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Moved page from wikipedia:en:Complex adaptive system (history)
此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。{{Use dmy dates|date=July 2013}}
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.<ref name="Miller, John H., and Scott E. Page">{{Cite book|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}}</ref> In complex adaptive systems, the whole is more complex than its parts,<ref>Holland, J. H. (1998). Emergence: From chaos to order. Reading, MA: Helix Books.</ref> and more complicated and meaningful than the aggregate of its parts. The study of complex adaptive systems, a subset of [[nonlinear dynamical system]]s,<ref name="Lansing 2003 pp. 183–204">{{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}}</ref> 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]].<ref>{{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}}</ref>
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. In complex adaptive systems, the whole is more complex than its parts, and more complicated and meaningful than the aggregate of its parts. The study of complex adaptive systems, a subset of nonlinear dynamical systems, 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.
复杂适应性系统是一个系统，在这个系统中，对个别部分的完美理解并不能自动传达对整个系统行为的完美理解。在复杂适应系统中，整体比部分更复杂，比部分的集合更复杂、更有意义。复杂适应系统是非线性动力系统的一个子集，其研究是高度跨学科的，融合了自然科学和社会科学的见解，以开发系统级的模型和见解，允许异质的代理人，相变，和突现行为。
They are ''[[complex system|complex]]'' in that they are [[Dynamic network analysis|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-organizing|self-organize]] corresponding to the change-initiating micro-event or collection of events.<ref name=CAS-T-01/><ref name=CAS-T-02/><ref name="Miller, John H., and Scott E. Page"/> They are a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to [[Adaptive system|adapt]] to the changing environment and increase their survivability as a [[macrostructure (sociology)|macro-structure]].<ref name=CAS-T-01/><ref name=CAS-T-02/><ref name=CAS-T-12/> The Complex Adaptive Systems approach builds on [[replicator dynamics]].<ref>{{cite journal |last1=Foster |first1=John |date=2006 |title=Why is economics not a complex systems science? |url=https://espace.library.uq.edu.au/view/UQ:9953/econ_dp_336_04.pdf |journal=Journal of Economic Issues |volume=40 |issue=4 |pages=1069-1091 |doi=10.1080/00213624.2006.11506975 |access-date=2020-01-18 }}</ref>
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.
它们是复杂的，因为它们是动态的交互网络，它们的关系不是单个静态实体的聚合，也就是说，集合的行为不是由组件的行为所预测的。它们是适应性的，因为个体和集体的行为会随着引发变化的微事件或事件的集合而变异和自我组织。
== Overview ==
== Overview ==
概览
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 [[life|living]], adaptable, changeable systems. Complex adaptive systems may adopt hard or softer approaches <ref>{{cite journal |last1=Yolles |first1=Maurice |title=The complexity continuum, Part 1: hard and soft theories |journal=Kybernetes |date=2018 |volume=48 |issue=6 |pages=1330–1354 |doi=10.1108/K-06-2018-0337}}</ref>. Hard theories use formal language that is precise, tend to see agents as having tangible properties, and usually see objects in a behavioral system that can be manipulated in some way. Softer theories use natural language and narratives that may be imprecise, and agents are subjects having both tangible and intangible properties. Examples of hard complexity theories include Complex Adaptive Systems (CAS) and [[Viability_theory|Viability Theory]], and a class of softer theory is [[Viable_system_theory|Viable System Theory]]. Many of the propositional consideration made in hard theory are also of relevance to softer theory. From here on, interest will now center on CAS.
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. Complex adaptive systems may adopt hard or softer approaches . Hard theories use formal language that is precise, tend to see agents as having tangible properties, and usually see objects in a behavioral system that can be manipulated in some way. Softer theories use natural language and narratives that may be imprecise, and agents are subjects having both tangible and intangible properties. Examples of hard complexity theories include Complex Adaptive Systems (CAS) and Viability Theory, and a class of softer theory is Viable System Theory. Many of the propositional consideration made in hard theory are also of relevance to softer theory. From here on, interest will now center on CAS.
复杂适应系统这个术语，或者复杂性科学，经常被用来描述围绕这类系统研究而成长起来的松散组织的学术领域。复杂性科学不是一个单一的理论---- 它包含不止一个理论框架，并且是高度跨学科的，寻求一些关于活的、可适应的、可变的系统的基本问题的答案。复杂适应系统可能采用硬方法或软方法。硬理论使用精确的形式语言，倾向于认为代理人具有有形的属性，并且通常认为行为系统中的物体可以以某种方式被操纵。软理论使用自然语言和可能不精确的叙述，而代理人是同时具有有形和无形属性的主体。硬复杂性理论的例子包括复杂适应系统(CAS)和生存理论，一类较为软的理论是生存系统理论。硬理论中提出的许多命题考虑也与较软理论相关。从现在开始，人们的兴趣将集中在中科院。
The study of CAS focuses on complex, emergent and macroscopic properties of the system.<ref name="CAS-T-12" /><ref name="CAS-T-11" /><ref name="CAS-T-13" /> [[John Henry Holland|John H. Holland]] said that CAS "are systems that have a large numbers of components, often called agents, that interact and adapt or learn".<ref>{{cite journal|year=2006|title=Studying Complex Adaptive Systems|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|hdl=2027.42/41486|url=https://deepblue.lib.umich.edu/bitstream/2027.42/41486/1/11424_2006_Article_1.pdf}}</ref>
The study of CAS focuses on complex, emergent and macroscopic properties of the system.
