Complex adaptive system

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Complex adaptive systems are 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. 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]

Overview[edit]

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 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 in economics and social systems.

The study of CAS focuses on complex, emergent and macroscopic properties of the system.[3][10][11] John H. Holland said that CAS "are systems that have a large numbers of components, often called agents, that interact and adapt or learn."[12]

General properties[edit]

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; where as 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.

Characteristics[edit]

Some of the most important characteristics of complex systems are:[13]

  • The number of elements is sufficiently large that conventional descriptions (e.g. a system of differential equations) 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
  • Such systems may be open and it may be difficult or impossible to define system boundaries
  • Complex systems operate under far from equilibrium conditions. There has to be a constant flow of energy to maintain the organization of the system
  • Complex systems have a history. They evolve and their past is co-responsible for their present behaviour
  • 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 & Michael D. Cohen[14] identify a series of key terms from a modeling perspective:

  • 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

Modeling and Simulation[edit]

CAS are occasionally modeled by means of agent-based models and complex network-based models.[15] Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model.[16] Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components.[17]

Recently SpringerOpen/BioMed Central has launched an online open-access journal on the topic of Complex Adaptive Systems Modeling (CASM).[18]

Evolution of complexity[edit]

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.[19] This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".[20]

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.[21] Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution.[22][23]

However, the idea of a general trend towards complexity in evolution can also be explained through a passive process.[21] 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,[24] which comprise about half the world's biomass[25] and constitute the vast majority of Earth's biodiversity.[26] 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[edit]

References[edit]

  1. ^ a b "Insights from Complexity Theory: Understanding Organisations better". by Assoc. Prof. Amit Gupta, Student contributor - S. Anish , IIM Bangalore. Retrieved 1 June 2012. 
  2. ^ a b "Ten Principles of Complexity & Enabling Infrastructures". by Professor Eve Mitleton-Kelly, Director Complexity Research Programme, London School of Economics. Retrieved 1 June 2012. 
  3. ^ a b "Evolutionary Psychology, Complex Systems, and Social Theory". Bruce MacLennan, Department of Electrical Engineering & Computer Science, University of Tennessee, Knoxville. eecs.utk.edu. Retrieved 25 August 2012. 
  4. ^ a b 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. cia.gov. 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". The International C2 Journal. Retrieved 25 August 2012.  by Paul W. Phister Jr
  9. ^ "Complex Adaptive Systems". mit.edu. 2001. Retrieved 25 August 2012.  by Serena Chan, Research Seminar in Engineering Systems
  10. ^ "A Complex Adaptive Organization Under the Lens of the LIFE Model:The Case of Wikipedia". Retrieved 25 August 2012. 
  11. ^ "Complex Adaptive Systems as a Model for Evaluating Organisational : Change Caused by the Introduction of Health Information Systems". Kieren Diment, Ping Yu, Karin Garrety, Health Informatics Research Lab, Faculty of Informatics, University of Wollongong, School of Management, University of Wollongong, NSW. uow.edu.au. Retrieved 25 August 2012. 
  12. ^ Holland, John H.; (2006). "Studying Complex Adaptive Systems." Journal of Systems Science and Complexity 19 (1): 1-8. http://hdl.handle.net/2027.42/41486
  13. ^ Paul Cilliers (1998) Complexity and Postmodernism: Understanding Complex Systems
  14. ^ Robert Axelrod & Michael D. Cohen, Harnessing Complexity. Basic Books, 2001
  15. ^ 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
  16. ^ John H. Miller & Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press Book page
  17. ^ Melanie Mitchell, Complexity A Guided Tour, Oxford University Press, Book page
  18. ^ Springer Complex Adaptive Systems Modeling Journal (CASM)
  19. ^ Adami C (2002). "What is complexity?". BioEssays 24 (12): 1085–94. doi:10.1002/bies.10192. PMID 12447974. 
  20. ^ McShea D (1991). "Complexity and evolution: What everybody knows". Biology and Philosophy 6 (3): 303–24. doi:10.1007/BF00132234. 
  21. ^ a b Carroll SB (2001). "Chance and necessity: the evolution of morphological complexity and diversity". Nature 409 (6823): 1102–9. doi:10.1038/35059227. PMID 11234024. 
  22. ^ Furusawa C, Kaneko K (2000). "Origin of complexity in multicellular organisms". Phys. Rev. Lett. 84 (26 Pt 1): 6130–3. arXiv:nlin/0009008. Bibcode:2000PhRvL..84.6130F. doi:10.1103/PhysRevLett.84.6130. PMID 10991141. 
  23. ^ Adami C, Ofria C, Collier TC (2000). "Evolution of biological complexity". Proc. Natl. Acad. Sci. U.S.A. 97 (9): 4463–8. arXiv:physics/0005074. Bibcode:2000PNAS...97.4463A. doi:10.1073/pnas.97.9.4463. PMC 18257. PMID 10781045. 
  24. ^ Oren A (2004). "Prokaryote diversity and taxonomy: current status and future challenges". Philos. Trans. R. Soc. Lond., B, Biol. Sci. 359 (1444): 623–38. doi:10.1098/rstb.2003.1458. PMC 1693353. PMID 15253349. 
  25. ^ Whitman W, Coleman D, Wiebe W (1998). "Prokaryotes: the unseen majority". Proc Natl Acad Sci USA 95 (12): 6578–83. Bibcode:1998PNAS...95.6578W. doi:10.1073/pnas.95.12.6578. PMC 33863. PMID 9618454. 
  26. ^ Schloss P, Handelsman J (2004). "Status of the microbial census". Microbiol Mol Biol Rev 68 (4): 686–91. doi:10.1128/MMBR.68.4.686-691.2004. PMC 539005. PMID 15590780. 

Literature[edit]

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