Editing Complex adaptive system (section)

== 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 [[life|living]], adaptable, changeable systems. Complex adaptive systems may adopt hard or softer approaches.<ref>{{cite journal |last1=Yolles |first1=Maurice |title=The complexity continuum, Part 1: hard and soft theories |journal=Kybernetes |date=2018 |volume=48 |issue=6 |pages=1330–1354 |doi=10.1108/K-06-2018-0337|s2cid=69636750 }}</ref> 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|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.<ref name="CAS-T-12" /><ref name="CAS-T-11" /><ref name="CAS-T-13" /> [[John Henry Holland|John H. Holland]] said that CAS "are systems that have a large numbers of components, often called agents, that interact and adapt or learn."<ref>{{cite journal|year=2006|title=Studying Complex Adaptive Systems|journal=Journal of Systems Science and Complexity|volume=19|issue=1|pages=1–8|doi=10.1007/s11424-006-0001-z|author=Holland John H|hdl=2027.42/41486|s2cid=27398208|url=https://deepblue.lib.umich.edu/bitstream/2027.42/41486/1/11424_2006_Article_1.pdf|hdl-access=free}}</ref>

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;<ref name=AFC-NA-21/> the [[brain]] and the [[immune system]]; and the [[cell (biology)|cell]] and the developing [[embryo]]. Human social group-based endeavors, such as [[political party|political parties]], [[community|communities]], [[geopolitical]] [[organisations|organizations]], [[war]], and [[terrorist network analysis|terrorist networks]] are also considered CAS.<ref name=AFC-NA-21/><ref name=GT-33/><ref name=CAS-T-19>{{cite web|first=Samuel|last=Solvit|title=Dimensions of War: Understanding War as a Complex Adaptive System|url=http://dimensionsofwar.com/|publisher=L'Harmattan|year=2012|access-date=25 August 2013}}</ref> The [[internet]] and [[cyberspace]]—composed, collaborated, and managed by a complex mix of [[human–computer interaction]]s, is also regarded as a complex adaptive system.<ref name=CAS-T-16/><ref name=CAS-T-17/><ref name=CAS-T-18/> CAS can be hierarchical, but more often exhibit aspects of "self-organization".<ref>{{Cite book|title=Hidden order: how adaptation builds complexity|last=Holland, John H. (John Henry)|date=1996|publisher=Addison-Wesley|isbn=0201442302|oclc=970420200}}</ref>