复杂适应系统的研究主要集中在系统的复杂性、突发性和宏观性上。
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;<ref name=AFC-NA-21/> the [[brain]] and the [[immune system]]; and the [[cell (biology)|cell]] and the developing [[embryo]]. Human social group-based endeavors, such as [[political party|political parties]], [[community|communities]], [[geopolitical]] [[organisations|organizations]], [[war]], and [[terrorist network analysis|terrorist networks]] are also considered CAS.<ref name=AFC-NA-21/><ref name=GT-33/><ref name=CAS-T-19>{{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}}</ref> The [[internet]] and [[cyberspace]]—composed, collaborated, and managed by a complex mix of [[human–computer interaction]]s, is also regarded as a complex adaptive system.<ref name=CAS-T-16/><ref name=CAS-T-17/><ref name=CAS-T-18/> CAS can be hierarchical, but more often exhibit aspects of "self-organization".<ref>{{Cite book|title=Hidden order: how adaptation builds complexity|last=Holland, John H. (John Henry)|date=1996|publisher=Addison-Wesley|isbn=0201442302|location=|pages=|oclc=970420200}}</ref>
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; The internet and cyberspace—composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system.
复杂适应系统的典型例子包括: 气候; 城市; 企业; 市场; 政府; 工业; 生态系统; 社会网络; 电网; 动物群落; 交通流量; 社会昆虫(例如:。蚁群: 互联网和网络空间ー由复杂的人机交互组成、协作和管理，也被视为复杂适应性系统。
=== General properties ===
=== General properties ===
一般性质
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 characterized by a high degree of [[adaptive capacity]], giving them resilience in the face of [[wikt:perturbation|perturbation]].
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 characterized by a high degree of adaptive capacity, giving them resilience in the face of perturbation.
Cas 与纯多智能体系统的区别在于，它关注的是顶级属性和特征，比如自相似性、复杂性、出现性和自我组织。多智能体系统是由多个相互作用的智能体组成的系统，而在 CAS 系统中，智能体和系统是自适应的，系统是自相似的。Cas 是一个复杂的、自相似的、相互作用的自适应代理的总体。复杂适应系统具有高度的适应能力，使拥有属性在面对干扰时具有弹性。
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]].
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.
其他重要的属性是适应(或内稳态) ，沟通，合作，专业化，时空组织和繁殖。它们可以在各个层面上被发现: 细胞专门化、适应和繁殖，就像大型生物一样。沟通和合作发生在各个层面，从代理到系统层面。在某些情况下，可以用博弈论分析这种系统中主体之间合作的驱动力。
=== Characteristics ===
=== Characteristics ===
特点
Some of the most important characteristics of complex systems are:<ref>[[Paul Cilliers]] (1998) ''Complexity and Postmodernism: Understanding Complex Systems''</ref>
Some of the most important characteristics of complex systems are:
复杂系统的一些最重要的特征是:
* The number of elements is sufficiently large that conventional descriptions (e.g. a system of [[differential equation]]s) 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''
* The overall behavior of the system of elements is not predicted by the behavior of the individual elements
* Such systems may be open and it may be difficult or impossible to define system boundaries
* Complex systems operate under [[Non-equilibrium thermodynamics|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 (academic)|Michael D. Cohen]]<ref>[[Robert Axelrod]] & [[Michael D. Cohen (academic)|Michael D. Cohen]], ''Harnessing Complexity''. [[Basic Books]], 2001</ref> identify a series of key terms from a modeling perspective:
Robert Axelrod & Michael D. Cohen identify a series of key terms from a modeling perspective:
从建模的角度确定了一系列关键迈克尔·科恩:
* '''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
Turner and Baker<ref>Turner, J. R., & Baker, R. (in press). Just doing the do: A case study testing creativity and innovative processes as complex adaptive systems. New Horizons in Adult Education and Human Resource Development.</ref> synthesized the characteristics of complex adaptive systems from the literature and tested these characteristics in the context of creativity and innovation. Each of these eight characteristics had been shown to be present in the creativity and innovative processes:
Turner and Baker synthesized the characteristics of complex adaptive systems from the literature and tested these characteristics in the context of creativity and innovation. Each of these eight characteristics had been shown to be present in the creativity and innovative processes:
特纳和贝克从文献中综合了复杂适应系统的特征，并在创造力和创新的背景下测试了这些特征。这八个特点中的每一个都显示出在创造性和创新过程中存在:
* '''Path dependent:''' Systems tend to be sensitive to their initial conditions. The same force might affect systems differently.<ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref>
* '''Systems have a history:''' The future behavior of a system depends on its initial starting point and subsequent history.<ref>{{cite journal | last1 = Boal | first1 = K. B. | last2 = Schultz | first2 = P. L. | year = 2007 | title = Storytelling, time, and evolution: The role of strategic leadership in complex adaptive systems | url = | journal = The Leadership Quarterly | volume = 18 | issue = 4| pages = 411–428 | doi = 10.1016/j.leaqua.2007.04.008 }}</ref>
* '''Non-linearity:''' React disproportionately to environmental perturbations. Outcomes differ from those of simple systems.<ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref> <ref>{{cite journal | last1 = Luoma | first1 = M | year = 2006 | title = A play of four arenas - How complexity can serve management development | url = | journal = Management Learning | volume = 37 | issue = | pages = 101–123 | doi = 10.1177/1350507606058136 }}</ref>
* '''Emergence:''' Each system's internal dynamics affect its ability to change in a manner that might be quite different from other systems.<ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref>
* '''Irreducible:''' Irreversible process transformations cannot be reduced back to its original state.<ref>{{cite journal | last1 = Borzillo | first1 = S. | last2 = Kaminska-Labbe | first2 = R. | year = 2011 | title = Unravelling the dynamics of knowledge creation in communities of practice through complexity theory lenses | url = | journal = Knowledge Management Research & Practice | volume = 9 | issue = | pages = 353–366 | doi = 10.1057/kmrp.2011.13 }}</ref>
* '''Adaptive/Adaptability:''' Systems that are simultaneously ordered and disordered are more adaptable and resilient.<ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref>
* '''Operates between order and chaos:''' Adaptive tension emerges from the energy differential between the system and its environment.<ref>{{cite journal | last1 = Borzillo | first1 = S. | last2 = Kaminska-Labbe | first2 = R. | year = 2011 | title = Unravelling the dynamics of knowledge creation in communities of practice through complexity theory lenses | url = | journal = Knowledge Management Research & Practice | volume = 9 | issue = | pages = 353–366 | doi = 10.1057/kmrp.2011.13 }}</ref>
* '''Self-organizing:''' Systems are composed of interdependency, interactions of its parts, and diversity in the system. <ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref>
== Modeling and simulation ==
== Modeling and simulation ==
建模与模拟
CAS are occasionally modeled by means of [[agent-based model]]s and [[complex network]]-based models.<ref>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]</ref> Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model.<ref>John H. Miller & Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press [http://press.princeton.edu/titles/8429.html Book page]</ref> Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components.<ref>Melanie Mitchell, Complexity A Guided Tour, Oxford University Press, [http://www.oup.com/us/catalog/general/subject/LifeSciences/~~/dmlldz11c2EmY2k9OTc4MDE5NTEyNDQxNQ== Book page]</ref>
CAS are occasionally modeled by means of agent-based models and complex network-based models. Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model. Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components.
