| 词条 | Complex adaptive system |
| 释义 |
A complex adaptive system is a system in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system's behavior.[1] The study of complex adaptive systems, a subset of nonlinear dynamical systems,[2] is highly interdisciplinary and blends insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behavior.[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.[5][1] They are a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to adapt to the changing environment and increase their survivability as a macro-structure.[5][9] OverviewThe 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. The study of CAS focuses on complex, emergent and macroscopic properties of the system.[9][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."[4] 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;[14] the brain and the immune system; and the cell and the developing embryo. Human social group-based endeavors, such as political parties, communities, geopolitical organizations, war, and terrorist networks are also considered CAS.[14][16][5] The internet and cyberspace—composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system.[18][19][20] CAS can be hierarchical, but more often exhibit aspects of "self-organization."[6] General propertiesWhat 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 characterized 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 analyzed with game theory. CharacteristicsSome of the most important characteristics of complex systems are:[7]
Robert Axelrod & Michael D. Cohen[8] identify a series of key terms from a modeling perspective:
Modeling and simulationCAS are occasionally modeled by means of agent-based models and complex network-based models.[9] Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model.[10] Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components.[11] In 2013 SpringerOpen/BioMed Central has launched an online open-access journal on the topic of complex adaptive systems modeling (CASM).[12] Evolution of complexity{{Main|Evolution of biological complexity}}Living organisms are complex adaptive systems. Although complexity is hard to quantify in biology, evolution has produced some remarkably complex organisms.[13] This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".[14] 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.[15] Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution.[16][17] However, the idea of a general trend towards complexity in evolution can also be explained through a passive process.[15] 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,[18] which comprise about half the world's biomass[19] and constitute the vast majority of Earth's biodiversity.[20] Therefore, simple life remains dominant on Earth, and complex life appears more diverse only because of sampling bias. If there is a lack of an overall trend towards complexity in biology, this would not preclude the existence of forces driving systems towards complexity in a subset of cases. These minor trends would be balanced by other evolutionary pressures that drive systems towards less complex states. See also{{Portal|Systems science}}{{div col|colwidth=30em}}
References1. ^1 {{Cite book|url=https://www.worldcat.org/oclc/760073369|title=Complex adaptive systems : an introduction to computational models of social life|last=Miller, John H., and Scott E. Page|date=2007-01-01|publisher=Princeton University Press|isbn=9781400835522|location=|pages=|oclc=760073369}} [21][22][23][24][25][26][27][28][29]2. ^{{cite journal | last=Lansing | first=J. Stephen | title=Complex Adaptive Systems | journal=Annual Review of Anthropology | publisher=Annual Reviews | volume=32 | issue=1 | year=2003 | issn=0084-6570 | doi=10.1146/annurev.anthro.32.061002.093440 | pages=183–204}} 3. ^{{Cite news|url=http://www.slate.com/articles/technology/bitwise/2016/01/a_crude_look_at_the_whole_looks_at_complexity_theory_which_wants_to_understand.html|title=The Theory of Everything and Then Some|last=Auerbach|first=David|date=2016-01-19|work=Slate|access-date=2017-03-07|language=en-US|issn=1091-2339}} 4. ^{{cite journal|year=2006|title=Studying Complex Adaptive Systems|url=http://hdl.handle.net/2027.42/41486|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}} 5. ^{{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|accessdate=25 August 2013}} 6. ^{{Cite book|url=https://www.worldcat.org/oclc/970420200|title=Hidden order : how adaptation builds complexity|last=Holland, John H. (John Henry),|first=|date=1996|publisher=Addison-Wesley|year=|isbn=0201442302|location=|pages=|oclc=970420200}} 7. ^Paul Cilliers (1998) Complexity and Postmodernism: Understanding Complex Systems 8. ^Robert Axelrod & Michael D. Cohen, Harnessing Complexity. Basic Books, 2001 9. ^Muaz A. K. Niazi, Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems [https://dspace.stir.ac.uk/handle/1893/3365 PhD Thesis] 10. ^John H. Miller & Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press Book page 11. ^Melanie Mitchell, Complexity A Guided Tour, Oxford University Press, Book page 12. ^Springer [https://casmodeling.springeropen.com/ Complex Adaptive Systems Modeling Journal] (CASM) 13. ^{{cite