Wednesday, June 9, 2010

Complex adaptive system

Complex adaptive system


Complex adaptive systems are special cases of complex systems. They are complex in that they are diverse and made up of multiple interconnected elements (and so a part ofnetwork science) and adaptive in that they have the capacity to change and learn from experience. The term complex adaptive systems (CAS) was coined at the interdisciplinary Santa Fe Institute (SFI), by John H. Holland, Murray Gell-Mann and others.[citation needed]

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[edit]Overview

Complex Adaptive System

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 theecosystem, 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 biological views on adaptation andevolution. The theory of complex adaptive systems bridges developments of systems theory with the ideas of generalized Darwinism, which suggests that Darwinian principles of evolution can explain a range of complex material phenomena, from cosmic to social objects.

[edit]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:

A Complex Adaptive System (CAS) is a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it has to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment by many individual agents.[1]
A CAS behaves/evolves according to three key principles: order is emergent as opposed to predetermined (c.f. Neural Networks), the system's history is irreversible, and the system's future is often unpredictable. The basic building blocks of the CAS are agents. Agents scan their environment and develop schema representing interpretive and action rules. These schema are subject to change and evolution.[2]
  • Other definitions
Macroscopic collections of simple (and typically nonlinear) interacting units that are endowed with the ability to evolve and adapt to a changing environment.[3]

[edit]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 ofperturbation.

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 be analysed with game theory. Many of the issues of complexity science and new tools for the analysis of complexity are being developed within network science.[citation needed]

[edit]Properties

Complex adaptive systems have many properties[4] and the most important are:

  • Emergence: Rather than being planned or controlled the agents in the system interact in apparently random ways. From all these interactions patterns emerge which informs the behaviour of the agents within the system and the behaviour of the system itself. For example a termite hill is a wondrous piece of architecture with a maze of interconnecting passages, large caverns, ventilation tunnels and much more. Yet there is no grand plan, the hill just emerges as a result of the termites following a few simple local rules.
  • Co-evolution: All systems exist within their own environment and they are also part of that environment. Therefore, as their environment changes they need to change to ensure best fit. But because they are part of their environment, when they change, they change their environment, and as it has changed they need to change again, and so it goes on as a constant process. Some people draw a distinction between complex adaptive systems and complex evolving systems. Where the former continuously adapt to the changes around them but do not learn from the process. And where the latter learn and evolve from each change enabling them to influence their environment, better predict likely changes in the future, and prepare for them accordingly.
  • Sub optimal: A complex adaptive systems does not have to be perfect in order for it to thrive within its environment. It only has to be slightly better than its competitors and any energy used on being better than that is wasted energy. A complex adaptive systems once it has reached the state of being good enough will trade off increased efficiency every time in favour of greater effectiveness.
  • Requisite Variety: The greater the variety within the system the stronger it is. In fact ambiguity and paradox abound in complex adaptive systems which use contradictions to create new possibilities to co-evolve with their environment. Democracy is a good example in that its strength is derived from its tolerance and even insistence in a variety of political perspectives.
  • Connectivity: The ways in which the agents in a system connect and relate to one another is critical to the survival of the system, because it is from these connections that the patterns are formed and the feedback disseminated. The relationships between the agents are generally more important than the agents themselves.
  • Simple Rules: Complex adaptive systems are not complicated. The emerging patterns may have a rich variety, but like a kaleidoscope the rules governing the function of the system are quite simple. A classic example is that all the water systems in the world, all the streams, rivers, lakes, oceans, waterfalls etc with their infinite beauty, power and variety are governed by the simple principle that water finds its own level.
  • Iteration: Small changes in the initial conditions of the system can have significant effects after they have passed through the emergence - feedback loop a few times. A rolling snowball for example gains on each roll much more snow than it did on the previous roll and very soon a fist sized snowball becomes a giant one.
  • Self Organising: There is no hierarchy of command and control in a complex adaptive system. There is no planning or managing, but there is a constant re-organising to find the best fit with the environment. A classic example is that if one were to take any western town and add up all the food in the shops and divide by the number of people in the town there will be near enough two weeks supply of food, but there is no food plan, food manager or any other formal controlling process. The system is continually self organising through the process of emergence and feedback.
  • Edge of Chaos: Complexity theory is not the same as chaos theory, which is derived from mathematics. But chaos does have a place in complexity theory in that systems exist on a spectrum ranging from equilibrium to chaos. A system in equilibrium does not have the internal dynamics to enable it to respond to its environment and will slowly (or quickly) die. A system in chaos ceases to function as a system. The most productive state to be in is at the edge of chaos where there is maximum variety and creativity, leading to new possibilities.
  • Nested Systems: Most systems are nested within other systems and many systems are systems of smaller systems. If we take the example in self organising above and consider a food shop. The shop is itself a system with its staff, customers, suppliers, and neighbours. It also belongs within the food system of that town and the larger food system of that country. It belongs to the retail system locally and nationally and the economy system locally and nationally, and probably many more. Therefore it is part of many different systems most of which are themselves part of other systems.

[edit]Management

When used in the management of people, CAS includes [1] setting appropriate containers, [2] understanding significant differences, and [3] facilitating transformation exchanges. In a CAS, managers set guidelines for workers to interpret, and use to self-organize.

[edit]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.
Main article: Evolution of complexity

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 microscopicprokaryotes,[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.

[edit]See also

[edit]References

  1. ^ M. Mitchell Waldrop. (1994). Complexity: the emerging science at the edge of order and chaos. Harmondsworth [Eng.]: Penguin. ISBN 0-14-017968-2.
  2. ^ K. Dooley, AZ State University
  3. ^ Complexity in Social Science glossary a research training project of the European Commission
  4. ^ Peter Fryer. [http://www.trojanmice.com/articles/complexadaptivesystems.htm "A brief description of Complex Adaptive Systems and Complexity Theory"]. Retrieved 2010-01-24.
  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. 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. PMID 10781045. PMC 18257.
  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.PMID 15253349.
  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. PMID 9618454.PMC 33863.
  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. PMID 15590780. PMC 539005.

[edit]Literature

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