复杂适应系统有时可以用基于代理的模型和基于复杂网络的模型来建模。基于主体的模型主要是通过识别模型中的不同主体，利用各种方法和工具开发的。开发复杂适应系统模型的另一种方法是利用复杂适应系统各组成部分的交互数据开发复杂的网络模型。
In 2013 [[SpringerOpen|SpringerOpen/BioMed Central]] has launched an online open-access journal on the topic of ''complex adaptive systems modeling'' (CASM).<ref>Springer ''[https://casmodeling.springeropen.com/ Complex Adaptive Systems Modeling Journal]'' (CASM)</ref>
In 2013 SpringerOpen/BioMed Central has launched an online open-access journal on the topic of complex adaptive systems modeling (CASM).
2013年，springeropen / biomed Central 推出了一个在线开放获取期刊，主题是复杂适应性系统建模。
== Evolution of complexity ==
== Evolution of complexity ==
复杂性的进化
[[File:Evolution of complexity.svg|thumb|300px|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.]]
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.
复杂性演变中的消极趋势与积极趋势。Cas 在过程的开始是红色的。系统数量的变化由条形图的高度来表示，每一组图在一个时间序列中向上移动。
{{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.<ref>{{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}}</ref> This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".<ref>{{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}}</ref>
Living organisms are complex adaptive systems. Although complexity is hard to quantify in biology, evolution has produced some remarkably complex organisms. This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".
生命有机体是复杂的适应系统。虽然复杂性在生物学中难以量化，但进化已经产生了一些非常复杂的生物体。这一观察结果导致了一种普遍的错误观念，认为进化是渐进的，并导致了被认为是“更高级的生物体”。
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.<ref name=Carroll>{{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 }}</ref> Indeed, some [[artificial life]] simulations have suggested that the generation of CAS is an inescapable feature of evolution.<ref>{{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 }}</ref><ref>{{cite journal |vauthors=Adami C, Ofria C, Collier TC |title=Evolution of biological complexity |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 }}</ref>
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. Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution.
如果这是普遍正确的，那么进化就会朝着复杂性的方向发展。如下所示，在这种类型的流程中，最常见的复杂性值会随着时间的推移而增加。事实上，一些人工生命模拟已经表明，CAS 的产生是进化过程中不可避免的特征。
However, the idea of a general trend towards complexity in evolution can also be explained through a passive process.<ref name=Carroll/> This involves an increase in [[variance]] but the most common value, the [[mode (statistics)|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]].
However, the idea of a general trend towards complexity in evolution can also be explained through a passive process. 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.
然而，进化的复杂性总体趋势的观点也可以通过一个被动的过程来解释。这涉及到方差的增加，但是最常见的值，模式，并没有改变。因此，最大程度的复杂性随着时间的推移而增加，但仅仅作为总体上有更多生物体的间接产物。这种类型的随机过程也称为有界随机游动。
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 [[Skewness|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 [[microorganism|microscopic]] [[prokaryote]]s,<ref>{{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}}</ref> which comprise about half the world's [[biomass]]<ref>{{cite journal |vauthors=Whitman W, Coleman D, Wiebe W | title = Prokaryotes: the unseen majority | 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 }}</ref> and constitute the vast majority of Earth's biodiversity.<ref>{{cite journal |vauthors=Schloss P, Handelsman J |title=Status of the microbial census |pmc=539005 |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}}</ref> Therefore, simple life remains dominant on Earth, and complex life appears more diverse only because of [[sampling bias]].
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, which comprise about half the world's biomass and constitute the vast majority of Earth's biodiversity. Therefore, simple life remains dominant on Earth, and complex life appears more diverse only because of sampling bias.
在这一假设中，向更复杂的生物体发展的明显趋势是一种错觉，因为它只注意居住在复杂性分布的右端的少数大型、非常复杂的生物体，而忽略了更简单和更普通的生物体。这个被动模型强调，绝大多数物种是微小的原核生物，它们构成了世界生物量的一半，构成了地球生物多样性的绝大多数。因此，简单生命在地球上仍然占主导地位，而复杂生命仅仅因为抽样偏差而显得更加多样化。
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.
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.
