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==历史==
 
==历史==
==History==
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==历史==
{{see also|Sociology and complexity science}}
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[[File:Complexity-map-with-sociolo.png|thumb|right|300px|Historical map of research paradigms and associated scientists in [[sociology and complexity science]].]]
 
[[File:Complexity-map-with-sociolo.png|thumb|right|300px|Historical map of research paradigms and associated scientists in [[sociology and complexity science]].]]
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{{main|Systems theory|Structural functionalism}}
 
{{main|Systems theory|Structural functionalism}}
 
In the post-war era, [[Vannevar Bush]]'s [[differential analyser]], [[John von Neumann]]'s [[Von Neumann cellular automata|cellular automata]], [[Norbert Wiener]]'s [[cybernetics]], and [[Claude Shannon]]'s [[information theory]] became influential paradigms for modeling and understanding complexity in technical systems. In response, scientists in disciplines such as physics, biology, electronics, and economics began to articulate a [[systems theory|general theory of systems]] in which all natural and physical phenomena are manifestations of interrelated elements in a system that has common patterns and properties. Following [[Émile Durkheim]]'s call to analyze complex modern society ''[[sui generis]]'',<ref>{{cite book|first=Émile |last=Durkheim |title=The Division of Labor in Society |location=New York, NY |publisher=Macmillan}}</ref> post-war structural functionalist sociologists such as [[Talcott Parsons]] seized upon these theories of systematic and hierarchical interaction among constituent components to attempt to generate grand unified sociological theories, such as the [[AGIL paradigm]].<ref name="Bailey">{{cite book|first1=Kenneth D. |last1=Bailey |editor=Jonathan H. Turner |chapter=Systems Theory |title=Handbook of Sociological Theory |publisher=Springer Science |year=2006 |location=New York, NY |isbn=978-0-387-32458-6 |pages=379–404}}</ref> Sociologists such as [[George Homans]] argued that sociological theories should be formalized into hierarchical structures of propositions and precise terminology from which other propositions and hypotheses could be derived and operationalized into empirical studies.<ref name="Blackwell">{{cite encyclopedia|year=2007 |title=Computational Sociology |last=Bainbridge |first=William Sims |encyclopedia=Blackwell Encyclopedia of Sociology |publisher=Blackwell Reference Online |url=http://www.sociologyencyclopedia.com/subscriber/tocnode?id=g9781405124331_chunk_g97814051243319_ss1-85 |doi=10.1111/b.9781405124331.2007.x |hdl=10138/224218 |editor=Ritzer, George|isbn=978-1-4051-2433-1|hdl-access=free }}</ref> Because computer algorithms and programs had been used as early as 1956 to test and validate mathematical theorems, such as the [[four color theorem]],<ref>{{cite book|last=Crevier |first=D. |year=1993 |title=AI: The Tumultuous History of the Search for Artificial Intelligence |url=https://archive.org/details/aitumultuoushist00crev |url-access=registration |publisher=Basic Books |location=New York, NY}}</ref> some scholars anticipated that similar computational approaches could "solve" and "prove" analogously formalized problems and theorems of social structures and dynamics.
 
