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Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.

Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.

'''计算社会学 Computational Sociology''' 是社会学的一个分支，它使用计算密集型方法来分析和模拟社会现象。<font color='red'>计算社会学通过使用计算机模拟、人工智能、复杂统计方法以及社会网络分析等分析方法自下而上地建立社会互动模型，是建立并检验复杂社会过程的理论。</font> <font color='blue'>  借助计算机模拟、人工智能、复杂统计方法以及社会网络分析等分析方法，计算社会学通过自下而上地建立社会互动模型来发展和检验关于复杂社会过程的理论。</font>

'''计算社会学 Computational Sociology''' 是社会学的一个分支，它使用计算密集型方法来分析和模拟社会现象。借助计算机模拟、人工智能、复杂统计方法以及社会网络分析等分析方法，计算社会学通过自下而上地建立社会互动模型来发展和检验关于复杂社会过程的理论。

It involves the understanding of social agents, the interaction among these agents, and the effect of these interactions on the social aggregate. Although the subject matter and methodologies in social science differ from those in natural science or computer science, several of the approaches used in contemporary social simulation originated from fields such as physics and artificial intelligence. Some of the approaches that originated in this field have been imported into the natural sciences, such as measures of network centrality from the fields of social network analysis and network science.

It involves the understanding of social agents, the interaction among these agents, and the effect of these interactions on the social aggregate. Although the subject matter and methodologies in social science differ from those in natural science or computer science, several of the approaches used in contemporary social simulation originated from fields such as physics and artificial intelligence. Some of the approaches that originated in this field have been imported into the natural sciences, such as measures of network centrality from the fields of social network analysis and network science.

==History 历史==

==历史==

{{see also|Sociology and complexity science}}

{{see also|Sociology and complexity science}}

=== Background 背景===

===背景===

While emergence has had a valuable and important role with the foundation of Computational Sociology, there are those who do not necessarily agree.  One major leader in the field, Epstein, doubted the use because there were aspects that are unexplainable.  Epstein put up a claim against emergentism, in which he says it "is precisely the generative sufficiency of the parts that constitutes the whole's explanation".

While emergence has had a valuable and important role with the foundation of Computational Sociology, there are those who do not necessarily agree.  One major leader in the field, Epstein, doubted the use because there were aspects that are unexplainable.  Epstein put up a claim against emergentism, in which he says it "is precisely the generative sufficiency of the parts that constitutes the whole's explanation".

虽然涌现在计算社会学的建立上发挥了宝贵而重要的作用，但也有人不太同意。该领域的一个主要人物爱泼斯坦 Epstein对这种<font color='red'>用法</font> <font color='blue'>概念</font>持怀疑态度，因为有些方面是这个<font color='red'>用法</font> <font color='blue'>概念</font>无法解释的。爱泼斯坦提出了一种反对“涌现主义”的观点，他表示:“<font color=='red'>正是这些部分的有生成意义的充分性构成了对整体的解释</font><font color='blue'>  正是这些部分所产生的充分性构成了对整体的解释</font>”。

虽然涌现在计算社会学的建立上发挥了宝贵而重要的作用，但也有人不太同意。该领域的一个主要人物爱泼斯坦 Epstein对这种概念持怀疑态度，因为有些方面是这个概念无法解释的。爱泼斯坦提出了一种反对“涌现主义”的观点，他表示:“正是这些部分所产生的充分性构成了对整体的解释”。

Agent-based models have had a historical influence on Computational Sociology. These models first came around in the 1960s, and were used to simulate control and feedback processes in organizations, cities, etc.  During the 1970s, the application introduced the use of individuals as the main units for the analyses and used bottom-up strategies for modeling behaviors.  The last wave occurred in the 1980s.  At this time, the models were still bottom-up; the only difference is that the agents interact interdependently.

Agent-based models have had a historical influence on Computational Sociology. These models first came around in the 1960s, and were used to simulate control and feedback processes in organizations, cities, etc.  During the 1970s, the application introduced the use of individuals as the main units for the analyses and used bottom-up strategies for modeling behaviors.  The last wave occurred in the 1980s.  At this time, the models were still bottom-up; the only difference is that the agents interact interdependently.

'''基于主体的模型 Agent-based Models'''对计算社会学有着历史性的影响。这些模型最早出现在20世纪60年代，用于仿真模拟组织、城市等系统的控制和反馈过程。在20世纪70年代，<font color='red'>应用程序</font><font color='blue'> ABM模型</font>引入个体作为主要的分析单元，并使用自下而上的策略来对行为建模。最后一波浪潮发生在20世纪80年代。那时，模型仍然是自下而上的; 唯一的区别是主体之间具有互相依赖和相互作用。

'''基于主体的模型 Agent-based Models'''对计算社会学有着历史性的影响。这些模型最早出现在20世纪60年代，用于仿真模拟组织、城市等系统的控制和反馈过程。在20世纪70年代，ABM模型引入个体作为主要的分析单元，并使用自下而上的策略来对行为建模。最后一波浪潮发生在20世纪80年代。那时，模型仍然是自下而上的; 唯一的区别是主体之间具有互相依赖和相互作用。