如果在生物学中缺乏一个复杂性的总体趋势，这并不排除在一个子集的情况下驱动系统走向复杂性的力量的存在。这些小的趋势将被其他的进化压力所平衡，这些进化压力驱使系统朝着不那么复杂的状态发展。
== See also ==
== See also ==
参见
{{Portal|Systems science}}
{{div col|colwidth=30em}}
* [[Artificial life]]
* [[Chaos theory]]
* [[Cognitive science]]
* [[Command and Control Research Program]]
* [[Complex system]]
* [[Computational economics]]
* [[Computational sociology]]
* [[Dual-phase evolution]]
* [[Enterprise systems engineering]]
* [[Generative sciences]]
* [[Open system (systems theory)]]
* [[Santa Fe Institute]]
* [[Simulated reality]]
* [[Sociology and complexity science]]
* [[Wicked problem|Super wicked problem]]
* [[Swarm Development Group]]
* [[Universal Darwinism]]
{{div col end}}
==References==
==References==
参考资料
{{Reflist|2|refs=
{{Reflist|2|refs=
{通货再膨胀 | 2 | 参考文献
<ref name=CAS-T-01>{{cite web |url=http://tejas.iimb.ac.in/articles/12.php|title=Insights from Complexity Theory: Understanding Organisations better |publisher= by Assoc. Prof. Amit Gupta, Student contributor - S. Anish, IIM Bangalore|date= |accessdate=1 June 2012 }}</ref>
<ref name=CAS-T-02>{{cite journal |title=Ten Principles of Complexity & Enabling Infrastructures |publisher= by Professor Eve Mitleton-Kelly, Director Complexity Research Programme, London School of Economics |date= |citeseerx = 10.1.1.98.3514}}</ref>
<ref name=CAS-T-11>{{cite journal|title=A Complex Adaptive Organization Under the Lens of the LIFE Model:The Case of Wikipedia|journal=Egosnet.org|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|last1=Faucher|first1=Jean-Baptiste}}</ref>
<ref name=CAS-T-12>{{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}}</ref>
<ref name=CAS-T-13>{{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|url-status=dead|archiveurl=https://web.archive.org/web/20120905170544/http://www.uow.edu.au/~kd21/uploads/Diment-complexity.pdf|archivedate=5 September 2012|df=dmy-all}}</ref>
<ref name=CAS-T-16>{{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}}</ref>
<ref name=CAS-T-17>{{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
<ref name=CAS-T-17>{{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
17{ cite web | title Cyberspace: The Ultimate 复杂适应性系统 | http://www.dodccrp.org/files/ic2j_v4n2_03_phister.pdf|publisher=The 国际 c 2 Journal
|accessdate=25 August 2012}} by Paul W. Phister Jr</ref>
|accessdate=25 August 2012}} by Paul W. Phister Jr</ref>
2012年8月25日 | accessdate }}
<ref name=CAS-T-18>{{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</ref>
<ref name=AFC-NA-21>[[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/|page = |date=|accessdate=11 June 2012}} "Unfolding the science behind the idea of six degrees of separation"</ref>
<ref name=GT-33>{{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}}</ref>
}}
}}
}}
== Literature ==
== Literature ==
文学
{{refbegin|2}}
*{{cite journal |vauthors=Ahmed E, Elgazzar AS, Hegazi AS |title=An overview of complex adaptive systems |journal=Mansoura J. Math |volume=32 |pages=6059 |date=28 June 2005 |bibcode=2005nlin......6059A |id=arXiv:nlin/0506059v1 [nlin.AO] |arxiv=nlin/0506059}}
*{{Cite journal |vauthors=Bullock S, Cliff D |title=Complexity and Emergent Behaviour in ICT Systems |publisher=Hewlett-Packard Labs |year=2004 |url=http://www.hpl.hp.com/techreports/2004/HPL-2004-187.html |id=HP-2004-187}}; commissioned as a [https://web.archive.org/web/20090207162407/http://www.foresight.gov.uk/OurWork/CompletedProjects/IIS/Docs/ComplexityandEmergentBehaviour.asp report] by the UK government's [http://www.foresight.gov.uk/ Foresight Programme].
* Dooley, K., ''Complexity in Social Science'' glossary a research training project of the European Commission.
*{{cite book |author1=Edwin E. Olson |author2=Glenda H. Eoyang |title=Facilitating Organization Change |publisher=Jossey-Bass |location=San Francisco |year=2001 |isbn=0-7879-5330-X }}
*{{cite book |author=Gell-Mann, Murray |title=The quark and the jaguar: adventures in the simple and the complex |publisher=W.H. Freeman |location=San Francisco |year=1994 |isbn=0-7167-2581-9 }}
*{{cite book |author=Holland, John H. |title=Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence |url=https://archive.org/details/adaptationinnatu00holl |url-access=registration |publisher=MIT Press |location=Cambridge, Massachusetts |year=1992 |isbn=0-262-58111-6 }}
*{{cite book |author=Holland, John H. |title=Emergence: from chaos to order |publisher=Perseus Books |location=Reading, Mass |year=1999 |isbn=0-7382-0142-1 }}
*{{cite book |author=Solvit, Samuel |title=Dimensions of War: Understanding War as a Complex Adaptive System |publisher=L'Harmattan |location=Paris, France |year=2012 |isbn=978-2-296-99721-9 }}
*{{cite book |author=Kelly, Kevin |title=Out of control: the new biology of machines, social systems and the economic world |publisher=Addison-Wesley |location=Boston |year=1994 |isbn=0-201-48340-8 |url=https://archive.org/details/outofcontrolnewb00kell |format=Full text available online |author-link=Kevin Kelly (editor) |url-access=registration }}
* {{Not a typo|Pharaoh}}, M.C. (online). [http://homepage.ntlworld.com/m.pharoah/ Looking to systems theory for a reductive explanation of phenomenal experience and evolutionary foundations for higher order thought] Retrieved 15 January 2008.
* Hobbs, George & Scheepers, Rens (2010),"Agility in Information Systems: Enabling Capabilities for the IT Function," ''Pacific Asia Journal of the Association for Information Systems'': Vol. 2: Iss. 4, Article 2. [http://aisel.aisnet.org/pajais/vol2/iss4/2 Link]
*{{cite book |author=Sidney Dekker|authorlink=Sidney Dekker|title=Drift into Failure: From Hunting Broken Components to Understanding Complex Systems|date=2011|publisher=CRC Press}}
{{refend}}
==External links==
==External links==
外部链接
{{commons category|Complex adaptive systems}}
*[http://www.cas-group.net/ Complex Adaptive Systems Group] loosely coupled group of scientists and software engineers interested in complex adaptive systems
*[http://www.dnawales.co.uk/ DNA Wales Research Group] Current Research in Organisational change CAS/CES related news and free research data. Also linked to the Business Doctor & BBC documentary series
*[http://pespmc1.vub.ac.be/CAS.html A description] of complex adaptive systems on the Principia Cybernetica Web.
* [http://bactra.org/notebooks/complexity.html Quick reference] single-page description of the 'world' of complexity and related ideas hosted by the Center for the Study of Complex Systems at the University of Michigan.