In the post-war era, [[Vannevar Bush]]'s [[differential analyser]], [[John von Neumann]]'s [[Von Neumann cellular automata|cellular automata]], [[Norbert Wiener]]'s [[cybernetics]], and [[Claude Shannon]]'s [[information theory]] became influential paradigms for modeling and understanding complexity in technical systems. In response, scientists in disciplines such as physics, biology, electronics, and economics began to articulate a [[systems theory|general theory of systems]] in which all natural and physical phenomena are manifestations of interrelated elements in a system that has common patterns and properties. Following [[Émile Durkheim]]'s call to analyze complex modern society ''[[sui generis]]'',<ref>{{cite book|first=Émile |last=Durkheim |title=The Division of Labor in Society |location=New York, NY |publisher=Macmillan}}</ref> post-war structural functionalist sociologists such as [[Talcott Parsons]] seized upon these theories of systematic and hierarchical interaction among constituent components to attempt to generate grand unified sociological theories, such as the [[AGIL paradigm]].<ref name="Bailey">{{cite book|first1=Kenneth D. |last1=Bailey |editor=Jonathan H. Turner |chapter=Systems Theory |title=Handbook of Sociological Theory |publisher=Springer Science |year=2006 |location=New York, NY |isbn=978-0-387-32458-6 |pages=379–404}}</ref> Sociologists such as [[George Homans]] argued that sociological theories should be formalized into hierarchical structures of propositions and precise terminology from which other propositions and hypotheses could be derived and operationalized into empirical studies.<ref name="Blackwell">{{cite encyclopedia|year=2007 |title=Computational Sociology |last=Bainbridge |first=William Sims |encyclopedia=Blackwell Encyclopedia of Sociology |publisher=Blackwell Reference Online |url=http://www.sociologyencyclopedia.com/subscriber/tocnode?id=g9781405124331_chunk_g97814051243319_ss1-85 |doi=10.1111/b.9781405124331.2007.x |hdl=10138/224218 |editor=Ritzer, George|isbn=978-1-4051-2433-1|hdl-access=free }}</ref> Because computer algorithms and programs had been used as early as 1956 to test and validate mathematical theorems, such as the [[four color theorem]],<ref>{{cite book|last=Crevier |first=D. |year=1993 |title=AI: The Tumultuous History of the Search for Artificial Intelligence |url=https://archive.org/details/aitumultuoushist00crev |url-access=registration |publisher=Basic Books |location=New York, NY}}</ref> some scholars anticipated that similar computational approaches could "solve" and "prove" analogously formalized problems and theorems of social structures and dynamics.
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===系统论和功能主义===
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在战后时期,万尼瓦尔·布希的微分分析器、约翰·冯·诺伊曼的细胞自动机、诺伯特·维纳的模控学 与克劳德·夏农的信息论在技术系统中成为模拟与暸解复杂度具有影响力的典范。相对应地,在像是物理学、生物学、电子学,和经济学等学门的科学家开始表述一种一般性的系统理论,其中所有自然与物理现象皆为一个系统中具有相同模式与性质的相关元素的展现。  随着艾弥尔·涂尔干以实事求是的方式分析复杂现代社会的呼声<ref>{{cite book|first=Émile |last=Durkheim |title=The Division of Labor in Society |location=New York, NY |publisher=Macmillan}}</ref> ,战后结构功能主义社会学家如塔尔科特·帕森斯利用这些构成元素之间系统化与阶层化互动的理论,来尝试生成宏大而统一的社会学理论,例如四种功能(AGIL paradigm)<ref name="Bailey">{{cite book|first1=Kenneth D. |last1=Bailey |editor=Jonathan H. Turner |chapter=Systems Theory |title=Handbook of Sociological Theory |publisher=Springer Science |year=2006 |location=New York, NY |isbn=978-0-387-32458-6 |pages=379–404}}</ref>。  如George Homans等社会学家辩称社会理论应该被形式化(正规化)(formalized),成为命题和精确术语的阶层结构,其他的命题与假设可以从中被推演出来并操作化以进行实证研究。<ref name="Blackwell">{{cite encyclopedia|year=2007 |title=Computational Sociology |last=Bainbridge |first=William Sims |encyclopedia=Blackwell Encyclopedia of Sociology |publisher=Blackwell Reference Online |url=http://www.sociologyencyclopedia.com/subscriber/tocnode?id=g9781405124331_chunk_g97814051243319_ss1-85 |doi=10.1111/b.9781405124331.2007.x |hdl=10138/224218 |editor=Ritzer, George|isbn=978-1-4051-2433-1|hdl-access=free }}</ref> 由于电脑算法与程式早在1956年就已用来测试和验证数学定理,<ref>{{cite book|last=Crevier |first=D. |year=1993 |title=AI: The Tumultuous History of the Search for Artificial Intelligence |url=https://archive.org/details/aitumultuoushist00crev |url-access=registration |publisher=Basic Books |location=New York, NY}}</ref>例如四色定理,社会科学家与系统动力学家预期类似的计算取径可以类比地“解决”与“证明”正规化的问题,和社会结构与动力的理论。
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===Macrosimulation and microsimulation===
 