===Systems theory and structural functionalism 系统理论和结构功能主义===

===系统理论和结构功能主义===

{{main|Systems theory|Structural functionalism}}

{{main|Systems theory|Structural functionalism}}

In the post-war era, Vannevar Bush's differential analyser, John von Neumann's 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 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, 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. 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. Because computer algorithms and programs had been used as early as 1956 to test and validate mathematical theorems, such as the four color theorem, 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 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 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, 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. 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. Because computer algorithms and programs had been used as early as 1956 to test and validate mathematical theorems, such as the four color theorem, some scholars anticipated that similar computational approaches could "solve" and "prove" analogously formalized problems and theorems of social structures and dynamics.

战后时期，范内瓦·布什（Vannevar Bush）的'''微分分析机 Differential Analyser''' 、冯·诺伊曼（John von Neumann）的'''元胞自动机 Cellular Automata'''、 维纳（Norbert Wiener）的'''控制论 Cybernetics'''和克劳德·香农（Claude Shannon）的'''信息论 Information Theory'''成为技术系统中建模和理解复杂性的重要范式。物理学、生物学、电子学和经济学等学科的科学家开始阐述系统的一般理论，即所有自然和物理现象都是具有共同模式和属性的系统中相互关联的元素的表现。<font color='red'>按照 </font><font color='blue'> 沿着</font>涂尔干（Émile Durkheim）分析特定的复杂现代社会

战后时期，范内瓦·布什（Vannevar Bush）的'''微分分析机 Differential Analyser''' 、'''冯·诺伊曼 John von Neumann'''的'''元胞自动机 Cellular Automata'''、 '''维纳 Norbert Wiener'''的'''控制论 Cybernetics'''和'''克劳德·香农 Claude Shannon'''的'''信息论 Information Theory'''成为技术系统中建模和理解复杂性的重要范式。物理学、生物学、电子学和经济学等学科的科学家开始阐述系统的一般理论，即所有自然和物理现象都是具有共同模式和属性的系统中相互关联的元素的表现。沿着'''涂尔干 Émile Durkheim'''分析特定的复杂现代社会的思路，战后结构功能主义社会学家如'''帕森斯 Talcott Parsons'''利用这些组成部分之间系统化和层次化相互作用的理论，试图产生大统一的社会学理论，如 AGIL 范式。'''霍曼斯 George Homans'''等社会学家认为，社会学理论应该被构建为具有逻辑命题和精确术语的层次化结构，并且从中可以得出能够在实证研究中被操作化的其他命题和假设。由于早在1956年计算机算法和程序就已经被用来测试和验证数学定理（如'''四色定理Four Color Theorem'''），一些学者预计相似的计算方法可以“解释”和“证明”关于社会结构和演变的类似形式化的问题和定理。

===宏观模拟和微观模拟===

 

的<font color='red'>要求</font><font color='blue'> 思路</font>，战后结构功能主义社会学家如帕森斯（Talcott Parsons）利用这些组成部分之间系统<font color='blue'>化</font>和<font color='red'>等级</font><font color='blue'> 层次化</font>相互作用的理论，试图产生大统一的社会学理论，如 AGIL 范式。霍曼斯（George Homans）等社会学家认为，社会学理论应该被构建为具有<font color='blue'>逻辑</font>命题和精确术语的<font color='red'>等级</font><font color='blue'> 层次化</font>结构，并且从中可以得出能够在实证研究中被操作化的其他命题和假设。由于早在1956年计算机算法和程序就已经被用来测试和验证数学定理（如'''四色定理Four Color Theorem'''），<font color='red'>一些学者预计相似的计算方法可以“解决”和“证明”类似的社会结构和动态的问题和定理。</font><font color='blue'> 一些学者预计相似的计算方法可以“解释”和“证明”关于社会结构和演变的类似形式化的问题和定理。</font>

 

===Macrosimulation and microsimulation 宏观模拟和微观模拟===

{{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. 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, the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific. 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. However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.

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. 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, the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific. 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. However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.