The term complex adaptive system was coined in 1968 by sociologist [[Walter F. Buckley]]<ref name="Buckley_etal_2008">{{cite journal | last1=Buckley | first1= Walter | last2=Schwandt | first2=David | last3=Goldstein | first3=Jeffrey A. | date=2008 | title=An introduction to "Society as a complex adaptive system" | journal=E:CO Emergence: Complexity & Organization |volume=10 |issue=3 |pages=86–112 | url=https://journal.emergentpublications.com/article/an-introduction-to-society-as-a-complex-adaptive-system/pdf/ | access-date=2020-11-02 }}</ref><ref name="Bentley&Anandhi_2020">{{cite journal | last1=Bentley | first1=Chance | last2=Anandhi | first2=Aavudai | date=2020 | title=Representing driver-response complexity in ecosystems using an improved conceptual model | journal=Ecological Modelling | volume=437 | issue=437 | page=109320 | doi=10.1016/j.ecolmodel.2020.109320 | url=https://www.researchgate.net/publication/346156576 | access-date=2020-12-24 | doi-access=free | bibcode=2020EcMod.43709320B }}</ref> who proposed a model of [[cultural evolution]] which regards psychological and socio-cultural systems as analogous with biological [[species]].<ref name="Buckley_1968">{{cite book |last=Buckley |first=Walter W. |title=Modern Systems Research for the Behavioral Scientist: A Sourcebook |publisher=Aldine |date=1968 |isbn=9780202369402 | url=https://books.google.com/books?id=zmankKmLmQYC&q=%22complex+adaptive+system%22&pg=PA490 | access-date=2020-11-02 }}</ref> In the modern context, complex adaptive system is sometimes linked to [[memetics]],<ref name="Situngkir_2004">{{cite journal|last1=Situngkir|first1=Hokky|date=2004|title=On selfish memes: culture as complex adaptive system|url=https://www.researchgate.net/publication/23742033|journal=Journal of Social Complexity|volume=2|issue=1|pages=20–32|access-date=2020-11-02}}</ref> or proposed as a reformulation of memetics.<ref name="Frank_2008">{{cite book |last=Frank |first=Roslyn M. |date=2008|editor-last=Frank | title=Sociocultural Situatedness, Vol. 2| publisher=De Gruyter |chapter=The Language–organism–species analogy: a complex adaptive systems approach to shifting perspectives on "language" |chapter-url=https://www.academia.edu/374356 |pages=215–262 |isbn=978-3-11-019911-6 |access-date=2020-11-02 }}</ref> [[Michael D. Cohen (academic)|Michael D. Cohen]] and [[Robert Axelrod (political scientist)|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 '[[population]]s of business strategies', for example, the detailed evolutionary mechanisms are often distinctly unbiological.<ref name="Axelrod&Cohen_1999">{{cite book|last1=Axelrod|first1=Robert M.|title=Harnessing Complexity: Organizational Implications of a Scientific Frontier|last2=Cohen|first2=M. D.|date=1999|publisher=Free Press|isbn=9780684867175}}</ref> As such, complex adaptive system is more similar to [[Richard Dawkins]]'s idea of [[Replicator (evolution unit)|replicators]].<ref name="Axelrod&Cohen_1999" /><ref name="Gell-Mann_1994">{{cite book|last=Gell-Mann|first=Murray|title=Studies in the Sciences of Complexity, Proc. Vol. XIX|date=1994|publisher=Addison-Wesley|editor-last1=Cowan|editor-first1=G.|pages=17–45|chapter=Complex adaptive systems|access-date=2020-11-06|editor-last2=Pines|editor-first2=D.|editor-last3=Meltzer|editor-first3=D.|chapter-url=https://authors.library.caltech.edu/60491/1/MGM%20113.pdf}}</ref><ref name="Fromm_2004">{{cite book|last=Fromm|first=Jochen|url=https://www.upress.uni-kassel.de/katalog/Download.php?ISBN=978-3-89958-069-3&type=pdf-f|title=The Emergence of Complexity|date=2004|publisher=Kassel University Press|access-date=2020-11-06}}</ref>

=== 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 (disambiguation) | collectivity]] of interacting, adaptive agents.  Complex adaptive systems feature a high degree of [[adaptive capacity]], giving them resilience in the face of [[wikt:perturbation|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:<ref>[[Paul Cilliers]] (1998) ''Complexity and Postmodernism: Understanding Complex Systems''</ref>

* The number of elements is sufficiently large that conventional descriptions (e.g. a system of [[differential equation]]s) are not only impractical, but cease to assist in understanding the system. Moreover, the elements interact dynamically, and the interactions can be physical or involve the exchange of information.
* Such interactions are rich, i.e. any element or sub-system in the system is affected by and affects several other elements or sub-systems.
* The interactions are [[non-linear]]: small changes in inputs, physical interactions or stimuli can cause large effects or very significant changes in outputs.
* Interactions are primarily but not exclusively with immediate neighbours and the nature of the influence is modulated.
* Any interaction can feed back onto itself directly or after a number of intervening stages. Such feedback can vary in quality.  This is known as ''recurrency.''
* The overall behavior of the system of elements is not predicted by the behavior of the individual elements 
* Such systems may be open and it may be difficult or impossible to define system boundaries
* Complex systems operate under [[Non-equilibrium thermodynamics|far from equilibrium]] conditions. There has to be a constant flow of energy to maintain the organization of the system
* Agents in the system are adaptive. They update their strategies in response to input from other agents, and the system itself.<ref name="Miller, John H., and Scott E. Page" />
* Elements in the system may be ignorant of the behaviour of the system as a whole, responding only to the information or physical stimuli available to them locally

