer theory. From here on, interest will now center on CAS. The study of CAS focuses on complex, emergent and macroscopic properties of the system. John H. Holland said that CAS "are systems that have a large numbers of components, often called agents, that interact and adapt or learn." Typical examples of complex adaptive systems include: climate; cities; firms; markets; governments; industries; ecosystems; social networks; power grids; animal swarms; traffic flows; social insect (e.g. ant) colonies; the brain and the immune system; and the cell and the developing embryo. Human social group-based endeavors, such as political parties, communities, geopolitical organizations, war, supply chains and terrorist networks are also considered CAS. The internet and cyberspace—composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system. CAS can be hierarchical, but more often exhibit aspects of "self-organization". The term complex adaptive system was coined in 1968 by sociologist Walter F. Buckley who proposed a model of cultural evolution which regards psychological and socio-cultural systems as analogous with biological species. In the modern context, complex adaptive system is sometimes linked to memetics, or proposed as a reformulation of memetics. Michael D. Cohen and Robert Axelrod however argue the approach is not social Darwinism or sociobiology because, even though the concepts of variation, interaction and selection can be applied to modelling 'populations of business strategies', for example, the detailed evolutionary mechanisms are often distinctly unbiological. As such, complex adaptive system is more similar to Richard Dawkins's idea of replicators.
=== General properties === What distinguishes a complex adaptive system (CAS) from a pure multi-agent system (MAS) is the focus on top-level properties and features like self-similarity, complexity, emergence and self-organization. Theorists define an MAS as a system composed of multiple interacting agents; whereas in CAS, the agents as well as the system are adaptive and the system is self-similar. A CAS is a complex, self-similar collectivity of interacting, adaptive agents. Complex adaptive systems feature a high degree of adaptive capacity, giving them resilience in the face of perturbation. Other important properties include adaptation (or homeostasis), communication, cooperation, specialization, spatial and temporal organization, and reproduction. Such properties can manifest themselves on all levels: cells specialize, adapt and reproduce themselves just like larger organisms do. Communication and cooperation take place on all levels, from the agent- to the system-level. In some cases the forces driving co-operation between agents in such a system can be analyzed using game theory.
=== Characteristics === Some of the most important characteristics of complex adaptive systems are: