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第1行： − 此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。 − − − + − − 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] − − − 计算社会科学是社会学的一个分支，通过使用计算机模拟、人工智能、复杂的统计学方法，社会网络分析等技术分析计算社会学的发展，以及对社会互动自下而上的建模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.<ref name="SfSS1">{{cite book|last1=Gilbert |first1=Nigel |last2=Troitzsch |first2=Klaus |year=2005 |title=Simulation for Social Scientists |edition=2 |chapter=Simulation and social science |publisher=Open University Press |chapter-url=http://cress.soc.surrey.ac.uk/s4ss/}}</ref> 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]].<ref name="Artificial Societies">{{cite book|last1=Epstein |first1=Joshua M. |last2=Axtell |first2=Robert |year=1996 |title=Growing Artificial Societies: Social Science from the Bottom Up |location=Washington DC |publisher=Brookings Institution Press|url=https://archive.org/details/growingartificia00epst|url-access=registration |isbn=978-0262050531 }}</ref><ref name="Cooperation">{{cite book|last1=Axelrod |first1=Robert |year=1997 |title=The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration |location=Princeton, NJ |publisher=Princeton University Press}}</ref> Some of the approaches that originated in this field have been imported into the natural sciences, such as measures of [[centrality|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.[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. − − − 它涉及对社会主体的理解，这些主体之间的相互作用，以及这些相互作用对社会整体产生的影响。 [2]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，但当前社会仿真中使用的一些方法起源于物理学和人工智能等领域。 [3][4]同时这个领域的一些方法也被应用于自然科学的其他领域，例如来自[[社会网络分析]]和[[网络科学]]领域的[[网络中心性]]度量。 − − In relevant literature, computational sociology is often related to the study of [[social complexity]].<ref>{{cite journal|last=Casti |first=J |year=1999 |title=The Computer as Laboratory: Toward a Theory of Complex Adaptive Systems |journal=Complexity |volume=4 |issue=5 |pages=12–14 |doi=10.1002/(SICI)1099-0526(199905/06)4:5<12::AID-CPLX3>3.0.CO;2-4}}</ref> Social complexity concepts such as [[complex systems]], [[non-linear]] interconnection among macro and micro process, and [[emergence]], have entered the vocabulary of computational sociology.<ref>{{cite journal|last=Goldspink |first=C |year=2002 |title=Methodological Implications of Complex Systems Approaches to Sociality: Simulation as a Foundation for Knowledge |url=http://jasss.soc.surrey.ac.uk/5/1/3.html |publisher=Journal of Artificial Societies and Social Simulation |volume=5 |issue=1}}</ref> 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 structure|social system]].<ref name="SfSS1"/><ref name="Generative">{{cite book|last=Epstein |first=Joshua |year=2007 |title=Generative Social Science: Studies in Agent-Based Computational Modeling |location=Princeton, NJ |publisher=Princeton University Press|url=https://www.researchgate.net/publication/283615593}}</ref><!-- − While editing this lead/intro, please keep your text ABOVE this line - this will make it easier to automatically insert the lede into other articles - thanks! − --></onlyinclude> + − 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][7]+ − 在相关文献中，计算社会科学经常与[[社会复杂性]]的研究有关。 [[复杂系统]]、宏观和微观过程之间的非线性相互联系、[[涌现]]等社会复杂性概念已经进入了计算社会科学中。 [6]一个实际而著名的例子是以“人工社会”的形式建造一个计算模型，通过它研究人员可以分析一个社会系统的结构。 [2][7]+ + − + − − Computational social science revolutionizes both fundamental legs of the [[scientific method]]: [[empirical research]], especially through [[big data]], by analyzing the [[digital footprint]] left behind through social online activities; and [[scientific theory]], especially through [[computer simulation]] model building through [[social simulation]].