[[Robert Axelrod (political scientist)|Robert Axelrod]] & [[Michael D. Cohen (academic)|Michael D. Cohen]] identify a series of key terms from a modeling perspective:<ref>[[Robert Axelrod (political scientist)|Robert Axelrod]] & [[Michael D. Cohen (academic)|Michael D. Cohen]], ''Harnessing Complexity''. [[Basic Books]], 2001</ref>
* '''Strategy''', a conditional action pattern that indicates what to do in which circumstances
* '''Artifact''', a material resource that has definite location and can respond to the action of agents
* '''Agent''', a collection of properties, strategies & capabilities for interacting with artifacts & other agents
* '''Population''', a collection of agents, or, in some situations, collections of strategies
* '''System''', a larger collection, including one or more populations of agents and possibly also artifacts
* '''Type''', all the agents (or strategies) in a population that have some characteristic in common
* '''Variety''', the diversity of types within a population or system
* '''Interaction pattern''', the recurring regularities of contact among types within a system
* '''Space (physical)''', location in geographical space & time of agents and artifacts
* '''Space (conceptual)''', "location" in a set of categories structured so that "nearby" agents will tend to interact
* '''Selection''', processes that lead to an increase or decrease in the frequency of various types of agent or strategies
* '''Success criteria''' or '''performance measures''', a "score" used by an agent or designer in attributing credit in the selection of relatively successful (or unsuccessful) strategies or agents

Turner and Baker synthesized the characteristics of complex adaptive systems from the literature and tested these characteristics in the context of creativity and innovation.<ref>Turner, J. R., & Baker, R. (2020). Just doing the do: A case study testing creativity and innovative processes as complex adaptive systems. New Horizons in Adult Education and Human Resource Development, 32(2). {{doi|10.1002/nha3.20283}}</ref> Each of these eight characteristics had been shown to be present in the creativity and innovative processes:
* '''Path dependent:''' Systems tend to be sensitive to their initial conditions. The same force might affect systems differently.<ref name="Combating infections at Maine Medic">{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 | s2cid = 144225216 }}</ref>
* '''Systems have a history:''' The future behavior of a system depends on its initial starting point and subsequent history.<ref>{{cite journal | last1 = Boal | first1 = K. B. | last2 = Schultz | first2 = P. L. | year = 2007 | title = Storytelling, time, and evolution: The role of strategic leadership in complex adaptive systems | journal = The Leadership Quarterly | volume = 18 | issue = 4| pages = 411–428 | doi = 10.1016/j.leaqua.2007.04.008 }}</ref>
* '''Non-linearity:''' React disproportionately to environmental perturbations. Outcomes differ from those of simple systems.<ref name="Combating infections at Maine Medic"/><ref>{{cite journal | last1 = Luoma | first1 = M | year = 2006 | title = A play of four arenas – How complexity can serve management development | journal = Management Learning | volume = 37 | pages = 101–123 | doi = 10.1177/1350507606058136 | s2cid = 14435060 }}</ref>
* '''Emergence:''' Each system's internal dynamics affect its ability to change in a manner that might be quite different from other systems.<ref name="Combating infections at Maine Medic"/>
* '''Irreducible:''' Irreversible process transformations cannot be reduced back to its original state.<ref name="Unravelling the dynamics of knowled">{{cite journal | last1 = Borzillo | first1 = S. | last2 = Kaminska-Labbe | first2 = R. | year = 2011 | title = Unravelling the dynamics of knowledge creation in communities of practice through complexity theory lenses | journal = Knowledge Management Research & Practice | volume = 9 | issue = 4| pages = 353–366 | doi = 10.1057/kmrp.2011.13 | s2cid = 62134156 }}</ref>
* '''Adaptive/Adaptability:''' Systems that are simultaneously ordered and disordered are more adaptable and resilient.<ref name="Combating infections at Maine Medic"/>
* '''Operates between order and chaos:''' Adaptive tension emerges from the energy differential between the system and its environment.<ref name="Unravelling the dynamics of knowled"/>
* '''Self-organizing:''' Systems are composed of interdependency, interactions of its parts, and diversity in the system.<ref name="Combating infections at Maine Medic"/>