===Macrosimulation and microsimulation===
 
{{main|System dynamics|Microsimulation}}
 
{{main|System dynamics|Microsimulation}}
 
By the late 1960s and early 1970s, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. These models used differential equations to predict population distributions as holistic functions of other systematic factors such as inventory control, urban traffic, migration, and disease transmission.<ref>{{cite book|first=Jay |last=Forrester |year=1971 |title=World Dynamics |location=Cambridge, MA |publisher=MIT Press}}</ref><ref>{{cite journal|doi=10.1287/opre.26.2.237|title=Using Simulation to Develop and Validate Analytic Models: Some Case Studies |first1=Edward J. |last1=Ignall |first2=Peter |last2=Kolesar |first3=Warren E. |last3=Walker |journal=Operations Research |volume=26 |issue=2 |year=1978 |pages=237–253}}</ref> Although simulations of social systems received substantial attention in the mid-1970s after the [[Club of Rome]] published reports predicting that policies promoting exponential economic growth would eventually bring global environmental catastrophe,<ref>{{cite book|title=The Dynamics of Growth in a Finite World |last1=Meadows |first1=DL |last2=Behrens |first2=WW |last3=Meadows |first3=DH |last4=Naill |first4=RF |last5= Randers |first5=J |last6=Zahn |first6=EK |year=1974 |location=Cambridge, MA |publisher=MIT Press}}</ref> the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific.<ref name="SfSS1"/><ref>{{cite news|title=Computer View of Disaster Is Rebutted |newspaper=The New York Times |date=October 18, 1974|url=https://www.nytimes.com/1974/10/18/archives/computer-view-of-disaster-is-rebutted.html?_r=0}}</ref> Hoping to avoid the same fate, many social scientists turned their attention toward micro-simulation models to make forecasts and study policy effects by modeling aggregate changes in state of individual-level entities rather than the changes in distribution at the population level.<ref>{{cite journal|doi=10.1016/0167-2681(90)90038-F|title=From engineering to microsimulation : An autobiographical reflection |journal=Journal of Economic Behavior & Organization |year=1990 |volume=14 |issue=1 |pages=5–27 |first=Guy H. |last=Orcutt}}</ref> However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.<ref name="MW"/>
 
By the late 1960s and early 1970s, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. These models used differential equations to predict population distributions as holistic functions of other systematic factors such as inventory control, urban traffic, migration, and disease transmission.<ref>{{cite book|first=Jay |last=Forrester |year=1971 |title=World Dynamics |location=Cambridge, MA |publisher=MIT Press}}</ref><ref>{{cite journal|doi=10.1287/opre.26.2.237|title=Using Simulation to Develop and Validate Analytic Models: Some Case Studies |first1=Edward J. |last1=Ignall |first2=Peter |last2=Kolesar |first3=Warren E. |last3=Walker |journal=Operations Research |volume=26 |issue=2 |year=1978 |pages=237–253}}</ref> Although simulations of social systems received substantial attention in the mid-1970s after the [[Club of Rome]] published reports predicting that policies promoting exponential economic growth would eventually bring global environmental catastrophe,<ref>{{cite book|title=The Dynamics of Growth in a Finite World |last1=Meadows |first1=DL |last2=Behrens |first2=WW |last3=Meadows |first3=DH |last4=Naill |first4=RF |last5= Randers |first5=J |last6=Zahn |first6=EK |year=1974 |location=Cambridge, MA |publisher=MIT Press}}</ref> the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific.<ref name="SfSS1"/><ref>{{cite news|title=Computer View of Disaster Is Rebutted |newspaper=The New York Times |date=October 18, 1974|url=https://www.nytimes.com/1974/10/18/archives/computer-view-of-disaster-is-rebutted.html?_r=0}}</ref> Hoping to avoid the same fate, many social scientists turned their attention toward micro-simulation models to make forecasts and study policy effects by modeling aggregate changes in state of individual-level entities rather than the changes in distribution at the population level.<ref>{{cite journal|doi=10.1016/0167-2681(90)90038-F|title=From engineering to microsimulation : An autobiographical reflection |journal=Journal of Economic Behavior & Organization |year=1990 |volume=14 |issue=1 |pages=5–27 |first=Guy H. |last=Orcutt}}</ref> However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.<ref name="MW"/>
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===宏观模拟与微观模拟===
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到了1960年代晚期与1970年代早期,社会科学家使用更为可得的电脑科技对组织、产业、城市,与全球人口进行控制与回馈过程的宏观模拟。这些模型使用微分方程,将人口分布视为其他系统性因素(如存货控管、都市交通、迁徙、疾病传染等)的整体计算型函数(holistic functions)来进行预测。罗马俱乐部根据对于全球经济的模拟而出版了预测全球环境浩劫的报告。<ref>{{cite book|first=Jay |last=Forrester |year=1971 |title=World Dynamics |location=Cambridge, MA |publisher=MIT Press}}</ref><ref>{{cite journal|doi=10.1287/opre.26.2.237|title=Using Simulation to Develop and Validate Analytic Models: Some Case Studies |first1=Edward J. |last1=Ignall |first2=Peter |last2=Kolesar |first3=Warren E. |last3=Walker |journal=Operations Research |volume=26 |issue=2 |year=1978 |pages=237–253}}</ref> 
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尽管在这份报告发表后的1970年代中期,对社会体系的模拟因而得到了大量的关注,<ref>{{cite book|title=The Dynamics of Growth in a Finite World |last1=Meadows |first1=DL |last2=Behrens |first2=WW |last3=Meadows |first3=DH |last4=Naill |first4=RF |last5= Randers |first5=J |last6=Zahn |first6=EK |year=1974 |location=Cambridge, MA |publisher=MIT Press}}</ref> 
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然而模型的结果被认为对于模型的假设非常敏感(在罗马俱乐部的例子中,仅有少数的证据支持),亦暂时使得这初生的领域失去可信度。<ref name="SfSS1"/><ref>{{cite news|title=Computer View of Disaster Is Rebutted |newspaper=The New York Times |date=October 18, 1974|url=https://www.nytimes.com/1974/10/18/archives/computer-view-of-disaster-is-rebutted.html?_r=0}}</ref>
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对于利用计算工具来预测宏观的社会与经济行为产生的怀疑渐增,因此社会科学家将其注意力转向了微观模拟模型(microsimulation),借由模拟个人层级个体的状态渐进改变,而非人口层级的分布的改变,社会学家们作出了预测,也研究政策的效果<ref>{{cite journal|doi=10.1016/0167-2681(90)90038-F|title=From engineering to microsimulation : An autobiographical reflection |journal=Journal of Economic Behavior & Organization |year=1990 |volume=14 |issue=1 |pages=5–27 |first=Guy H. |last=Orcutt}}</ref>。
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然而,这些微观模拟模型并未允许个体进行互动或适应,其目的也非基本理论研究<ref name="MW"/>。
    