<ref>DT&SC 7-1: [https://www.youtube.com/watch?v=9x3d75ZMuYU . Introduction to e-Science]: From the DT&SC [https://canvas.instructure.com/courses/949415 online course] at the University of California</ref><ref name="HilbertICT4ICT4D">{{cite book|author=Hilbert, M.|year=2015|title=e-Science for Digital Development: ICT4ICT4D|publisher=Centre for Development Informatics, SEED, University of Manchester|url=http://www.seed.manchester.ac.uk/medialibrary/IDPM/working_papers/di/di-wp60.pdf|isbn=978-1-905469-54-3|url-status=dead|archiveurl=https://web.archive.org/web/20150924100018/http://www.seed.manchester.ac.uk/medialibrary/IDPM/working_papers/di/di-wp60.pdf|archivedate=2015-09-24}}</ref> It is a multi-disciplinary and integrated approach to social survey focusing on information processing by means of advanced information technology. The computational tasks include the analysis of social networks, social geographic systems,<ref>{{cite journal|title=Computational social science |first=Claudio |last=Cioffi-Revilla |journal=[[Wiley Interdisciplinary Reviews: Computational Statistics]] |year=2010 |volume=2 |issue=3 |pages=259–271 |doi=10.1002/wics.95}}</ref> social media content and traditional media content. − − Computational social science revolutionizes both fundamental legs of the scientific method: empirical research, especially through big data, by analyzing the digital footprint left behind through social online activities; and scientific theory, especially through computer simulation model building through social simulation. It is a multi-disciplinary and integrated approach to social survey focusing on information processing by means of advanced information technology. The computational tasks include the analysis of social networks, social geographic systems, social media content and traditional media content. − − 计算社会科学彻底改变了科学方法的两个基本支柱: 实证研究，特别是通过大数据，通过分析社会在线活动留下的数据痕迹; 科学理论，特别是通过社会模拟建立计算机模拟模型。 − − 利用先进的信息技术进行以信息处理为核心的社会调查是一种多学科、综合的方法。包括对社交网络、社交地理系统、社交媒体内容和传统媒体内容的分析计算。 − − == 发展历史 == 第51行：
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第31行： − + − + − + − + − − In the past four decades, computational sociology has been introduced and gaining popularity {{According to whom|date=June 2017}}. 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.<ref name="EACICS">Salgado, Mauricio, and Nigel Gilbert. "[http://epubs.surrey.ac.uk/749319/1/Emergence%20and%20Communication%20-%20V3%201.pdf Emergence and communication in computational sociology]." Journal for the Theory of Social Behaviour 43.1 (2013): 87-110.</ref> − 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.<ref>Macy, Michael W., and Robert Willer. "[http://sct.uab.cat/lsds/sites/sct.uab.cat.lsds/files/FROM%20FACTORS%20TO%20ACTORS%20(Macy%20and%20Willer%202002).pdf From factors to actors: computational sociology and agent-based modeling]." Annual review of sociology 28.1 (2002): 143-166.</ref> − 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.<ref name="EACICS"/> − − 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".<ref name="EACICS"/> − 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.<ref name="EACICS"/>+ − [[基于主体建模]]（Agent-based models）对计算社会学有着历史性的意义。这些模型最早出现在20世纪六十年代，用于模拟组织和城市等的控制和反馈机制。在七十年代时，基于主体的建模引入了个体（individual）作为主要的建模单元进行分析，闭关使用自底向上的策略来对行为建模。八十年代时发生的主要改变则是主体们的交互时独立的。<ref name="EACICS"/>+ − + − ===Systems theory and structural functionalism=== − {{main|Systems theory|Structural functionalism}} − In the post-war era, [[Vannevar Bush]]'s [[differential analyser]], [[John von Neumann]]'s [[Von Neumann cellular automata|cellular automata]], [[Norbert Wiener]]'s [[cybernetics]], and [[Claude Shannon]]'s [[information theory]] became influential paradigms for modeling and understanding complexity in technical systems. In response, scientists in disciplines such as physics, biology, electronics, and economics began to articulate a [[systems theory|general theory