复杂适应系统 Complex adaptive system

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Yillia Jing（讨论 | 贡献）2020年7月14日 (二) 11:22的版本
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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] In complex adaptive systems, the whole is more complex than its parts,[2] and more complicated and meaningful than the aggregate of its parts. The study of complex adaptive systems, a subset of nonlinear dynamical systems,[3] 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.[4]

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(complex adaptive system)是一种系统，在这种系统中，对系统部分行为的理解并不能完美解释系统的整体行为[1]。在复杂适应系统中，系统的整体比其单独某个组成部分[5]或部分的加和都更加复杂、更有意义。复杂适应系统是非线性动力系统的子集[3]，其研究是高度跨学科的，融合了自然科学和社会科学的知识开发出了系统级的模型和见解，从而使得系统可以实现'''异构代理（heterogeneous agents）'''、'''相变（phase transition）'''和'''突现行为（emergent behavior）'''[6] 。


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.[7][8][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.[7][8][9] The Complex Adaptive Systems approach builds on replicator dynamics.[10]

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.

这种系统是复杂系统/复杂的，因为它们是动态的交互网络，并不是单个静态实体的简单聚合，也就是说，集合的行为不能通过已知每个组件的行为来进行预测。同时它们也是适应性的，因为个体和集体的行为会随着微事件或事件集合的发生而进行变异或自我组织。


Overview

The term complex adaptive systems, or complexity science, is often used to describe the loosely organized academic field that has grown up around the study of such systems. Complexity science is not a single theory—it encompasses more than one theoretical framework and is highly interdisciplinary, seeking the answers to some fundamental questions about living, adaptable, changeable systems. Complex adaptive systems may adopt hard or softer approaches [11]. 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.

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。硬复杂性理论包括复杂适应系统理论和生存理论，其中一类较为软性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软理论有关。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.[9][12][13] John H. Holland said that CAS "are systems that have a large numbers of components, often called agents, that interact and adapt or learn".[14]

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;[15] 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.[15][16][17] 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".[21]

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

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.

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，它关注于顶级属性和特征，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集体。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

Some of the most important characteristics of complex systems are:[22]

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 equations) are not only impractical, but cease to assist in understanding the system. Moreover, the elements interact dynamically, and the interactions can be physical or involve the exchange of information
  • Such interactions are rich, i.e. any element or sub-system in the system is affected by and affects several other elements or sub-systems
  • The interactions are non-linear: small changes in inputs, physical interactions or stimuli can cause large effects or very significant changes in outputs
  • Interactions are primarily but not exclusively with immediate neighbours and the nature of the influence is modulated
  • Any interaction can feed back onto itself directly or after a number of intervening stages. Such feedback can vary in quality. This is known as recurrency
  • 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 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[23] 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:

从建模的角度来看，Robert Axelrod 和迈克尔·科恩确定了一系列关键术语:

  • 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[24] 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.[25]
  • Systems have a history: The future behavior of a system depends on its initial starting point and subsequent history.[26]
  • Non-linearity: React disproportionately to environmental perturbations. Outcomes differ from those of simple systems.[27] [28]
  • Emergence: Each system's internal dynamics affect its ability to change in a manner that might be quite different from other systems.[29]
  • Irreducible: Irreversible process transformations cannot be reduced back to its original state.[30]
  • Adaptive/Adaptability: Systems that are simultaneously ordered and disordered are more adaptable and resilient.[31]
  • Operates between order and chaos: Adaptive tension emerges from the energy differential between the system and its environment.[32]
  • Self-organizing: Systems are composed of interdependency, interactions of its parts, and diversity in the system. [33]


Modeling and simulation

CAS are occasionally modeled by means of agent-based models and complex network-based models.[34] Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model.[35] Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components.[36]

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/BioMed Central has launched an online open-access journal on the topic of complex adaptive systems modeling (CASM).[37]

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

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。在进程的开始时 CAS 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 article: 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.[38] This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".[39]

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，即进化是渐进的，并导致被视为“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.[40] Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution.[41][42]

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.[40] 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.

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，复杂性在进化中的普遍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，复杂性的最大程度随着时间的推移而增加，但仅仅是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 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,[43] which comprise about half the world's biomass[44] and constitute the vast majority of Earth's biodiversity.[45] 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

模板:Portal


References

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Literature

  • Ahmed E, Elgazzar AS, Hegazi AS (28 June 2005). "An overview of complex adaptive systems". Mansoura J. Math. 32: 6059. arXiv:nlin/0506059. Bibcode:2005nlin......6059A. arXiv:nlin/0506059v1 [nlin.AO].
  • Dooley, K., Complexity in Social Science glossary a research training project of the European Commission.
  • Edwin E. Olson; Glenda H. Eoyang (2001). Facilitating Organization Change. San Francisco: Jossey-Bass. ISBN 0-7879-5330-X. 
  • Gell-Mann, Murray (1994). The quark and the jaguar: adventures in the simple and the complex. San Francisco: W.H. Freeman. ISBN 0-7167-2581-9. 
  • Holland, John H. (1999). Emergence: from chaos to order. Reading, Mass: Perseus Books. ISBN 0-7382-0142-1. 
  • Solvit, Samuel (2012). Dimensions of War: Understanding War as a Complex Adaptive System. Paris, France: L'Harmattan. ISBN 978-2-296-99721-9. 
  • 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. Link
  • Sidney Dekker (2011). Drift into Failure: From Hunting Broken Components to Understanding Complex Systems. CRC Press. 



External links

模板:Commons category

  • 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
  • A description of complex adaptive systems on the Principia Cybernetica Web.
  • 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.


模板:Systems

Category:Complex systems theory

范畴: 复杂系统理论

Category:Systems science

类别: 系统科学


This page was moved from wikipedia:en:Complex adaptive system. Its edit history can be viewed at 复杂适应系统/edithistory

  1. ↑ "Complex Adaptive Systems" (PDF). mit.edu. 2001. Retrieved 25 August 2012. by Serena Chan, Research Seminar in Engineering Systems
  2. ↑ Steven Strogatz, Duncan J. Watts and Albert-László Barabási "explaining synchronicity (at 6:08), network theory, self-adaptation mechanism of complex systems, Six Degrees of separation, Small world phenomenon, events are never isolated as they depend upon each other (at 27:07) in the BBC / Discovery Documentary". BBC / Discovery. Retrieved 11 June 2012. "Unfolding the science behind the idea of six degrees of separation"
  3. ↑ "Toward a Complex Adaptive Intelligence Community The Wiki and the Blog". D. Calvin Andrus. cia.gov. Retrieved 25 August 2012.
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