===Cellular automata and agent-based modeling===
 
===Cellular automata and agent-based modeling===
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This cellular automata paradigm gave rise to a third wave of social simulation emphasizing agent-based modeling. Like micro-simulations, these models emphasized bottom-up designs but adopted four key assumptions that diverged from microsimulation: autonomy, interdependency, simple rules, and adaptive behavior.<ref name="MW"/> Agent-based models are less concerned with predictive accuracy and instead emphasize theoretical development.<ref>{{cite journal |title=A simulation of the structure of academic science |journal=Sociological Research Online |volume=2 |issue=2 |pages=1–15 |year=1997 |first=Nigel |last=Gilbert |url=http://www.socresonline.org.uk/socresonline/2/2/3.html |doi=10.5153/sro.85 |access-date=2009-12-16 |archive-url=https://web.archive.org/web/19980524062306/http://www.socresonline.org.uk/socresonline/2/2/3.html |archive-date=1998-05-24 |url-status=dead }}</ref> In 1981, mathematician and political scientist [[Robert Axelrod]] and evolutionary biologist [[W.D. Hamilton]] published a major paper in ''[[Science (journal)|Science]]'' titled "The Evolution of Cooperation" which used an agent-based modeling approach to demonstrate how social cooperation based upon reciprocity can be established and stabilized in a [[prisoner's dilemma]] game when agents followed simple rules of self-interest.<ref>{{cite journal|title=The Evolution of Cooperation |first1=Robert |last1=Axelrod |first2=William D. |last2=Hamilton |journal=Science |volume=211 |issue=4489 |pages=1390–1396 |doi=10.1126/science.7466396|pmid=7466396 |date=March 27, 1981|bibcode=1981Sci...211.1390A }}</ref> Axelrod and Hamilton demonstrated that individual agents following a simple rule set of (1) cooperate on the first turn and (2) thereafter replicate the partner's previous action were able to develop "norms" of cooperation and sanctioning in the absence of canonical sociological constructs such as demographics, values, religion, and culture as preconditions or mediators of cooperation.<ref name="Cooperation"/> Throughout the 1990s, scholars like [[William Sims Bainbridge]], [[Kathleen Carley]], [[Michael Macy]],  and [[John Skvoretz]] developed multi-agent-based models of [[generalized reciprocity]], [[prejudice]], [[social influence]], and organizational [[information processing]]. In 1999, [[Nigel Gilbert]] published the first textbook on Social Simulation: ''Simulation for the social scientist'' and established its most relevant journal: the [[Journal of Artificial Societies and Social Simulation]].
 