*[https://web.archive.org/web/20080723135438/http://www.complexsystems.net.au/ Complex systems research network]
* [https://web.archive.org/web/20090105183805/http://www.openabm.org/site/ The Open Agent-Based Modeling Consortium]
*[https://www.youtube.com/watch?v=jS0zj_dYeBE TEDxRotterdam - Igor Nikolic - Complex adaptive systems], and [https://www.youtube.com/watch?v=AB85AFzqtOY The emergence of universal consciousness: Brendan Hughes at TEDxPretoria ]. Talks discussing various practical examples of complex adaptive systems, including Wikipedia, star galaxies, genetic mutation, and other examples
{{Systems}}
{{DEFAULTSORT:Complex Adaptive System}}
[[Category:Complex systems theory]]
Category:Complex systems theory
范畴: 复杂系统理论
[[Category:Systems science]]
Category:Systems science
类别: 系统科学
[[Category:Organizational theory|*]]
*
*
<noinclude>
<small>This page was moved from [[wikipedia:en:Complex adaptive system]]. Its edit history can be viewed at [[复杂适应系统理论/edithistory]]</small></noinclude>
[[Category:待整理页面]]
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.<ref name="Miller, John H., and Scott E. Page">{{Cite book|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}}</ref> In complex adaptive systems, the whole is more complex than its parts,<ref>Holland, J. H. (1998). Emergence: From chaos to order. Reading, MA: Helix Books.</ref> and more complicated and meaningful than the aggregate of its parts. The study of complex adaptive systems, a subset of [[nonlinear dynamical system]]s,<ref name="Lansing 2003 pp. 183–204">{{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}}</ref> 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]].<ref>{{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}}</ref>
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. In complex adaptive systems, the whole is more complex than its parts, and more complicated and meaningful than the aggregate of its parts. The study of complex adaptive systems, a subset of nonlinear dynamical systems, 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.
复杂适应性系统是一个系统，在这个系统中，对个别部分的完美理解并不能自动传达对整个系统行为的完美理解。在复杂适应系统中，整体比部分更复杂，比部分的集合更复杂、更有意义。复杂适应系统是非线性动力系统的一个子集，其研究是高度跨学科的，融合了自然科学和社会科学的见解，以开发系统级的模型和见解，允许异质的代理人，相变，和突现行为。
They are ''[[complex system|complex]]'' in that they are [[Dynamic network analysis|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-organizing|self-organize]] corresponding to the change-initiating micro-event or collection of events.<ref name=CAS-T-01/><ref name=CAS-T-02/><ref name="Miller, John H., and Scott E. Page"/> They are a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to [[Adaptive system|adapt]] to the changing environment and increase their survivability as a [[macrostructure (sociology)|macro-structure]].<ref name=CAS-T-01/><ref name=CAS-T-02/><ref name=CAS-T-12/> The Complex Adaptive Systems approach builds on [[replicator dynamics]].<ref>{{cite journal |last1=Foster |first1=John |date=2006 |title=Why is economics not a complex systems science? |url=https://espace.library.uq.edu.au/view/UQ:9953/econ_dp_336_04.pdf |journal=Journal of Economic Issues |volume=40 |issue=4 |pages=1069-1091 |doi=10.1080/00213624.2006.11506975 |access-date=2020-01-18 }}</ref>
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.
它们是复杂的，因为它们是动态的交互网络，它们的关系不是单个静态实体的聚合，也就是说，集合的行为不是由组件的行为所预测的。它们是适应性的，因为个体和集体的行为会随着引发变化的微事件或事件的集合而变异和自我组织。
== Overview ==
== Overview ==
概览
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 [[life|living]], adaptable, changeable systems. Complex adaptive systems may adopt hard or softer approaches <ref>{{cite journal |last1=Yolles |first1=Maurice |title=The complexity continuum, Part 1: hard and soft theories |journal=Kybernetes |date=2018 |volume=48 |issue=6 |pages=1330–1354 |doi=10.1108/K-06-2018-0337}}</ref>. Hard theories use formal language that is precise, tend to see agents as having tangible properties, and usually see objects in a behavioral system that can be manipulated in some way. Softer theories use natural language and narratives that may be imprecise, and agents are subjects having both tangible and intangible properties. Examples of hard complexity theories include Complex Adaptive Systems (CAS) and [[Viability_theory|Viability Theory]], and a class of softer theory is [[Viable_system_theory|Viable System Theory]]. Many of the propositional consideration made in hard theory are also of relevance to softer theory. From here on, interest will now center on CAS.
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. Complex adaptive systems may adopt hard or softer approaches . Hard theories use formal language that is precise, tend to see agents as having tangible properties, and usually see objects in a behavioral system that can be manipulated in some way. Softer theories use natural language and narratives that may be imprecise, and agents are subjects having both tangible and intangible properties. Examples of hard complexity theories include Complex Adaptive Systems (CAS) and Viability Theory, and a class of softer theory is Viable System Theory. Many of the propositional consideration made in hard theory are also of relevance to softer theory. From here on, interest will now center on CAS.
复杂适应系统这个术语，或者复杂性科学，经常被用来描述围绕这类系统研究而成长起来的松散组织的学术领域。复杂性科学不是一个单一的理论---- 它包含不止一个理论框架，并且是高度跨学科的，寻求一些关于活的、可适应的、可变的系统的基本问题的答案。复杂适应系统可能采用硬方法或软方法。硬理论使用精确的形式语言，倾向于认为代理人具有有形的属性，并且通常认为行为系统中的物体可以以某种方式被操纵。软理论使用自然语言和可能不精确的叙述，而代理人是同时具有有形和无形属性的主体。硬复杂性理论的例子包括复杂适应系统(CAS)和生存理论，一类较为软的理论是生存系统理论。硬理论中提出的许多命题考虑也与较软理论相关。从现在开始，人们的兴趣将集中在中科院。
The study of CAS focuses on complex, emergent and macroscopic properties of the system.<ref name="CAS-T-12" /><ref name="CAS-T-11" /><ref name="CAS-T-13" /> [[John Henry Holland|John H. Holland]] said that CAS "are systems that have a large numbers of components, often called agents, that interact and adapt or learn".<ref>{{cite journal|year=2006|title=Studying Complex Adaptive Systems|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|hdl=2027.42/41486|url=https://deepblue.lib.umich.edu/bitstream/2027.42/41486/1/11424_2006_Article_1.pdf}}</ref>
The study of CAS focuses on complex, emergent and macroscopic properties of the system.
