计算社会学

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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.[1]

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。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。


It involves the understanding of social agents, the interaction among these agents, and the effect of these interactions on the social aggregate.[2] 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.[3][4] 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.

它涉及理解社会行为主体和它们的相互作用，以及这些相互作用如何影响社会总体(--嘉树（讨论） social aggregate)。虽然社会科学的主题和方法不同于自然科学或计算机科学，但当代社会仿真模拟中使用的一些方法起源于物理学和人工智能等领域。这一领域的一些方法，如来自社会网络分析和网络科学领域的网络中心性度量方法等，早已被引入到自然科学中。


In relevant literature, computational sociology is often related to the study of social complexity.[5] Social complexity concepts such as complex systems, non-linear interconnection among macro and micro process, and emergence, have entered the vocabulary of computational sociology.[6] A practical and well-known example is the construction of a computational model in the form of an "artificial society", by which researchers can analyze the structure of a social system.[2]引用错误：没有找到与</ref>对应的<ref>标签 The idea behind emergence is that properties of any bigger system do not always have to be properties of the components that the system is made of.[7] The people responsible for the introduction of the idea of emergence are Alexander, Morgan, and Broad, who were classical emergentists. The time at which these emergentists came up with this concept and method was during the time of the early twentieth century. The aim of this method was to find a good enough accommodation between two different and extreme ontologies, which were reductionist materialism and dualism.[8]

In the past four decades, computational sociology has been introduced and gaining popularity. This has been used primarily for modeling or building explanations of social processes and are depending on the emergence of complex behavior from simple activities. The idea behind emergence is that properties of any bigger system do not always have to be properties of the components that the system is made of. The people responsible for the introduction of the idea of emergence are Alexander, Morgan, and Broad, who were classical emergentists. The time at which these emergentists came up with this concept and method was during the time of the early twentieth century. The aim of this method was to find a good enough accommodation between two different and extreme ontologies, which were reductionist materialism and dualism.

在过去的四十年里，计算社会学诞生并且越来越受欢迎。这主要用于建模或构建社会过程的解释，并且依赖于从简单活动中涌现 Emergence的复杂行为。涌现背后的思想是，任何较大系统不总是必须和组成系统的部分具有一样的性质。引入涌现思想的人是亚历山大 Alexander、摩根 Morgan和布罗德 Broad，他们都是经典涌现主义者 Classical Emergentists。经典涌现主义者提出这个概念和方法的时间是在二十世纪初期。这种方法的目的是在还原论的唯物主义和二元论这两种不同的本体论之间找到一个足够好的调和。


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".[8]

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对这种用法持怀疑态度，因为有些方面是这个用法无法解释的。爱泼斯坦提出了一种反对“涌现主义”的观点，他表示:“正是这些部分的有生成意义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.[8]

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，应用程序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

Main articles: Systems theory and 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,[9] 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.[10] 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.[11] Because computer algorithms and programs had been used as early as 1956 to test and validate mathematical theorems, such as the four color theorem,[12] 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。按照涂尔干（Émile Durkheim）分析特定的复杂现代社会

（--嘉树（讨论）Following Émile Durkheim's call to analyze complex modern society sui generis） 

 --~~克劳德·香农（Claude Shannon）  这些同样去括号

的要求，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和等级相互作用的理论，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命题和精确术语的等级结构，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“解决”和“证明”类似的社会结构和动态的问题和定理。

Macrosimulation and microsimulation 宏观模拟和微观模拟

Main articles: System dynamics and 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.[13][14] 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,[15] the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific.[2][16] 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.[17] However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.[1]

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，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。 

 （--嘉树（讨论） 根据意思，inconvenient译为悲观的，因为这个结论预示了悲观的未来，所以令指出这一点的科学家们感到inconvenient）

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（--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 元胞自动机和基于主体建模

Main articles: Cellular automata and 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.[18] 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.[2] 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.[1] Agent-based models are less concerned with predictive accuracy and instead emphasize theoretical development.[19] 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.[20] 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.[4] 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 数据挖掘和社会网络分析

Main articles: Data mining and Social network analysis

--嘉树（讨论） 少一段原文 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.[21] 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.[22] 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.[23][24]

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计算机数据库中电子代理的行为数据。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（--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。


Narrative network of US Elections 2012[25]

Narrative network of US Elections 2012

【图2：Narrative network of US Elections 2012 + 2012年美国大选叙事网络】

The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale, turning textual data into network data. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from Network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.[26] This automates the approach introduced by quantitative narrative analysis,[27] whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.[25]

The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale, turning textual data into network data. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from Network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes. This automates the approach introduced by quantitative narrative analysis, whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.