This cellular automata paradigm gave rise to a third wave of social simulation emphasizing agent-based modeling. Like micro-simulations, these models emphasized bottom-up designs but adopted four key assumptions that diverged from microsimulation: autonomy, interdependency, simple rules, and adaptive behavior.<ref name="MW"/> Agent-based models are less concerned with predictive accuracy and instead emphasize theoretical development.<ref>{{cite journal |title=A simulation of the structure of academic science |journal=Sociological Research Online |volume=2 |issue=2 |pages=1–15 |year=1997 |first=Nigel |last=Gilbert |url=http://www.socresonline.org.uk/socresonline/2/2/3.html |doi=10.5153/sro.85 |access-date=2009-12-16 |archive-url=https://web.archive.org/web/19980524062306/http://www.socresonline.org.uk/socresonline/2/2/3.html |archive-date=1998-05-24 |url-status=dead }}</ref> In 1981, mathematician and political scientist [[Robert Axelrod]] and evolutionary biologist [[W.D. Hamilton]] published a major paper in ''[[Science (journal)|Science]]'' titled "The Evolution of Cooperation" which used an agent-based modeling approach to demonstrate how social cooperation based upon reciprocity can be established and stabilized in a [[prisoner's dilemma]] game when agents followed simple rules of self-interest.<ref>{{cite journal|title=The Evolution of Cooperation |first1=Robert |last1=Axelrod |first2=William D. |last2=Hamilton |journal=Science |volume=211 |issue=4489 |pages=1390–1396 |doi=10.1126/science.7466396|pmid=7466396 |date=March 27, 1981|bibcode=1981Sci...211.1390A }}</ref> Axelrod and Hamilton demonstrated that individual agents following a simple rule set of (1) cooperate on the first turn and (2) thereafter replicate the partner's previous action were able to develop "norms" of cooperation and sanctioning in the absence of canonical sociological constructs such as demographics, values, religion, and culture as preconditions or mediators of cooperation.<ref name="Cooperation"/> Throughout the 1990s, scholars like [[William Sims Bainbridge]], [[Kathleen Carley]], [[Michael Macy]],  and [[John Skvoretz]] developed multi-agent-based models of [[generalized reciprocity]], [[prejudice]], [[social influence]], and organizational [[information processing]]. In 1999, [[Nigel Gilbert]] published the first textbook on Social Simulation: ''Simulation for the social scientist'' and established its most relevant journal: the [[Journal of Artificial Societies and Social Simulation]].
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===元胞自动机与基于主体的建模===
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20世纪七十到八十年代,数学家和物理学家尝试建模和分析怎样从简单的单元,比如原子中,产生全局现象,比如复杂材料才低温、磁场、和湍流中的属性。<ref>{{cite book|title=Cellular automata machines: a new environment for modeling |url=https://archive.org/details/cellularautomata00toff |url-access=registration |first1=Tommaso |last1=Toffoli |first2=Norman |last2=Margolus | author2-link = Norman Margolus |year=1987 |publisher=MIT Press |location=Cambridge, MA}}</ref>
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使用元胞自动机(Celluer Automata),设定了一个只由方格组成的系统,每个方格就是一个“元胞(cell)”。每个元胞只能有有限个状态,期额元胞在各个状态间的转换条件只由紧贴着该元胞的周围元胞状态决定。元胞自动机与人工智能技术和微型计算机的所获得的进步一道为[[混沌理论]]和[[复杂系统]]等研究领域的建立做出重大贡献,同时也冲洗唤起了人们在理解交叉学科的复杂物理和社会系统的兴趣。众多致力于研究复杂科学的科研组织也是建立于这个时候:[[圣塔菲研究所]]由一群来自[https://www.lanl.gov/ 洛斯阿拉莫斯国家实验室]的物理学家在1984年发起,密歇根大学的[https://lsa.umich.edu/cscs/about-us/history.html BACH小组]也是在八十年代中期成立的。