复杂适应系统的研究主要集中在系统的复杂性、突发性和宏观性上。
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;<ref name=AFC-NA-21/> the [[brain]] and the [[immune system]]; and the [[cell (biology)|cell]] and the developing [[embryo]]. Human social group-based endeavors, such as [[political party|political parties]], [[community|communities]], [[geopolitical]] [[organisations|organizations]], [[war]], and [[terrorist network analysis|terrorist networks]] are also considered CAS.<ref name=AFC-NA-21/><ref name=GT-33/><ref name=CAS-T-19>{{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}}</ref> The [[internet]] and [[cyberspace]]—composed, collaborated, and managed by a complex mix of [[human–computer interaction]]s, is also regarded as a complex adaptive system.<ref name=CAS-T-16/><ref name=CAS-T-17/><ref name=CAS-T-18/> CAS can be hierarchical, but more often exhibit aspects of "self-organization".<ref>{{Cite book|title=Hidden order: how adaptation builds complexity|last=Holland, John H. (John Henry)|date=1996|publisher=Addison-Wesley|isbn=0201442302|location=|pages=|oclc=970420200}}</ref>
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; The internet and cyberspace—composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system.
复杂适应系统的典型例子包括: 气候; 城市; 企业; 市场; 政府; 工业; 生态系统; 社会网络; 电网; 动物群落; 交通流量; 社会昆虫(例如:。蚁群: 互联网和网络空间ー由复杂的人机交互组成、协作和管理，也被视为复杂适应性系统。
=== General properties ===
=== General properties ===
一般性质
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 characterized by a high degree of [[adaptive capacity]], giving them resilience in the face of [[wikt:perturbation|perturbation]].
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 characterized by a high degree of adaptive capacity, giving them resilience in the face of perturbation.
Cas 与纯多智能体系统的区别在于，它关注的是顶级属性和特征，比如自相似性、复杂性、出现性和自我组织。多智能体系统是由多个相互作用的智能体组成的系统，而在 CAS 系统中，智能体和系统是自适应的，系统是自相似的。Cas 是一个复杂的、自相似的、相互作用的自适应代理的总体。复杂适应系统具有高度的适应能力，使拥有属性在面对干扰时具有弹性。
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]].
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.
其他重要的属性是适应(或内稳态) ，沟通，合作，专业化，时空组织和繁殖。它们可以在各个层面上被发现: 细胞专门化、适应和繁殖，就像大型生物一样。沟通和合作发生在各个层面，从代理到系统层面。在某些情况下，可以用博弈论分析这种系统中主体之间合作的驱动力。
=== Characteristics ===
=== Characteristics ===
特点
Some of the most important characteristics of complex systems are:<ref>[[Paul Cilliers]] (1998) ''Complexity and Postmodernism: Understanding Complex Systems''</ref>
Some of the most important characteristics of complex systems are:
复杂系统的一些最重要的特征是:
* The number of elements is sufficiently large that conventional descriptions (e.g. a system of [[differential equation]]s) 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''
* The overall behavior of the system of elements is not predicted by the behavior of the individual elements
* Such systems may be open and it may be difficult or impossible to define system boundaries
* Complex systems operate under [[Non-equilibrium thermodynamics|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 (academic)|Michael D. Cohen]]<ref>[[Robert Axelrod]] & [[Michael D. Cohen (academic)|Michael D. Cohen]], ''Harnessing Complexity''. [[Basic Books]], 2001</ref> identify a series of key terms from a modeling perspective:
Robert Axelrod & Michael D. Cohen identify a series of key terms from a modeling perspective:
从建模的角度确定了一系列关键迈克尔·科恩:
* '''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
Turner and Baker<ref>Turner, J. R., & Baker, R. (in press). Just doing the do: A case study testing creativity and innovative processes as complex adaptive systems. New Horizons in Adult Education and Human Resource Development.</ref> synthesized the characteristics of complex adaptive systems from the literature and tested these characteristics in the context of creativity and innovation. Each of these eight characteristics had been shown to be present in the creativity and innovative processes:
Turner and Baker synthesized the characteristics of complex adaptive systems from the literature and tested these characteristics in the context of creativity and innovation. Each of these eight characteristics had been shown to be present in the creativity and innovative processes:
特纳和贝克从文献中综合了复杂适应系统的特征，并在创造力和创新的背景下测试了这些特征。这八个特点中的每一个都显示出在创造性和创新过程中存在:
* '''Path dependent:''' Systems tend to be sensitive to their initial conditions. The same force might affect systems differently.<ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref>
* '''Systems have a history:''' The future behavior of a system depends on its initial starting point and subsequent history.<ref>{{cite journal | last1 = Boal | first1 = K. B. | last2 = Schultz | first2 = P. L. | year = 2007 | title = Storytelling, time, and evolution: The role of strategic leadership in complex adaptive systems | url = | journal = The Leadership Quarterly | volume = 18 | issue = 4| pages = 411–428 | doi = 10.1016/j.leaqua.2007.04.008 }}</ref>
* '''Non-linearity:''' React disproportionately to environmental perturbations. Outcomes differ from those of simple systems.<ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref> <ref>{{cite journal | last1 = Luoma | first1 = M | year = 2006 | title = A play of four arenas - How complexity can serve management development | url = | journal = Management Learning | volume = 37 | issue = | pages = 101–123 | doi = 10.1177/1350507606058136 }}</ref>
* '''Emergence:''' Each system's internal dynamics affect its ability to change in a manner that might be quite different from other systems.<ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref>
* '''Irreducible:''' Irreversible process transformations cannot be reduced back to its original state.<ref>{{cite journal | last1 = Borzillo | first1 = S. | last2 = Kaminska-Labbe | first2 = R. | year = 2011 | title = Unravelling the dynamics of knowledge creation in communities of practice through complexity theory lenses | url = | journal = Knowledge Management Research & Practice | volume = 9 | issue = | pages = 353–366 | doi = 10.1057/kmrp.2011.13 }}</ref>