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 计算机内容分析 （名词翻译可吗？）

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.[28][29][30][31][32] The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al.[33] 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.[34]

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的数以百万计的文档都可以被分析。Flaounas等人对可读性、性别偏差和话题偏差 Topic Bias为主要研究对象，展示了不同的话题如何有不同水平的性别偏差和可读性; 展示了通过分析推特内容来检测人群情绪变化的可能性。


The analysis of vast quantities of historical newspaper content has been pioneered by Dzogang et al.,[35] which showed how periodic structures can be automatically discovered in historical newspapers. A similar analysis was performed on social media, again revealing strongly periodic structures.引用错误：没有找到与</ref>对应的<ref>标签

</ref>

/ 参考

Challenges 挑战

Computational sociology, as with any field of study, faces a set of challenges.[36] 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.

计算社会学和其他研究领域一样面临着一系列的挑战。必须有意义地处理这些挑战（--嘉树（讨论） meaningfully怎么翻译才比较通顺？），才能对社会产生最大的影响。


Levels and their interactions 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.[36]

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.

形成的每个社会都处于某种层次上，并且这些层次之间有相互作用的倾向。社会中的级别不仅可以分为微观或宏观的层次，还可以存在中间的层次，比如群体、网络、社区等。 （--嘉树（讨论） 合并了英文的原句）


The question however arises as to how to identify these levels and how they come into existence? And once they are in existence how do they interact within themselves and with other levels?

The question however arises as to how to identify these levels and how they come into existence? And once they are in existence how do they interact within themselves and with other levels?

然而问题是，这些层次是如何确定的以及如何产生的？并且，一旦它们产生了，它们如何在自身内部以及与其他层次相互作用？


If we view entities (agents) as nodes and the connections between them as the edges, we see the formation of networks. The connections in these networks do not come about based on just objective relationships between the entities, rather they are decided upon by factors chosen by the participating entities.[37] The challenge with this process is that, it is difficult to identify when a set of entities will form a network. These networks may be of trust networks, co-operation networks, dependence networks etc. There have been cases where heterogeneous set of entities have shown to form strong and meaningful networks among themselves.[38][39]

If we view entities (agents) as nodes and the connections between them as the edges, we see the formation of networks. The connections in these networks do not come about based on just objective relationships between the entities, rather they are decided upon by factors chosen by the participating entities. The challenge with this process is that, it is difficult to identify when a set of entities will form a network. These networks may be of trust networks, co-operation networks, dependence networks etc. There have been cases where heterogeneous set of entities have shown to form strong and meaningful networks among themselves.

如果我们把实体 Entities（主体 Agents）看作节点，把它们之间的连接看作边，我们就看到了网络的形成。这些网络中的连接并非只是基于实体之间的客观关系，而是由实体选择的因素决定的。这个过程的困难之处在于，很难确定一组实体何时形成网络。这些网络可以是信任网络、合作网络、依赖网络等。在一些情况下，异质的实体集在它们之间形成了强大而有意义的网络。


As discussed previously, societies fall into levels and in one such level, the individual level, a micro-macro link[40] refers to the interactions which create higher-levels. There are a set of questions that needs to be answered regarding these Micro-Macro links. How they are formed? When do they converge? What is the feedback pushed to the lower levels and how are they pushed?

As discussed previously, societies fall into levels and in one such level, the individual level, a micro-macro link refers to the interactions which create higher-levels. There are a set of questions that needs to be answered regarding these Micro-Macro links. How they are formed? When do they converge? What is the feedback pushed to the lower levels and how are they pushed?

正如前面所讨论的，社会分为不同层次。在其中的一个层次：个体层次中，微观-宏观联系 Micro-macro Link指的是创造更高层次的相互作用。关于微观-宏观链接有一系列问题等待回答。它们是如何形成的？它们什么时候汇聚？什么反馈被推到了较低的层次，以及它们是如何被推到较低层次的？


Another major challenge in this category concerns the validity of information and their sources. In recent years there has been a boom in information gathering and processing. However, little attention was paid to the spread of false information between the societies. Tracing back the sources and finding ownership of such information is difficult.