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，<s>在</s><font color='blue'>对</font>组织、工业、城市和全球人口<s>中</s>进行<font color='blue'>包含</font>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<font color='red'>库存</font><font color='blue'> 财产</font>控制、城市交通、人口迁移和疾病传播等。<font color='red'>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，这个悲观的结论导致许多研究者试图反驳这些模型，并试图让研究显得不那么科学。</font>

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，对组织、工业、城市和全球人口进行包含控制和反馈过程的'''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，这些系统因素包括财产控制、城市交通、人口迁移和疾病传播等。20世纪70年代中期，尽管对社会系统的仿真获得了巨大的关注，但在'''罗马俱乐部 Club of Rome''' 发布预测报告称促进指数式经济增长的政策最终将导致全球环境灾难，这个悲观的结论导致许多研究者尝试抹黑这些(仿真)模型，并试图让(这些模型的)研究者自身显得不那么科学。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分布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，研究者也不打算将它们用于基础理论研究。

<font color='blue'> 20世纪70年代中期，尽管对社会系统的仿真获得了巨大的关注，但在'''罗马俱乐部 Club of Rome''' 发布预测报告称促进指数式经济增长的政策最终将导致全球环境灾难，这个悲观的结论导致许多研究者尝试抹黑这些(仿真)模型，并试图让(这些模型的)研究者自身显得不那么科学。</font>

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<font color='blue'>分布</font>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--[[用户:木子二月鸟|木子二月鸟]]（[[用户讨论:木子二月鸟|讨论]]）我觉得ok），研究者也不打算将它们用于基础理论研究。

===Cellular automata and agent-based modeling 元胞自动机和基于主体建模===

===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何产生整体特性，比如低温环境中原子在磁性材料和湍流中<font color='blue'>(表现出的)</font>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。

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何产生整体特性，比如低温环境中原子在磁性材料和湍流中(表现出的)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中，当主体遵循简单的自利规则（rules of self-interest）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<s>基于多主体的</s><font color='blue'>关于</font>广义互惠、偏见、社会影响和组织信息处理模型<font color='blue'>的多主体模型</font>。1999年，吉尔伯特（Nigel Gilbert）出版了第一本关于社会模拟<font color='blue'>的教科书</font>: 《写给社会科学家的仿真模拟（Simulation for the social scientist）》<s>的教科书</s>，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）。

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中，当主体遵循简单的自利规则（rules of self-interest）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 这样的学者开发了关于广义互惠、偏见、社会影响和组织信息处理模型的多主体模型。1999年，吉尔伯特（Nigel Gilbert）出版了第一本关于社会模拟的教科书: 《'''写给社会科学家的仿真模拟 Simulation for the social scientist'''》，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 数据挖掘和社会网络分析===

===数据挖掘和社会网络分析===

{{main|Data mining|Social network analysis}}

{{main|Data mining|Social network analysis}}

 

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman.[22] The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments.[23] Continued improvements in machine learning algorithms likewise have permitted social scientists and entrepreneurs to use novel techniques to identify latent and meaningful patterns of social interaction and evolution in large electronic datasets.[24][25]

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman.[22] The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments.[23] Continued improvements in machine learning algorithms likewise have permitted social scientists and entrepreneurs to use novel techniques to identify latent and meaningful patterns of social interaction and evolution in large electronic datasets.[24][25]

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman. The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments. Continued improvements in machine learning algorithms likewise have permitted social scientists and entrepreneurs to use novel techniques to identify latent and meaningful patterns of social interaction and evolution in large electronic datasets.

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman. The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments. Continued improvements in machine learning algorithms likewise have permitted social scientists and entrepreneurs to use novel techniques to identify latent and meaningful patterns of social interaction and evolution in large electronic datasets.

'''社会网络分析 Social Network Analysis'''独立于社会系统计算模型的发展，在20世纪70年代和80年代出现于图论、统计学和社会结构的研究中，它作为一种独特的分析方法被社会学家如 James s. Coleman，Harrison White，Linton Freeman，J. Clyde Mitchell，Mark Granovetter，Ronald Burt 和 Barry Wellman 等阐述和采用。在整个1980年代和1990年代，计算和通信技术日益普及，这要求采用诸如网络分析和多级建模等分析技术，这些技术可以扩展到日益复杂和庞大的数据集中。最近的计算社会学没有使用模拟，而是使用网络分析和先进的统计技术来分析大规模<font color='blue'>电子服务器构成的</font>计算机数据库中<s>电子代理</s>的行为数据。电子记录，如电子邮件和即时消息记录，万维网上的超链接，移动电话数据，以及 Usenet 上的讨论，使社会科学家能够直接观察社会行为并在多个时间点和多个层次的分析行为，并且不受传统的实证方法，如访谈、观察（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]）为了通顺，删除研究对象/被试：participants）或调查工具的限制。机器学习算法的不断改进同样使得社会科学家和企业家能够使用新技术来识别大型电子数据集中潜在但有意义的社会互动和演化模式。

'''社会网络分析 Social Network Analysis'''独立于社会系统计算模型的发展，在20世纪70年代和80年代出现于图论、统计学和社会结构的研究中，它作为一种独特的分析方法被社会学家如 James s. Coleman，Harrison White，Linton Freeman，J. Clyde Mitchell，Mark Granovetter，Ronald Burt 和 Barry Wellman 等阐述和采用。在整个1980年代和1990年代，计算和通信技术日益普及，这要求采用诸如网络分析和多级建模等分析技术，这些技术可以扩展到日益复杂和庞大的数据集中。最近的计算社会学没有使用模拟，而是使用网络分析和先进的统计技术来分析大规模电子服务器构成的计算机数据库中的行为数据。电子记录，如电子邮件和即时消息记录，万维网上的超链接，移动电话数据，以及 Usenet 上的讨论，使社会科学家能够直接观察社会行为并在多个时间点和多个层次的分析行为，并且不受传统的实证方法，如访谈、观察或调查工具的限制。机器学习算法的不断改进同样使得社会科学家和企业家能够使用新技术来识别大型电子数据集中潜在但有意义的社会互动和演化模式。