* '''Adaptive/Adaptability:''' Systems that are simultaneously ordered and disordered are more adaptable and resilient.<ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref>
* '''Operates between order and chaos:''' Adaptive tension emerges from the energy differential between the system and its environment.<ref>{{cite journal | last1 = Borzillo | first1 = S. | last2 = Kaminska-Labbe | first2 = R. | year = 2011 | title = Unravelling the dynamics of knowledge creation in communities of practice through complexity theory lenses | url = | journal = Knowledge Management Research & Practice | volume = 9 | issue = | pages = 353–366 | doi = 10.1057/kmrp.2011.13 }}</ref>
* '''Self-organizing:''' Systems are composed of interdependency, interactions of its parts, and diversity in the system. <ref>{{cite journal | last1 = Lindberg | first1 = C. | last2 = Schneider | first2 = M. | year = 2013 | title = Combating infections at Maine Medical Center: Insights into complexity-informed leadership from positive deviance | url = | journal = Leadership | volume = 9 | issue = 2| pages = 229–253 | doi = 10.1177/1742715012468784 }}</ref>
== Modeling and simulation ==
== Modeling and simulation ==
建模与模拟
CAS are occasionally modeled by means of [[agent-based model]]s and [[complex network]]-based models.<ref>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]</ref> Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model.<ref>John H. Miller & Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press [http://press.princeton.edu/titles/8429.html Book page]</ref> Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components.<ref>Melanie Mitchell, Complexity A Guided Tour, Oxford University Press, [http://www.oup.com/us/catalog/general/subject/LifeSciences/~~/dmlldz11c2EmY2k9OTc4MDE5NTEyNDQxNQ== Book page]</ref>
CAS are occasionally modeled by means of agent-based models and complex network-based models. Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model. Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components.
复杂适应系统有时可以用基于代理的模型和基于复杂网络的模型来建模。基于主体的模型主要是通过识别模型中的不同主体，利用各种方法和工具开发的。开发复杂适应系统模型的另一种方法是利用复杂适应系统各组成部分的交互数据开发复杂的网络模型。
In 2013 [[SpringerOpen|SpringerOpen/BioMed Central]] has launched an online open-access journal on the topic of ''complex adaptive systems modeling'' (CASM).<ref>Springer ''[https://casmodeling.springeropen.com/ Complex Adaptive Systems Modeling Journal]'' (CASM)</ref>
In 2013 SpringerOpen/BioMed Central has launched an online open-access journal on the topic of complex adaptive systems modeling (CASM).
2013年，springeropen / biomed Central 推出了一个在线开放获取期刊，主题是复杂适应性系统建模。
== Evolution of complexity ==
== Evolution of complexity ==
复杂性的进化
[[File:Evolution of complexity.svg|thumb|300px|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.]]
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.
复杂性演变中的消极趋势与积极趋势。Cas 在过程的开始是红色的。系统数量的变化由条形图的高度来表示，每一组图在一个时间序列中向上移动。
{{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.<ref>{{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}}</ref> This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".<ref>{{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}}</ref>
Living organisms are complex adaptive systems. Although complexity is hard to quantify in biology, evolution has produced some remarkably complex organisms. This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".
生命有机体是复杂的适应系统。虽然复杂性在生物学中难以量化，但进化已经产生了一些非常复杂的生物体。这一观察结果导致了一种普遍的错误观念，认为进化是渐进的，并导致了被认为是“更高级的生物体”。
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.<ref name=Carroll>{{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 }}</ref> Indeed, some [[artificial life]] simulations have suggested that the generation of CAS is an inescapable feature of evolution.<ref>{{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 }}</ref><ref>{{cite journal |vauthors=Adami C, Ofria C, Collier TC |title=Evolution of biological complexity |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 }}</ref>
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. Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution.
如果这是普遍正确的，那么进化就会朝着复杂性的方向发展。如下所示，在这种类型的流程中，最常见的复杂性值会随着时间的推移而增加。事实上，一些人工生命模拟已经表明，CAS 的产生是进化过程中不可避免的特征。
However, the idea of a general trend towards complexity in evolution can also be explained through a passive process.<ref name=Carroll/> This involves an increase in [[variance]] but the most common value, the [[mode (statistics)|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]].
However, the idea of a general trend towards complexity in evolution can also be explained through a passive process. 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.
然而，进化的复杂性总体趋势的观点也可以通过一个被动的过程来解释。这涉及到方差的增加，但是最常见的值，模式，并没有改变。因此，最大程度的复杂性随着时间的推移而增加，但仅仅作为总体上有更多生物体的间接产物。这种类型的随机过程也称为有界随机游动。
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 [[Skewness|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 [[microorganism|microscopic]] [[prokaryote]]s,<ref>{{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}}</ref> which comprise about half the world's [[biomass]]<ref>{{cite journal |vauthors=Whitman W, Coleman D, Wiebe W | title = Prokaryotes: the unseen majority | 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 }}</ref> and constitute the vast majority of Earth's biodiversity.<ref>{{cite journal |vauthors=Schloss P, Handelsman J |title=Status of the microbial census |pmc=539005 |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}}</ref> Therefore, simple life remains dominant on Earth, and complex life appears more diverse only because of [[sampling bias]].
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, which comprise about half the world's biomass and constitute the vast majority of Earth's biodiversity. Therefore, simple life remains dominant on Earth, and complex life appears more diverse only because of sampling bias.
