Complex adaptive system


Complex adaptive system

Complex adaptive systems are special cases of complex systems. They are complex in that they are dynamic networks of interactions and relationships not aggregations of static entities. They are adaptive in that their individual and collective behaviour changes as a result of experience.[1][Need quotation to verify]

Contents

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 highly interdisciplinary, seeking the answers to some fundamental questions about living, adaptable, changeable systems.

Examples of complex adaptive systems include the stock market, social insect and ant colonies, 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 cultural and social system such as political parties or communities. There are close relationships between the field of CAS and artificial life. 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.

Definitions

A CAS is a complex, self-similar collection of interacting adaptive agents. The study of CAS focuses on complex, emergent and macroscopic properties of the system. Various definitions have been offered by different researchers:

  • John H. Holland "Cas [complex adaptive systems] are systems that have a large numbers of components, often called agents, that interact and adapt or learn." [2]

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 simply defined as a system composed of multiple interacting agents. In CASs, the agents as well as the system are adaptive: 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 of course 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 can, in some cases be analysed with game theory.

Characteristics

Complex adaptive systems are characterized as follows[3] and the most important are:

  • 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, the elements also have to interact and the interaction must be dynamic. Interactions can be physical or involve the exchange of information.
  • Such interactions are rich, i.e. any element in the system is affected and affects several other systems.
  • The interactions are non-linear which means that small causes can have large results.
  • 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 are 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
  • All complex systems have a history, they evolve and their past is co-responsible for their present behaviour
  • Elements in the system are ignorant of the behaviour of the system as a whole, responding only to what is available to it locally

Axelrod & Cohen [4] 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.

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.[5] This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".[6]

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.[7] Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution.[8][9]

However, the idea of a general trend towards complexity in evolution can also be explained through a passive process.[7] 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,[10] which comprise about half the world's biomass[11] and constitute the vast majority of Earth's biodiversity.[12] 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

References

  1. ^ A Juarrero. (2000). Dynamics in Action: Intentional behaviour as a complex system. MIT Press. ISBN 9780262100816. 
  2. ^ 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
  3. ^ Cilliers Paul, Complexity and Post Modernism http://www.amazon.com/Complexity-Postmodernism-Understanding-Complex-Systems/dp/0415152879
  4. ^ Harnessing Complexity
  5. ^ Adami C (2002). "What is complexity?". Bioessays 24 (12): 1085–94. doi:10.1002/bies.10192. PMID 12447974. 
  6. ^ McShea D (1991). "Complexity and evolution: What everybody knows". Biology and Philosophy 6 (3): 303–24. doi:10.1007/BF00132234. 
  7. ^ 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. 
  8. ^ Furusawa C, Kaneko K (2000). "Origin of complexity in multicellular organisms". Phys. Rev. Lett. 84 (26 Pt 1): 6130–3. Bibcode 2000PhRvL..84.6130F. doi:10.1103/PhysRevLett.84.6130. PMID 10991141. 
  9. ^ Adami C, Ofria C, Collier TC (2000). "Evolution of biological complexity". Proc. Natl. Acad. Sci. U.S.A. 97 (9): 4463–8. doi:10.1073/pnas.97.9.4463. PMC 18257. PMID 10781045. http://www.pnas.org/cgi/content/full/97/9/4463. 
  10. ^ 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. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1693353. 
  11. ^ Whitman W, Coleman D, Wiebe W (1998). "Prokaryotes: the unseen majority". Proc Natl Acad Sci USA 95 (12): 6578–83. doi:10.1073/pnas.95.12.6578. PMC 33863. PMID 9618454. http://www.pnas.org/cgi/content/full/95/12/6578. 
  12. ^ 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. http://mmbr.asm.org/cgi/pmidlookup?view=long&pmid=15590780. 

Literature

External links


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