文本语料库的自动解析使对参与者及其关系网络的大规模提取成为可能，它将文本数据转换为网络数据。由此产生的网络可以包含数千个'''节点 Nodes'''，然后利用网络理论中的工具对其进行分析，以确定关键参与者、关键群体，以及网络的总体特性如稳健性、结构稳定性，或某些节点的'''中心性 Centrality'''等。这使'''定量叙事分析Quantitative Narrative Analysis'''引入的方法得以自动化，据此，主语-动词-宾语三元组被看作由动作连接的成对行为者，或者由行为者-宾语形成的成对行为者。

文本语料库的自动解析使对参与者及其关系网络的大规模提取成为可能，它将文本数据转换为网络数据。由此产生的网络可以包含数千个'''节点 Nodes'''，然后利用网络理论中的工具对其进行分析，以确定关键参与者、关键群体，以及网络的总体特性如稳健性、结构稳定性，或某些节点的'''中心性 Centrality'''等。这使'''定量叙事分析Quantitative Narrative Analysis'''引入的方法得以自动化，据此，主语-动词-宾语三元组被看作由动作连接的成对行为者，或者由行为者-宾语形成的成对行为者。

===Computational content analysis 计算<font color='red'><s>机</s></font>内容分析 （名词翻译可吗？）===

===计算内容分析 ===

[[Content analysis]] has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "[[big data]]" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. [[Gender bias]], [[readability]], content similarity, reader preferences, and even mood have been analyzed based on [[text mining]] methods over millions of documents.<ref>{{cite journal|author1=I. Flaounas|author2=M. Turchi|author3=O. Ali|author4=N. Fyson|author5=T. De Bie|author6=N. Mosdell|author7=J. Lewis|author8=N. Cristianini|title=The Structure of EU Mediasphere|journal=PLOS One|volume=5|issue=12|pages=e14243|year=2010|doi=10.1371/journal.pone.0014243|url=https://orca-mwe.cf.ac.uk/50732/1/Flaounas%202010.pdf|pmid=21170383|pmc=2999531|bibcode=2010PLoSO...514243F}}</ref><ref>{{cite journal|title=Nowcasting Events from the Social Web with Statistical Learning|author1=V Lampos|author2=N Cristianini|journal=ACM Transactions on Intelligent Systems and Technology |volume=3|issue=4|page=72|doi=10.1145/2337542.2337557|year=2012|url=http://www.lampos.net/sites/default/files/papers/lampos2012nowcasting.pdf}}</ref><ref>{{cite conference|title=NOAM: news outlets analysis and monitoring system|author1=I. Flaounas|author2=O. Ali|author3=M. Turchi|author4=T Snowsill|author5=F Nicart|author6=T De Bie|author7=N Cristianini|conference=Proc. of the 2011 ACM SIGMOD international conference on Management of data|year=2011|url=http://www.tijldebie.net/system/files/SIGMOD_11_demo_Ilias.pdf|doi=10.1145/1989323.1989474}}</ref><ref>{{cite book|author=N Cristianini|title=''Combinatorial Pattern Matching''|pages=2–13|year=2011|volume=6661|series= Lecture Notes in Computer Science|isbn=978-3-642-21457-8|doi=10.1007/978-3-642-21458-5_2|chapter=Automatic Discovery of Patterns in Media Content|citeseerx=10.1.1.653.9525}}</ref><ref>{{Cite journal|last=Lansdall-Welfare|first=Thomas|last2=Sudhahar|first2=Saatviga|last3=Thompson|first3=James|last4=Lewis|first4=Justin|last5=Team|first5=FindMyPast Newspaper|last6=Cristianini|first6=Nello|date=2017-01-09|title=Content analysis of 150 years of British periodicals|url=http://www.pnas.org/content/early/2017/01/03/1606380114|journal=Proceedings of the National Academy of Sciences|volume=114|issue=4|language=en|pages=E457–E465|doi=10.1073/pnas.1606380114|issn=0027-8424|pmid=28069962|pmc=5278459}}</ref> The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al.<ref>{{cite journal|author1=I. Flaounas|author2=O. Ali|author3=M. Turchi|author4=T. Lansdall-Welfare|author5=T. De Bie|author6=N. Mosdell|author7=J. Lewis|author8=N. Cristianini|title=Research methods in the age of digital journalism|journal=Digital Journalism|year=2012|doi=10.1080/21670811.2012.714928|volume=1|pages=102–116}}</ref> showing how different topics have different gender biases and levels of readability; the possibility to detect mood shifts in a vast population by analysing Twitter content was demonstrated as well.<ref>{{cite conference|title=Effects of the Recession on Public Mood in the UK|author=T Lansdall-Welfare|author2=V Lampos|author3=N Cristianini|series=Mining Social Network Dynamics (MSND) session on Social Media Applications|doi=10.1145/2187980.2188264|conference=Proceedings of the 21st International Conference on World Wide Web|pages=1221–1226|location=New York, NY, USA|url=http://www.cs.bris.ac.uk/Publications/Papers/2001521.pdf}}</ref>