Another major challenge in this category concerns the validity of information and their sources. In recent years there has been a boom in information gathering and processing. However, little attention was paid to the spread of false information between the societies. Tracing back the sources and finding ownership of such information is difficult.

这一分类的另一个主要挑战涉及信息的有效性及其来源。近年来，信息收集和处理有着蓬勃的发展。但人们很少注意到社会之间虚假信息的传播。追溯资料来源并找到这些资料的所有者是很困难的。


Culture modeling 文化建模

The evolution of the networks and levels in the society brings about cultural diversity.[41] 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]

The evolution of the networks and levels in the society brings about cultural diversity. 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?

社会网络和层次的演变带来了文化的多样性。然而人们会想，当人们倾向于相互作用，变得更能接受其他文化和信仰时，为什么多样性仍然存在？为什么人们没有趋同？一个主要的挑战是如何为这些多样性建立模型。是否存在大众传媒、社会地域性等外部因素，这些因素影响文化多样性的演化或保持？


Experimentation and evaluation 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?

任何与实验结合的研究或建模都需要能够解决所提出的问题。计算社会科学处理大规模数据，并且数据规模越大建模的挑战就越明显。如何在一个大规模的数据集里设计信息仿真模型？并且，即使提出了一个大规模的仿真模型，应该如何对它进行评估？


Model choice and model complexities 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.[42]

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.

另一个挑战是确定最适合数据的模型，以及最合适的模型复杂性 Model Complexities。这些模型将帮助我们预测随着时间的推移社会将如何演变，并为事物如何运动和变化提供可能的解释。


Generative models 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.[43]

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，并根据简单的局部规则观察它们的演化和增长。


But what are these local rules? How does one identify them for a set of heterogeneous agents? Evaluation and impact of these rules state a whole new set of difficulties.

But what are these local rules? How does one identify them for a set of heterogeneous agents? Evaluation and impact of these rules state a whole new set of difficulties.

但是这些局部规则是什么呢？如何识别一组异质实体？这些规则的评估和影响都是新出现的困难之处。


Heterogeneous or ensemble models 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]. 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. 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。

Impact 影响

Computational sociology can bring impacts to science, technology and society.[36]

Computational sociology can bring impacts to science, technology and society.

计算社会学可以给科学、技术和社会带来影响。


Impact on science 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]

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.

为了使计算社会学的研究有效，必须有一些有价值的创新。这些创新可以是新形式的数据分析工具，或更好的模型和算法。这些创新的出现将为整个科学界带来益处。


Impact on society 对社会的影响

One of the major challenges of computational sociology is the modelling of social processes[citation needed]. 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].

One of the major challenges of computational sociology is the modelling of social processes . 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.

计算社会学的主要挑战之一是对社会过程的建模。各种法律和政策制定者将能够得到发布新指导方针的快速有效的方法，广大群众将能够评价和更好地理解各种备选方案，从而实现决策进程的公开和平衡。

Journals and academic publications 期刊和学术出版物

The most relevant journal of the discipline is the Journal of Artificial Societies and Social Simulation.

The most relevant journal of the discipline is the Journal of Artificial Societies and Social Simulation.

该学科最相关的期刊是《人工社会与社会模拟（the Journal of Artificial Societies and Social Simulation）》。


Associations, conferences and workshops 协会、会议及工作坊


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

  • UCLA, Minor in Human Complex Systems
  • UCLA, Major in Computational & Systems Biology (including behavioral sciences)


Centers and institutes 中心和机构

North America 北美


South America 南美


Europe 欧洲


Asia 亚洲


See also 参见

References

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  43. ↑ Epstein, Joshua M. Generative social science: Studies in agent-based computational modeling. Princeton University Press, 2006.


External links 外部链接

Category:Subfields of sociology

范畴: 社会学的子领域

Category:Complex systems theory

范畴: 复杂系统理论

Category:Methods in sociology

范畴: 社会学方法

Category:Computational fields of study

类别: 研究的计算领域


This page was moved from wikipedia:en:Computational sociology. Its edit history can be viewed at 社会计算/edithistory

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计算社会科学诞生至今，已有将近10年。在自然科学领域已经广泛应用大数据、分布式计算等方式提高研究质量的今天，社会科学领域涉及到的核心数据仍然被少数机构所拥有并创造着巨大的价值。本文将从计算社会科学的发展历史，常用方法，以及前沿问题来给大家粗略地展示计算社会科学的脉络。

取自“https://wiki.swarma.org/index.php?title=计算社会学&oldid=10814”