在这一假设中，向更复杂的生物体发展的明显趋势是一种错觉，因为它只注意居住在复杂性分布的右端的少数大型、非常复杂的生物体，而忽略了更简单和更普通的生物体。这个被动模型强调，绝大多数物种是微小的原核生物，它们构成了世界生物量的一半，构成了地球生物多样性的绝大多数。因此，简单生命在地球上仍然占主导地位，而复杂生命仅仅因为抽样偏差而显得更加多样化。
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.
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.
如果在生物学中缺乏一个复杂性的总体趋势，这并不排除在一个子集的情况下驱动系统走向复杂性的力量的存在。这些小的趋势将被其他的进化压力所平衡，这些进化压力驱使系统朝着不那么复杂的状态发展。
== See also ==
== See also ==
参见
{{Portal|Systems science}}
{{div col|colwidth=30em}}
* [[Artificial life]]
* [[Chaos theory]]
* [[Cognitive science]]
* [[Command and Control Research Program]]
* [[Complex system]]
* [[Computational economics]]
* [[Computational sociology]]
* [[Dual-phase evolution]]
* [[Enterprise systems engineering]]
* [[Generative sciences]]
* [[Open system (systems theory)]]
* [[Santa Fe Institute]]
* [[Simulated reality]]
* [[Sociology and complexity science]]
* [[Wicked problem|Super wicked problem]]
* [[Swarm Development Group]]
* [[Universal Darwinism]]
{{div col end}}
==References==
==References==
参考资料
{{Reflist|2|refs=
{{Reflist|2|refs=
{通货再膨胀 | 2 | 参考文献
<ref name=CAS-T-01>{{cite web |url=http://tejas.iimb.ac.in/articles/12.php|title=Insights from Complexity Theory: Understanding Organisations better |publisher= by Assoc. Prof. Amit Gupta, Student contributor - S. Anish, IIM Bangalore|date= |accessdate=1 June 2012 }}</ref>
<ref name=CAS-T-02>{{cite journal |title=Ten Principles of Complexity & Enabling Infrastructures |publisher= by Professor Eve Mitleton-Kelly, Director Complexity Research Programme, London School of Economics |date= |citeseerx = 10.1.1.98.3514}}</ref>
<ref name=CAS-T-11>{{cite journal|title=A Complex Adaptive Organization Under the Lens of the LIFE Model:The Case of Wikipedia|journal=Egosnet.org|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|last1=Faucher|first1=Jean-Baptiste}}</ref>
<ref name=CAS-T-12>{{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}}</ref>
<ref name=CAS-T-13>{{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|url-status=dead|archiveurl=https://web.archive.org/web/20120905170544/http://www.uow.edu.au/~kd21/uploads/Diment-complexity.pdf|archivedate=5 September 2012|df=dmy-all}}</ref>
<ref name=CAS-T-16>{{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}}</ref>
<ref name=CAS-T-17>{{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
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17{ cite web | title Cyberspace: The Ultimate 复杂适应性系统 | http://www.dodccrp.org/files/ic2j_v4n2_03_phister.pdf|publisher=The 国际 c 2 Journal
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<ref name=AFC-NA-21>[[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/|page = |date=|accessdate=11 June 2012}} "Unfolding the science behind the idea of six degrees of separation"</ref>
<ref name=GT-33>{{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}}</ref>
}}
}}
}}
== Literature ==
== Literature ==
文学
{{refbegin|2}}
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*{{cite book |author=Kelly, Kevin |title=Out of control: the new biology of machines, social systems and the economic world |publisher=Addison-Wesley |location=Boston |year=1994 |isbn=0-201-48340-8 |url=https://archive.org/details/outofcontrolnewb00kell |format=Full text available online |author-link=Kevin Kelly (editor) |url-access=registration }}
* {{Not a typo|Pharaoh}}, M.C. (online). [http://homepage.ntlworld.com/m.pharoah/ Looking to systems theory for a reductive explanation of phenomenal experience and evolutionary foundations for higher order thought] Retrieved 15 January 2008.
* Hobbs, George & Scheepers, Rens (2010),"Agility in Information Systems: Enabling Capabilities for the IT Function," ''Pacific Asia Journal of the Association for Information Systems'': Vol. 2: Iss. 4, Article 2. [http://aisel.aisnet.org/pajais/vol2/iss4/2 Link]
*{{cite book |author=Sidney Dekker|authorlink=Sidney Dekker|title=Drift into Failure: From Hunting Broken Components to Understanding Complex Systems|date=2011|publisher=CRC Press}}
{{refend}}
==External links==
==External links==
外部链接
{{commons category|Complex adaptive systems}}
*[http://www.cas-group.net/ Complex Adaptive Systems Group] loosely coupled group of scientists and software engineers interested in complex adaptive systems
*[http://www.dnawales.co.uk/ DNA Wales Research Group] Current Research in Organisational change CAS/CES related news and free research data. Also linked to the Business Doctor & BBC documentary series
*[http://pespmc1.vub.ac.be/CAS.html A description] of complex adaptive systems on the Principia Cybernetica Web.
* [http://bactra.org/notebooks/complexity.html Quick reference] single-page description of the 'world' of complexity and related ideas hosted by the Center for the Study of Complex Systems at the University of Michigan.
*[https://web.archive.org/web/20080723135438/http://www.complexsystems.net.au/ Complex systems research network]
* [https://web.archive.org/web/20090105183805/http://www.openabm.org/site/ The Open Agent-Based Modeling Consortium]
*[https://www.youtube.com/watch?v=jS0zj_dYeBE TEDxRotterdam - Igor Nikolic - Complex adaptive systems], and [https://www.youtube.com/watch?v=AB85AFzqtOY The emergence of universal consciousness: Brendan Hughes at TEDxPretoria ]. Talks discussing various practical examples of complex adaptive systems, including Wikipedia, star galaxies, genetic mutation, and other examples
{{Systems}}
{{DEFAULTSORT:Complex Adaptive System}}
[[Category:Complex systems theory]]
Category:Complex systems theory
范畴: 复杂系统理论
[[Category:Systems science]]
Category:Systems science
类别: 系统科学
[[Category:Organizational theory|*]]
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