[[Content analysis]] has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "[[big data]]" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. [[Gender bias]], [[readability]], content similarity, reader preferences, and even mood have been analyzed based on [[text mining]] methods over millions of documents.<ref>{{cite journal|author1=I. Flaounas|author2=M. Turchi|author3=O. Ali|author4=N. Fyson|author5=T. De Bie|author6=N. Mosdell|author7=J. Lewis|author8=N. Cristianini|title=The Structure of EU Mediasphere|journal=PLOS One|volume=5|issue=12|pages=e14243|year=2010|doi=10.1371/journal.pone.0014243|url=https://orca-mwe.cf.ac.uk/50732/1/Flaounas%202010.pdf|pmid=21170383|pmc=2999531|bibcode=2010PLoSO...514243F}}</ref><ref>{{cite journal|title=Nowcasting Events from the Social Web with Statistical Learning|author1=V Lampos|author2=N Cristianini|journal=ACM Transactions on Intelligent Systems and Technology |volume=3|issue=4|page=72|doi=10.1145/2337542.2337557|year=2012|url=http://www.lampos.net/sites/default/files/papers/lampos2012nowcasting.pdf}}</ref><ref>{{cite conference|title=NOAM: news outlets analysis and monitoring system|author1=I. Flaounas|author2=O. Ali|author3=M. Turchi|author4=T Snowsill|author5=F Nicart|author6=T De Bie|author7=N Cristianini|conference=Proc. of the 2011 ACM SIGMOD international conference on Management of data|year=2011|url=http://www.tijldebie.net/system/files/SIGMOD_11_demo_Ilias.pdf|doi=10.1145/1989323.1989474}}</ref><ref>{{cite book|author=N Cristianini|title=''Combinatorial Pattern Matching''|pages=2–13|year=2011|volume=6661|series= Lecture Notes in Computer Science|isbn=978-3-642-21457-8|doi=10.1007/978-3-642-21458-5_2|chapter=Automatic Discovery of Patterns in Media Content|citeseerx=10.1.1.653.9525}}</ref><ref>{{Cite journal|last=Lansdall-Welfare|first=Thomas|last2=Sudhahar|first2=Saatviga|last3=Thompson|first3=James|last4=Lewis|first4=Justin|last5=Team|first5=FindMyPast Newspaper|last6=Cristianini|first6=Nello|date=2017-01-09|title=Content analysis of 150 years of British periodicals|url=http://www.pnas.org/content/early/2017/01/03/1606380114|journal=Proceedings of the National Academy of Sciences|volume=114|issue=4|language=en|pages=E457–E465|doi=10.1073/pnas.1606380114|issn=0027-8424|pmid=28069962|pmc=5278459}}</ref> The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al.<ref>{{cite journal|author1=I. Flaounas|author2=O. Ali|author3=M. Turchi|author4=T. Lansdall-Welfare|author5=T. De Bie|author6=N. Mosdell|author7=J. Lewis|author8=N. Cristianini|title=Research methods in the age of digital journalism|journal=Digital Journalism|year=2012|doi=10.1080/21670811.2012.714928|volume=1|pages=102–116}}</ref> showing how different topics have different gender biases and levels of readability; the possibility to detect mood shifts in a vast population by analysing Twitter content was demonstrated as well.<ref>{{cite conference|title=Effects of the Recession on Public Mood in the UK|author=T Lansdall-Welfare|author2=V Lampos|author3=N Cristianini|series=Mining Social Network Dynamics (MSND) session on Social Media Applications|doi=10.1145/2187980.2188264|conference=Proceedings of the 21st International Conference on World Wide Web|pages=1221–1226|location=New York, NY, USA|url=http://www.cs.bris.ac.uk/Publications/Papers/2001521.pdf}}</ref>

Content analysis has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents. The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al. showing how different topics have different gender biases and levels of readability; the possibility to detect mood shifts in a vast population by analysing Twitter content was demonstrated as well.

Content analysis has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents. The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al. showing how different topics have different gender biases and levels of readability; the possibility to detect mood shifts in a vast population by analysing Twitter content was demonstrated as well.

