Complex adaptive system

A complex adaptive system is a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to adapt to the changing environment and increase its survivability as a macro-structure.[1][2][3]

They are complex in that they are dynamic networks of interactions, and their relationships are not aggregations of the individual static entities, i.e., the behavior of the ensemble is not predicted by the behavior of the components. They are adaptive in that the individual and collective behavior mutate and self-organize corresponding to the change-initiating micro-event or collection of events.[1][2]


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 theoryit encompasses more than one theoretical framework and is highly interdisciplinary, seeking the answers to some fundamental questions about living, adaptable, changeable systems.

Typical examples of complex adaptive systems include: the global macroeconomic network within a country or group of countries; stock market and complex web of cross border holding companies; social insect (e.g. ant) colonies;[4] the biosphere and the ecosystem; the brain and the immune system; the cell and the developing embryo; manufacturing businesses; and any human social group-based endeavour in a particular ideology and social system such as political parties, communities, geopolitical organisations, war, and terrorist networks of both hierarchical and leaderless nature.[4][5][6] The internet and cyberspace—composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system.[7][8][9]

The fields of CAS and artificial life are closely related. In both areas the principles of emergence and self-organization are very important. The ideas and models of CAS are essentially evolutionary, grounded in modern chemistry, biological views on adaptation, exaptation and evolution and simulation models[10] in economics and social systems.

The study of CAS focuses on complex, emergent and macroscopic properties of the system.[3][11][12] John H. Holland said that CAS "are systems that have a large numbers of components, often called agents, that interact and adapt or learn."[13]

General properties

What distinguishes a 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. A MAS is defined 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 are characterised by a high degree of adaptive capacity, giving them resilience in the face of perturbation.

Other important properties are adaptation (or homeostasis), communication, cooperation, specialization, spatial and temporal organization, and reproduction. They can be found 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. The forces driving co-operation between agents in such a system, in some cases, can be analysed with game theory.


Some of the most important characteristics of complex systems are:[14]

Robert Axelrod & Michael D. Cohen[15] identify a series of key terms from a modeling perspective:

Modeling and simulation

CAS are occasionally modeled by means of agent-based models and complex network-based models.[16] Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model.[17] Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components.[18]

Recently, SpringerOpen/BioMed Central has launched an online open-access journal on the topic of complex adaptive systems modeling (CASM).[19]

CAT and AAT model

Cultural agency theory (CAT) is a cybernetic living systems paradigm intending to model complex adaptive systems focusing also on the social and cultural dimesions.[20] It differs from CAS that is devoid of any substantive cybernetic reflections. Indeed, CAT provides a modelling approach to explore predefined contexts and can be adapted to reflect those contexts. If the modelling approach does not involve social and culture dimensions, it is called autonomous agency theory (AAT).

Evolution of complexity

Passive versus active trends in the evolution of complexity. CAS at the beginning of the processes are colored red. Changes in the number of systems are shown by the height of the bars, with each set of graphs moving up in a time series.

Living organisms are complex adaptive systems. Although complexity is hard to quantify in biology, evolution has produced some remarkably complex organisms.[21] This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".[22]

If this were generally true, evolution would possess an active trend towards complexity. As shown below, in this type of process the value of the most common amount of complexity would increase over time.[23] Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution.[24][25]

However, the idea of a general trend towards complexity in evolution can also be explained through a passive process.[23] This involves an increase in variance but the most common value, the mode, does not change. Thus, the maximum level of complexity increases over time, but only as an indirect product of there being more organisms in total. This type of random process is also called a bounded random walk.

In this hypothesis, the apparent trend towards more complex organisms is an illusion resulting from concentrating on the small number of large, very complex organisms that inhabit the right-hand tail of the complexity distribution and ignoring simpler and much more common organisms. This passive model emphasizes that the overwhelming majority of species are microscopic prokaryotes,[26] which comprise about half the world's biomass[27] and constitute the vast majority of Earth's biodiversity.[28] Therefore, simple life remains dominant on Earth, and complex life appears more diverse only because of sampling bias.

This lack of an overall trend towards complexity in biology does not preclude the existence of forces driving systems towards complexity in a subset of cases. These minor trends are balanced by other evolutionary pressures that drive systems towards less complex states.