journal |author=Adami C |title=What is complexity? |journal=BioEssays |volume=24 |issue=12 |pages=1085–94 |year=2002 |pmid=12447974 |doi=10.1002/bies.10192}} 14. ^{{cite journal |author=McShea D |title=Complexity and evolution: What everybody knows |journal=Biology and Philosophy |volume=6 |issue=3 |pages=303–24 |year=1991 |doi=10.1007/BF00132234}} 15. ^1 {{cite journal |author=Carroll SB |title=Chance and necessity: the evolution of morphological complexity and diversity |journal=Nature |volume=409 |issue=6823 |pages=1102–9 |year=2001 |pmid=11234024 |doi=10.1038/35059227|bibcode = 2001Natur.409.1102C }} 16. ^{{cite journal |vauthors=Furusawa C, Kaneko K |title=Origin of complexity in multicellular organisms |journal=Phys. Rev. Lett. |volume=84 |issue=26 Pt 1 |pages=6130–3 |year=2000 |pmid=10991141 |doi=10.1103/PhysRevLett.84.6130 |bibcode=2000PhRvL..84.6130F|arxiv = nlin/0009008 }} 17. ^{{cite journal |vauthors=Adami C, Ofria C, Collier TC |title=Evolution of biological complexity |url=http://www.pnas.org/cgi/content/full/97/9/4463 |journal=Proc. Natl. Acad. Sci. U.S.A. |volume=97 |issue=9 |pages=4463–8 |year=2000 |pmid=10781045 |doi=10.1073/pnas.97.9.4463 |pmc=18257|arxiv = physics/0005074 |bibcode = 2000PNAS...97.4463A }} 18. ^{{cite journal |author=Oren A |title=Prokaryote diversity and taxonomy: current status and future challenges |pmc=1693353 |journal=Philos. Trans. R. Soc. Lond. B Biol. Sci. |volume=359 |issue=1444 |pages=623–38 |year=2004 |pmid=15253349 |doi=10.1098/rstb.2003.1458}} 19. ^{{cite journal |vauthors=Whitman W, Coleman D, Wiebe W | title = Prokaryotes: the unseen majority | url=http://www.pnas.org/cgi/content/full/95/12/6578 | journal = Proc Natl Acad Sci USA | volume = 95 | issue = 12 | pages = 6578–83 | year = 1998 |pmid = 9618454 | doi = 10.1073/pnas.95.12.6578 | pmc = 33863|bibcode = 1998PNAS...95.6578W }} 20. ^{{cite journal |vauthors=Schloss P, Handelsman J |title=Status of the microbial census |pmc=539005 |url=http://mmbr.asm.org/cgi/pmidlookup?view=long&pmid=15590780 |journal=Microbiol Mol Biol Rev |volume=68 |issue=4 |pages=686–91 |year=2004 |pmid=15590780 |doi=10.1128/MMBR.68.4.686-691.2004}} 21. ^1 2 {{cite web |url=http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.98.3514&rep=rep1&type=pdf|title=Ten Principles of Complexity & Enabling Infrastructures |publisher= by Professor Eve Mitleton-Kelly, Director Complexity Research Programme, London School of Economics |date= |accessdate=1 June 2012 }} 22. ^1 {{cite web|title=A Complex Adaptive Organization Under the Lens of the LIFE Model:The Case of Wikipedia|url=https://otago.academia.edu/JeanBaptisteFaucher/Papers/489796/A_Complex_Adaptive_Organization_Under_the_Lens_of_the_LIFE_Model_The_Case_of_Wikipedia|accessdate=25 August 2012}} 23. ^1 2 {{cite web|title=Evolutionary Psychology, Complex Systems, and Social Theory|url=http://web.eecs.utk.edu/~mclennan/papers/EPCSST.pdf|work=Bruce MacLennan, Department of Electrical Engineering & Computer Science, University of Tennessee, Knoxville|publisher=eecs.utk.edu|accessdate=25 August 2012}} 24. ^1 {{cite web|title=Complex Adaptive Systems as a Model for Evaluating Organisational : Change Caused by the Introduction of Health Information Systems|url=http://www.uow.edu.au/~kd21/uploads/Diment-complexity.pdf|work=Kieren Diment, Ping Yu, Karin Garrety, Health Informatics Research Lab, Faculty of Informatics, University of Wollongong, School of Management, University of Wollongong, NSW|publisher=uow.edu.au|accessdate=25 August 2012|deadurl=yes|archiveurl=https://web.archive.org/web/20120905170544/http://www.uow.edu.au/~kd21/uploads/Diment-complexity.pdf|archivedate=5 September 2012|df=dmy-all}} 25. ^1 {{cite web|title=The Internet Analyzed as a Complex Adaptive System|url=http://spacecollective.org/aloksubbarao/5730/The-Internet-Analyzed-as-a-Complex-Adaptive-System|accessdate=25 August 2012}} 26. ^1 {{cite web|title=Cyberspace: The Ultimate Complex Adaptive System|url=http://www.dodccrp.org/files/IC2J_v4n2_03_Phister.pdf|publisher=The International C2 Journal|accessdate=25 August 2012}} by Paul W. Phister Jr 27. ^1 {{cite web|title=Complex Adaptive Systems|url=http://web.mit.edu/esd.83/www/notebook/Complex%20Adaptive%20Systems.pdf|publisher=mit.edu|year=2001|accessdate=25 August 2012}} by Serena Chan, Research Seminar in Engineering Systems 28. ^1 2 Steven Strogatz, Duncan J. Watts and Albert-László Barabási {{cite web |title = 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 |work = BBC / Discovery |url=http://topdocumentaryfilms.com/six-degrees-of-separation/|publisher= |page = |date=|accessdate=11 June 2012}} "Unfolding the science behind the idea of six degrees of separation" 29. ^1 {{cite web|title=Toward a Complex Adaptive Intelligence Community The Wiki and the Blog|url=https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/csi-studies/studies/vol49no3/html_files/Wik_and_%20Blog_7.htm|work=D. Calvin Andrus|publisher=cia.gov|accessdate=25 August 2012}} }} Literature{{refbegin|2}}
External links{{commons category|Complex adaptive systems}}
3 : Complex systems theory|Systems|Organizational theory |
| 随便看 |
|
开放百科全书收录14589846条英语、德语、日语等多语种百科知识，基本涵盖了大多数领域的百科知识，是一部内容自由、开放的电子版国际百科全书。