长期以来，内容分析一直是社会科学和媒体研究的传统方法。内容分析的自动化使得这一领域发生了一场“大数据”革命，这些研究中，社交媒体和报纸内容包括了数百万条的新闻。基于<font color='blue'>数以百万计的文档和</font>文本挖掘方法，'''性别偏差Gender Bias'''、'''可读性 Readability'''、'''内容相似度 Content Similarity'''、'''读者偏好 Reader Preferences'''，甚至是'''情绪 Mood'''<s>的数以百万计的文档</s>都可以被分析。Flaounas等人对可读性、性别偏差和'''话题偏差 Topic Bias'''<s>为主要研究对象</s><font color='blue'>的研究</font>，展示了不同的话题如何有不同<s>水平</s>的性别偏差和可读性<font color='blue'>水平</font>; <s>展示了</s><font color='blue'>以及</font>通过分析推特内容来检测人群情绪变化的可能性。

长期以来，内容分析一直是社会科学和媒体研究的传统方法。内容分析的自动化使得这一领域发生了一场“大数据”革命，这些研究中，社交媒体和报纸内容包括了数百万条的新闻。基于数以百万计的文档和文本挖掘方法，'''性别偏差Gender Bias'''、'''可读性 Readability'''、'''内容相似度 Content Similarity'''、'''读者偏好 Reader Preferences'''，甚至是'''情绪 Mood'''都可以被分析。Flaounas等人对可读性、性别偏差和'''话题偏差 Topic Bias'''的研究，展示了不同的话题如何有不同的性别偏差和可读性水平; 以及通过分析推特内容来检测人群情绪变化的可能性。

/ 参考

/ 参考

==Challenges 挑战==

==挑战==

Computational sociology, as with any field of study, faces a set of challenges.<ref name="MCSS">Conte, Rosaria, et al. "[https://link.springer.com/content/pdf/10.1140%252Fepjst%252Fe2012-01697-8.pdf Manifesto of computational social science]." The European Physical Journal Special Topics 214.1 (2012): 325-346.</ref> These challenges need to be handled meaningfully so as to make the maximum impact on society.

Computational sociology, as with any field of study, faces a set of challenges.<ref name="MCSS">Conte, Rosaria, et al. "[https://link.springer.com/content/pdf/10.1140%252Fepjst%252Fe2012-01697-8.pdf Manifesto of computational social science]." The European Physical Journal Special Topics 214.1 (2012): 325-346.</ref> These challenges need to be handled meaningfully so as to make the maximum impact on society.

Computational sociology, as with any field of study, faces a set of challenges. These challenges need to be handled meaningfully so as to make the maximum impact on society.

Computational sociology, as with any field of study, faces a set of challenges. These challenges need to be handled meaningfully so as to make the maximum impact on society.

===Levels and their interactions 层级和层级的交互关系===

===层级和层级的交互关系===

Each society that is formed tends to be in one level or the other and there exists tendencies of interactions between and across these levels. Levels need not only be micro-level or macro-level in nature. There can be intermediate levels in which a society exists say - groups, networks, communities etc.<ref name="MCSS" />

Each society that is formed tends to be in one level or the other and there exists tendencies of interactions between and across these levels. Levels need not only be micro-level or macro-level in nature. There can be intermediate levels in which a society exists say - groups, networks, communities etc.<ref name="MCSS" />

Each society that is formed tends to be in one level or the other and there exists tendencies of interactions between and across these levels. Levels need not only be micro-level or macro-level in nature. There can be intermediate levels in which a society exists say - groups, networks, communities etc.

Each society that is formed tends to be in one level or the other and there exists tendencies of interactions between and across these levels. Levels need not only be micro-level or macro-level in nature. There can be intermediate levels in which a society exists say - groups, networks, communities etc.

===Culture modeling 文化建模===

===文化建模===

The evolution of the networks and levels in the society brings about cultural diversity.<ref>{{cite journal|last1=Centola|first1=D.|last2=Gonz´alez-Avella|first2=J. C.|last3=Egu´ıluz|first3=V. M.|last4=San Miguel|first4=M.|title=Journal of Conflict Resolution|issue=2007|pages=51}}</ref> A thought which arises however is that, when people tend to interact and become more accepting of other cultures and beliefs, how is it that diversity still persists? Why is there no convergence? A major challenge is how to model these diversities. Are there external factors like mass media, locality of societies etc. which influence the evolution or persistence of cultural diversities?{{Citation needed|reason=Sounds just like personal thoughts|date=May 2017}}

The evolution of the networks and levels in the society brings about cultural diversity.<ref>{{cite journal|last1=Centola|first1=D.|last2=Gonz´alez-Avella|first2=J. C.|last3=Egu´ıluz|first3=V. M.|last4=San Miguel|first4=M.|title=Journal of Conflict Resolution|issue=2007|pages=51}}</ref> A thought which arises however is that, when people tend to interact and become more accepting of other cultures and beliefs, how is it that diversity still persists? Why is there no convergence? A major challenge is how to model these diversities. Are there external factors like mass media, locality of societies etc. which influence the evolution or persistence of cultural diversities?{{Citation needed|reason=Sounds just like personal thoughts|date=May 2017}}

===Experimentation and evaluation 实验和评估===

===实验和评估===

Any study or modelling when combined with experimentation needs to be able to address the questions being asked. [[Computational social science]] deals with large scale data and the challenge becomes much more evident as the scale grows. How would one design informative simulations on a large scale? And even if a large scale simulation is brought up, how is the evaluation supposed to be performed?