See also


  1. 1 2 "Insights from Complexity Theory: Understanding Organisations better". by Assoc. Prof. Amit Gupta, Student contributor - S. Anish , IIM Bangalore. Retrieved 1 June 2012.
  2. 1 2 "Ten Principles of Complexity & Enabling Infrastructures". by Professor Eve Mitleton-Kelly, Director Complexity Research Programme, London School of Economics. Retrieved 1 June 2012.
  3. 1 2 "Evolutionary Psychology, Complex Systems, and Social Theory" (PDF). Bruce MacLennan, Department of Electrical Engineering & Computer Science, University of Tennessee, Knoxville. Retrieved 25 August 2012.
  4. 1 2 Steven Strogatz, Duncan J. Watts and Albert-Laszlo Barabasi "explaining synchronicity (at 6:08) , network theory, self-adaptation mechanism of complex systems, Six Degrees of separation, Small world phenomenon, events are never isolated as they depend upon each other (at 27:07) in the BBC / Discovery Documentary". BBC / Discovery. Retrieved 11 June 2012. "Unfolding the science behind the idea of six degrees of separation"
  5. "Toward a Complex Adaptive Intelligence Community The Wiki and the Blog". D. Calvin Andrus. Retrieved 25 August 2012.
  6. Solvit, Samuel (2012). "Dimensions of War: Understanding War as a Complex Adaptive System". L'Harmattan. Retrieved 25 August 2013.
  7. "The Internet Analyzed as a Complex Adaptive System". Retrieved 25 August 2012.
  8. "Cyberspace: The Ultimate Complex Adaptive System" (PDF). The International C2 Journal. Retrieved 25 August 2012. by Paul W. Phister Jr
  9. "Complex Adaptive Systems" (PDF). 2001. Retrieved 25 August 2012. by Serena Chan, Research Seminar in Engineering Systems
  10. Patrascu, M.; Stancu, A.F.; Pop, F. (2014). "HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation". Soft Computing. 18: 2565–2576. doi:10.1007/s00500-014-1401-y.
  11. "A Complex Adaptive Organization Under the Lens of the LIFE Model:The Case of Wikipedia". Retrieved 25 August 2012.
  12. "Complex Adaptive Systems as a Model for Evaluating Organisational : Change Caused by the Introduction of Health Information Systems" (PDF). Kieren Diment, Ping Yu, Karin Garrety, Health Informatics Research Lab, Faculty of Informatics, University of Wollongong, School of Management, University of Wollongong, NSW. Retrieved 25 August 2012.
  13. Holland John H (2006). "Studying Complex Adaptive Systems". Journal of Systems Science and Complexity. 19 (1): 1–8. doi:10.1007/s11424-006-0001-z.
  14. Paul Cilliers (1998) Complexity and Postmodernism: Understanding Complex Systems
  15. Robert Axelrod & Michael D. Cohen, Harnessing Complexity. Basic Books, 2001
  16. Muaz A. K. Niazi, Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems PhD Thesis
  17. John H. Miller & Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press Book page
  18. Melanie Mitchell, Complexity A Guided Tour, Oxford University Press, Book page
  19. Springer Complex Adaptive Systems Modeling Journal (CASM)
  20. Klijn, E.H. (2008), Complexity theory and Public Administration: what's new; key concepts in complexity theory compared to their counterparts in public administration, Public Management Review, vol 10, issue 3: 299-317
  21. Adami C (2002). "What is complexity?". BioEssays. 24 (12): 1085–94. doi:10.1002/bies.10192. PMID 12447974.
  22. McShea D (1991). "Complexity and evolution: What everybody knows". Biology and Philosophy. 6 (3): 303–24. doi:10.1007/BF00132234.
  23. 1 2 Carroll SB (2001). "Chance and necessity: the evolution of morphological complexity and diversity". Nature. 409 (6823): 1102–9. Bibcode:2001Natur.409.1102C. doi:10.1038/35059227. PMID 11234024.
  24. Furusawa C, Kaneko K (2000). "Origin of complexity in multicellular organisms". Phys. Rev. Lett. 84 (26 Pt 1): 6130–3. arXiv:nlin/0009008Freely accessible. Bibcode:2000PhRvL..84.6130F. doi:10.1103/PhysRevLett.84.6130. PMID 10991141.
  25. Adami C, Ofria C, Collier TC (2000). "Evolution of biological complexity". Proc. Natl. Acad. Sci. U.S.A. 97 (9): 4463–8. arXiv:physics/0005074Freely accessible. Bibcode:2000PNAS...97.4463A. doi:10.1073/pnas.97.9.4463. PMC 18257Freely accessible. PMID 10781045.
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