of systems]] in which all natural and physical phenomena are manifestations of interrelated elements in a system that has common patterns and properties. Following [[Émile Durkheim]]'s call to analyze complex modern society ''[[sui generis]]'',<ref>{{cite book|first=Émile |last=Durkheim |title=The Division of Labor in Society |location=New York, NY |publisher=Macmillan}}</ref> post-war structural functionalist sociologists such as [[Talcott Parsons]] seized upon these theories of systematic and hierarchical interaction among constituent components to attempt to generate grand unified sociological theories, such as the [[AGIL paradigm]].<ref name="Bailey">{{cite book|first1=Kenneth D. |last1=Bailey |editor=Jonathan H. Turner |chapter=Systems Theory |title=Handbook of Sociological Theory |publisher=Springer Science |year=2006 |location=New York, NY |isbn=978-0-387-32458-6 |pages=379–404}}</ref> Sociologists such as [[George Homans]] argued that sociological theories should be formalized into hierarchical structures of propositions and precise terminology from which other propositions and hypotheses could be derived and operationalized into empirical studies.<ref name="Blackwell">{{cite encyclopedia|year=2007 |title=Computational Sociology |last=Bainbridge |first=William Sims |encyclopedia=Blackwell Encyclopedia of Sociology |publisher=Blackwell Reference Online |url=http://www.sociologyencyclopedia.com/subscriber/tocnode?id=g9781405124331_chunk_g97814051243319_ss1-85 |doi=10.1111/b.9781405124331.2007.x |hdl=10138/224218 |editor=Ritzer, George|isbn=978-1-4051-2433-1|hdl-access=free }}</ref> Because computer algorithms and programs had been used as early as 1956 to test and validate mathematical theorems, such as the [[four color theorem]],<ref>{{cite book|last=Crevier |first=D. |year=1993 |title=AI: The Tumultuous History of the Search for Artificial Intelligence |url=https://archive.org/details/aitumultuoushist00crev |url-access=registration |publisher=Basic Books |location=New York, NY}}</ref> some scholars anticipated that similar computational approaches could "solve" and "prove" analogously formalized problems and theorems of social structures and dynamics. − − ===系统论和功能主义=== − − − ===Macrosimulation and microsimulation=== − {{main|System dynamics|Microsimulation}} − By the late 1960s and early 1970s, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. These models used differential equations to predict population distributions as holistic functions of other systematic factors such as inventory control, urban traffic, migration, and disease transmission.<ref>{{cite book|first=Jay |last=Forrester |year=1971 |title=World Dynamics |location=Cambridge, MA |publisher=MIT Press}}</ref><ref>{{cite journal|doi=10.1287/opre.26.2.237|title=Using Simulation to Develop and Validate Analytic Models: Some Case Studies |first1=Edward J. |last1=Ignall |first2=Peter |last2=Kolesar |first3=Warren E. |last3=Walker |journal=Operations Research |volume=26 |issue=2 |year=1978 |pages=237–253}}</ref> Although simulations of social systems received substantial attention in the mid-1970s after the [[Club of Rome]] published reports predicting that policies promoting exponential economic growth would eventually bring global environmental catastrophe,<ref>{{cite book|title=The Dynamics of Growth in a Finite World |last1=Meadows |first1=DL |last2=Behrens |first2=WW |last3=Meadows |first3=DH |last4=Naill |first4=RF |last5= Randers |first5=J |last6=Zahn |first6=EK |year=1974 |location=Cambridge, MA |publisher=MIT Press}}</ref> the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific.<ref name="SfSS1"/><ref>{{cite news|title=Computer View of Disaster Is Rebutted |newspaper=The New York Times |date=October 18, 1974|url=https://www.nytimes.com/1974/10/18/archives/computer-view-of-disaster-is-rebutted.html?