Any study or modelling when combined with experimentation needs to be able to address the questions being asked. [[Computational social science]] deals with large scale data and the challenge becomes much more evident as the scale grows. How would one design informative simulations on a large scale? And even if a large scale simulation is brought up, how is the evaluation supposed to be performed?

===Model choice and model complexities 模型选择和模型复杂性===

===模型选择和模型复杂性===

Another challenge is identifying the models that would best fit the data and the complexities of these models. These models would help us predict how societies might evolve over time and provide possible explanations on how things work.<ref>Weisberg, Michael. [http://www.academia.edu/download/30604870/lessmorefinal.pdf When less is more: Tradeoffs and idealization in model building]. Diss. Stanford University, 2003.</ref>

Another challenge is identifying the models that would best fit the data and the complexities of these models. These models would help us predict how societies might evolve over time and provide possible explanations on how things work.<ref>Weisberg, Michael. [http://www.academia.edu/download/30604870/lessmorefinal.pdf When less is more: Tradeoffs and idealization in model building]. Diss. Stanford University, 2003.</ref>

====Generative models 生成模型====

====生成模型====

Generative models helps us to perform extensive qualitative analysis in a controlled fashion. A model proposed by Epstein, is the agent-based simulation, which talks about identifying an initial set of heterogeneous entities (agents) and observe their evolution and growth based on simple local rules.<ref>Epstein, Joshua M. [https://www.researchgate.net/profile/Eric_Jones14/publication/283615593_Book_Review_-_Generative_Social_Science_Studies_in_Agent-Based_Computational_Modeling/links/5641398808aebaaea1f70216.pdf Generative social science: Studies in agent-based computational modeling]. Princeton University Press, 2006.</ref>

Generative models helps us to perform extensive qualitative analysis in a controlled fashion. A model proposed by Epstein, is the agent-based simulation, which talks about identifying an initial set of heterogeneous entities (agents) and observe their evolution and growth based on simple local rules.<ref>Epstein, Joshua M. [https://www.researchgate.net/profile/Eric_Jones14/publication/283615593_Book_Review_-_Generative_Social_Science_Studies_in_Agent-Based_Computational_Modeling/links/5641398808aebaaea1f70216.pdf Generative social science: Studies in agent-based computational modeling]. Princeton University Press, 2006.</ref>

Generative models helps us to perform extensive qualitative analysis in a controlled fashion. A model proposed by Epstein, is the agent-based simulation, which talks about identifying an initial set of heterogeneous entities (agents) and observe their evolution and growth based on simple local rules.

Generative models helps us to perform extensive qualitative analysis in a controlled fashion. A model proposed by Epstein, is the agent-based simulation, which talks about identifying an initial set of heterogeneous entities (agents) and observe their evolution and growth based on simple local rules.

'''生成模型 Generative Models'''帮助我们以一种受控的方式进行广泛的定性分析。爱泼斯坦（Epstein）提出的一个基于主体的模型，就是探讨如何识别异质实体(主体)的'''初始集 Initial Set'''，并<s>根据简单的局部规则</s>观察它们<font color='blue'>基于简单的局部规则带来</font>的演化和增长。

'''生成模型 Generative Models'''帮助我们以一种受控的方式进行广泛的定性分析。'''爱泼斯坦 Epstein'''提出的一个基于主体的模型，就是探讨如何识别异质实体(主体)的'''初始集 Initial Set'''，并观察它们基于简单的局部规则带来的演化和增长。

====Heterogeneous or ensemble models 异质模型和组合模型====

====异质模型和组合模型====

Integrating simple models which perform better on individual tasks to form a Hybrid model is an approach that can be looked into{{Citation needed|reason=unsupported claims|date=May 2017}}. These models can offer better performance and understanding of the data. However the trade-off of identifying and having a deep understanding of the interactions between these simple models arises when one needs to come up with one combined, well performing model. Also, coming up with tools and applications to help analyse and visualize the data based on these hybrid models is another added challenge.

Integrating simple models which perform better on individual tasks to form a Hybrid model is an approach that can be looked into{{Citation needed|reason=unsupported claims|date=May 2017}}. These models can offer better performance and understanding of the data. However the trade-off of identifying and having a deep understanding of the interactions between these simple models arises when one needs to come up with one combined, well performing model. Also, coming up with tools and applications to help analyse and visualize the data based on these hybrid models is another added challenge.