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This cellular automata paradigm gave rise to a third wave of social simulation emphasizing agent-based modeling. Like micro-simulations, these models emphasized bottom-up designs but adopted four key assumptions that diverged from microsimulation: autonomy, interdependency, simple rules, and adaptive behavior.<ref name="MW"/> Agent-based models are less concerned with predictive accuracy and instead emphasize theoretical development.<ref>{{cite journal |title=A simulation of the structure of academic science |journal=Sociological Research Online |volume=2 |issue=2 |pages=1–15 |year=1997 |first=Nigel |last=Gilbert |url=http://www.socresonline.org.uk/socresonline/2/2/3.html |doi=10.5153/sro.85 |access-date=2009-12-16 |archive-url=https://web.archive.org/web/19980524062306/http://www.socresonline.org.uk/socresonline/2/2/3.html |archive-date=1998-05-24 |url-status=dead }}</ref> In 1981, mathematician and political scientist [[Robert Axelrod]] and evolutionary biologist [[W.D. Hamilton]] published a major paper in ''[[Science (journal)|Science]]'' titled "The Evolution of Cooperation" which used an agent-based modeling approach to demonstrate how social cooperation based upon reciprocity can be established and stabilized in a [[prisoner's dilemma]] game when agents followed simple rules of self-interest.<ref>{{cite journal|title=The Evolution of Cooperation |first1=Robert |last1=Axelrod |first2=William D. |last2=Hamilton |journal=Science |volume=211 |issue=4489 |pages=1390–1396 |doi=10.1126/science.7466396|pmid=7466396 |date=March 27, 1981|bibcode=1981Sci...211.1390A }}</ref> Axelrod and Hamilton demonstrated that individual agents following a simple rule set of (1) cooperate on the first turn and (2) thereafter replicate the partner's previous action were able to develop "norms" of cooperation and sanctioning in the absence of canonical sociological constructs such as demographics, values, religion, and culture as preconditions or mediators of cooperation.<ref name="Cooperation"/> Throughout the 1990s, scholars like [[William Sims Bainbridge]], [[Kathleen Carley]], [[Michael Macy]],  and [[John Skvoretz]] developed multi-agent-based models of [[generalized reciprocity]], [[prejudice]], [[social influence]], and organizational [[information processing]]. In 1999, [[Nigel Gilbert]] published the first textbook on Social Simulation: ''Simulation for the social scientist'' and established its most relevant journal: the [[Journal of Artificial Societies and Social Simulation]].
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这一轮元胞自动机的研究范式催生了使用基于主体建模(Agent-based Modeling)的第三次社会模拟浪潮。和宏观模拟类似,这些模型强调了自底向上的设计思想,但采用了四个不同于宏观建模的假设:自主(autonomy)、独立(interdependency)、简单规则(simple rules)、和适应性行为(adaptive behavior)。<ref name="MW"/> 相比于预测的准确度,基于主体的建模更加强调理论的建立。<ref>{{cite journal |title=A simulation of the structure of academic science |journal=Sociological Research Online |volume=2 |issue=2 |pages=1–15 |year=1997 |first=Nigel |last=Gilbert |url=http://www.socresonline.org.uk/socresonline/2/2/3.html |doi=10.5153/sro.85 |access-date=2009-12-16 |archive-url=https://web.archive.org/web/19980524062306/http://www.socresonline.org.uk/socresonline/2/2/3.html |archive-date=1998-05-24 |url-status=dead }}</ref>
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在1981年,数学家与政治学家[[罗伯特·阿克塞尔罗德]]与演化生物学家[https://zh.wikipedia.org/wiki/%E5%A8%81%E5%BB%89%C2%B7%E5%94%90%E7%B4%8D%C2%B7%E6%BC%A2%E5%BD%8C%E7%88%BE%E9%A0%93 威廉·汉密尔顿]一同在《Science》杂志上发表了一篇名为《合作的进化(The Evolution of Cooperation)》的经典论文,其中使用了基于主体的建模来展示了在囚徒困境的博弈中,当主体们(agents)只遵循简单的、自利的规则时,也可以在互惠的原则上建立稳定的社会合作。<ref>{{cite journal|title=The Evolution of Cooperation |first1=Robert |last1=Axelrod |first2=William D. |last2=Hamilton |journal=Science |volume=211 |issue=4489 |pages=1390–1396 |doi=10.1126/science.7466396|pmid=7466396 |date=March 27, 1981|bibcode=1981Sci...211.1390A }}</ref>
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阿克塞尔罗德
    
===Data mining and social network analysis===
 
===Data mining and social network analysis===
370

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