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→Cellular automata and agent-based modeling： 翻译1
截至20世纪60年代末70年代初，社会科学家越来越多地使用已有的计算技术，在组织、工业、城市和全球人口中进行控制和反馈过程的'''宏观模拟 Macrosimulation''' 。这些模型使用微分方程作为其他系统因素的整体函数来预测人口分布，这些系统因素包括库存控制、城市交通、人口迁移和疾病传播等。20世纪70年代中期，罗马俱乐部(Club of Rome)发表报告预测，促进指数式经济增长的政策最终将导致全球环境灾难，这些令人不舒服的结论导致许多研究者试图反驳这些模型，试图让研究人员自己显得不那么科学。为了避免同样的情况，许多社会科学家将注意力转向'''微观模拟 Microsimulation'''模型。这些模型通过模拟个体状态的总体变化而不是总体人口级别的变化来进行预测和研究政策的效果。然而，这些微观模拟模型并不允许个体相互作用或适应、变化（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 变化是根据适应（adapt）加上去的，不知道是否合理），研究者也不打算将它们用于基础理论研究。
截至20世纪60年代末70年代初，社会科学家越来越多地使用已有的计算技术，在组织、工业、城市和全球人口中进行控制和反馈过程的'''宏观模拟 Macrosimulation''' 。这些模型使用微分方程作为其他系统因素的整体函数来预测人口分布，这些系统因素包括库存控制、城市交通、人口迁移和疾病传播等。20世纪70年代中期，罗马俱乐部(Club of Rome)发表报告预测，促进指数式经济增长的政策最终将导致全球环境灾难，这些令人不舒服的结论导致许多研究者试图反驳这些模型，试图让研究人员自己显得不那么科学。为了避免同样的情况，许多社会科学家将注意力转向'''微观模拟 Microsimulation'''模型。这些模型通过模拟个体状态的总体变化而不是总体人口级别的变化来进行预测和研究政策的效果。然而，这些微观模拟模型并不允许个体相互作用或适应、变化（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 变化是根据适应（adapt）加上去的，不知道是否合理），研究者也不打算将它们用于基础理论研究。
===Cellular automata and agent-based modeling===
===Cellular automata and agent-based modeling 元胞自动机和基于主体建模===
{{main|Cellular automata|agent-based modeling}}
{{main|Cellular automata|agent-based modeling}}
The 1970s and 1980s were also a time when physicists and mathematicians were attempting to model and analyze how simple component units, such as atoms, give rise to global properties, such as complex material properties at low temperatures, in magnetic materials, and within turbulent flows. Using cellular automata, scientists were able to specify systems consisting of a grid of cells in which each cell only occupied some finite states and changes between states were solely governed by the states of immediate neighbors. Along with advances in artificial intelligence and microcomputer power, these methods contributed to the development of "chaos theory" and "complexity theory" which, in turn, renewed interest in understanding complex physical and social systems across disciplinary boundaries. Research organizations explicitly dedicated to the interdisciplinary study of complexity were also founded in this era: the Santa Fe Institute was established in 1984 by scientists based at Los Alamos National Laboratory and the BACH group at the University of Michigan likewise started in the mid-1980s.
The 1970s and 1980s were also a time when physicists and mathematicians were attempting to model and analyze how simple component units, such as atoms, give rise to global properties, such as complex material properties at low temperatures, in magnetic materials, and within turbulent flows. Using cellular automata, scientists were able to specify systems consisting of a grid of cells in which each cell only occupied some finite states and changes between states were solely governed by the states of immediate neighbors. Along with advances in artificial intelligence and microcomputer power, these methods contributed to the development of "chaos theory" and "complexity theory" which, in turn, renewed interest in understanding complex physical and social systems across disciplinary boundaries. Research organizations explicitly dedicated to the interdisciplinary study of complexity were also founded in this era: the Santa Fe Institute was established in 1984 by scientists based at Los Alamos National Laboratory and the BACH group at the University of Michigan likewise started in the mid-1980s.
20世纪70年代和80年代，物理学家和数学家也在试图模拟和分析简单的组成单位，如原子，如何引起整体特性，比如在低温下，在磁性材料和湍流中的复杂材料特性。使用'''元胞自动机 Cellular Automata'''，科学家们能够指定由元胞网格组成的系统，其中每个元胞只占据一些有限的状态，状态之间的变化完全由相邻元胞的状态控制。随着人工智能和微型计算机能力的进步，这些方法促进了“混沌理论”和“复杂性理论”的发展，这反过来又重新引起了人们对跨学科的复杂物理和社会系统的兴趣。明确致力于跨学科复杂性研究的机构也是在这个时代成立的: 圣菲研究所是由美国洛斯阿拉莫斯国家实验室（Los Alamos National Laboratory）的科学家于1984年建立的，密歇根大学的 BACH 小组也是在20世纪80年代中期建立的。
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. In 1981, mathematician and political scientist Robert Axelrod and evolutionary biologist W.D. Hamilton published a major paper in 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. 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. 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. In 1981, mathematician and political scientist Robert Axelrod and evolutionary biologist W.D. Hamilton published a major paper in 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. 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. 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.
元胞自动机范式引发了强调'''基于主体建模 Agent-based Modeling'''的第三次社会模拟浪潮。与微观模拟一样，这些模型强调自下而上的设计，但采用了与微观模拟不同的四个关键假设: '''自主性 Autonomy'''、'''相互依赖性 Interdependency'''、'''简单规则 Simple Rules'''和'''适应性行为 Adaptive Behavior'''。1981年，数学家、政治学家阿克塞尔罗德（Robert Axelrod）和进化生物学家汉密尔顿（W.D. Hamilton）在《科学》杂志上发表了一篇名为《合作的进化》（The Evolution of Cooperation）的重要论文，该论文采用基于主体建模方法，论证了在一个囚徒困境博弈中，当主体遵循简单的自利规则时，互惠的社会合作是如何建立和稳定的。阿克塞尔罗德和汉密尔顿证明，主体遵循这样一套简单的规则: (1)在第一轮进行合作，(2)在其后重复伙伴以前的行动，能够在没有人口学差异、价值观、宗教和文化等社会规范作为合作的先决条件或中介的情况下制定合作和制裁的“规范”。整个20世纪90年代，像威廉·希姆斯·本布里奇（William Sims Bainbridge），Kathleen Carley，Michael Macy 和 John Skvoretz 这样的学者开发了基于多主体的广义互惠、偏见、社会影响和组织信息处理模型。1999年，吉尔伯特（Nigel Gilbert）出版了第一本关于社会模拟: 写给社会科学家的仿真模拟（Simulation for the social scientist）的教科书，并建立了它最相关的杂志: 人工社会和社会模拟杂志（the Journal of Artificial Societies and Social Simulation）。
===Data mining and social network analysis===
===Data mining and social network analysis===