集成在单个任务中表现良好的简单模型并形成'''混合模型 Hybrid model'''是一种可以继续考虑的方法。这些模型有更好的性能并可以对数据提供更好的理解。然而，当需要提出一个整合的、性能良好的模型时，识别简单模型和深入理解模型之间相互作用的权衡问题就出现了。此外，另一个挑战是开发工具和应用程序来帮助分析和可视化基于这些混合模型的数据。

集成在单个任务中表现良好的简单模型并形成'''混合模型 Hybrid model'''是一种可以继续考虑的方法。这些模型有更好的性能并可以对数据提供更好的理解。然而，当需要提出一个整合的、性能良好的模型时，识别简单模型和深入理解模型之间相互作用的权衡问题就出现了。此外，另一个挑战是开发工具和应用程序来帮助分析和可视化基于这些混合模型的数据。

==Impact 影响==

==影响==

Computational sociology can bring impacts to science, technology and society.<ref name="MCSS" />

Computational sociology can bring impacts to science, technology and society.<ref name="MCSS" />

===Impact on science 对科学的影响===

===对科学的影响===

In order for the study of computational sociology to be effective, there has to be valuable innovations. These innovation can be of the form of new data analytics tools, better models and algorithms. The advent of such innovation will be a boon for the scientific community in large. {{Citation needed|reason=citation for claim on innovation and the type of innovation|date=May 2017}}

In order for the study of computational sociology to be effective, there has to be valuable innovations. These innovation can be of the form of new data analytics tools, better models and algorithms. The advent of such innovation will be a boon for the scientific community in large. {{Citation needed|reason=citation for claim on innovation and the type of innovation|date=May 2017}}

===Impact on society 对社会的影响===

===对社会的影响===

One of the major challenges of computational sociology is the modelling of social processes {{Citation needed|reason=to support the claim for need of modeling of social processes|date=May 2017}}. Various law and policy makers would be able to see efficient and effective paths to issue new guidelines and the mass in general would be able to evaluate and gain fair understanding of the options presented in front of them enabling an open and well balanced decision process. {{Citation needed|reason=unsupported claims|date=May 2017}}.

One of the major challenges of computational sociology is the modelling of social processes {{Citation needed|reason=to support the claim for need of modeling of social processes|date=May 2017}}. Various law and policy makers would be able to see efficient and effective paths to issue new guidelines and the mass in general would be able to evaluate and gain fair understanding of the options presented in front of them enabling an open and well balanced decision process. {{Citation needed|reason=unsupported claims|date=May 2017}}.

==Academic programs, departments and degrees 学术课程、院系和学位==

==学术课程、院系和学位==

* [http://mediapatterns.enm.bris.ac.uk/ University of Bristol "Mediapatterns" project]

* [http://mediapatterns.enm.bris.ac.uk/ University of Bristol "Mediapatterns" project]

==Centers and institutes 中心和机构==

==中心和机构==

===North America 北美===

===北美===

* [http://cnets.indiana.edu/ Center for Complex Networks and Systems Research], Indiana University, Bloomington, IN, USA.

* [http://cnets.indiana.edu/ Center for Complex Networks and Systems Research], Indiana University, Bloomington, IN, USA.

===South America 南美===

===南美===

* [http://www5.each.usp.br/mestrado-academico-em-modelagem-de-sistemas-complexos/ Modelagem de Sistemas Complexos], University of São Paulo - EACH, São Paulo, SP, Brazil

* [http://www5.each.usp.br/mestrado-academico-em-modelagem-de-sistemas-complexos/ Modelagem de Sistemas Complexos], University of São Paulo - EACH, São Paulo, SP, Brazil

===Europe 欧洲===

===欧洲===

* [http://cfpm.org Centre for Policy Modelling], Manchester, UK.

* [http://cfpm.org Centre for Policy Modelling], Manchester, UK.

===Asia 亚洲===

===亚洲===

* [http://bandungfe.net Bandung Fe Institute, Centre for Complexity in Surya University], Bandung, Indonesia.

* [http://bandungfe.net Bandung Fe Institute, Centre for Complexity in Surya University], Bandung, Indonesia.

== See also 参见==

==参见==

* [[Artificial society]] 人工社会

* [[Artificial society]] 人工社会

==External links 外部链接==

==外部链接==

*[http://cress.soc.surrey.ac.uk/s4ss/ On-line book "Simulation for the Social Scientist" by Nigel Gilbert and Klaus G. Troitzsch, 1999, second edition 2005 社会科学家的仿真建模 电子书 作者：Nigel Gilbert and Klaus G. Troitzsch 第二版（2005年版）]

*[http://cress.soc.surrey.ac.uk/s4ss/ On-line book "Simulation for the Social Scientist" by Nigel Gilbert and Klaus G. Troitzsch, 1999, second edition 2005 社会科学家的仿真建模 电子书 作者：Nigel Gilbert and Klaus G. Troitzsch 第二版（2005年版）]