_r=0}}</ref> Hoping to avoid the same fate, many social scientists turned their attention toward micro-simulation models to make forecasts and study policy effects by modeling aggregate changes in state of individual-level entities rather than the changes in distribution at the population level.<ref>{{cite journal|doi=10.1016/0167-2681(90)90038-F|title=From engineering to microsimulation : An autobiographical reflection |journal=Journal of Economic Behavior & Organization |year=1990 |volume=14 |issue=1 |pages=5–27 |first=Guy H. |last=Orcutt}}</ref> However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.<ref name="MW"/> 第101行：
第57行： − + − + − + − − 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 Samuel Coleman|James S. Coleman]], [[Harrison White]], [[Linton Freeman]], [[J. Clyde Mitchell]], [[Mark Granovetter]], [[Ronald Burt]], and [[Barry Wellman]].<ref>{{cite book|title=The Development of Social Network Analysis: A Study in the Sociology of Science |first=Linton C. |last=Freeman |publisher=Empirical Press |location=Vancouver, BC |year=2004}}</ref> − The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as [[network theory|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. − − − [[File:Tripletsnew2012.png|thumb|right|Narrative network of US Elections 2012<ref name="ReferenceA">{{cite journal|title=Automated analysis of the US presidential elections using Big Data and network analysis|author1=S Sudhahar|author2=GA Veltri|author3=N Cristianini|journal=Big Data & Society|volume=2|issue=1|pages=1–28|year=2015|doi=10.1177/2053951715572916|doi-access=free}}</ref>]] − 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.<ref>{{cite journal|title=Network analysis of narrative content in large corpora|author1=S Sudhahar|author2=G De Fazio|author3=R Franzosi|author4=N Cristianini|journal=Natural Language Engineering|volume=21|issue=1|pages=1–32|year=2013|doi=10.1017/S1351324913000247 |url=https://research-information.bristol.ac.uk/files/129621186/Network_Analysis_of_Narrative_Content_in_Large_Corpora.pdf}}</ref> This automates the approach introduced by quantitative narrative analysis,<ref>{{cite book|title=Quantitative Narrative Analysis|last=Franzosi|first=Roberto|publisher=Emory University|year=2010}}</ref> whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.<ref name="ReferenceA"/> − + − [[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> − − The analysis of vast quantities of historical newspaper content has been pioneered by Dzogang et al.,<ref>{{Cite journal|last=Dzogang|first=Fabon|last2=Lansdall-Welfare|first2=Thomas|last3=Team|first3=FindMyPast Newspaper|last4=Cristianini|first4=Nello|date=2016-11-08|title=Discovering Periodic Patterns in Historical News|journal=PLOS One|volume=11|issue=11|pages=e0165736|doi=10.1371/journal.pone.0165736|issn=1932-6203|pmc=5100883|pmid=27824911|bibcode=2016PLoSO..1165736D}}</ref> 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.<ref>[https://core.ac.uk/download/pdf/83929129.pdf Seasonal Fluctuations in Collective Mood Revealed by Wikipedia Searches and Twitter Posts] F Dzogang, T Lansdall-Welfare, N Cristianini - 2016 IEEE International Conference on Data Mining, Workshop on ''Data Mining'' in Human Activity Analysis − </ref> − 第139行：
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第94行： − + − + − 地理信息系统（GIS）最初是社会地理学家和制图员研究地理现象的可视化工具和空间分析的工具。目前在社会科学中有了许多应用，比如在犯罪学和区域经济学应用社会GIS可以有效的量化冲突，与其他的量化技术结合在一起可以产生一些使用数学和统计模型无法获得的有趣的见解。 − − − − − + − ==Challenges== − 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. − − 计算社会科学，和其他研究领域一样，面临着一系列的挑战。 [38]需要有意义地处理这些挑战，以便对社会产生最大的影响。 − − − + − ===Levels and their interactions=== + − 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" />+ − 形成的每个社会往往处于一个或另一个层级，并且在这些层级之间存在着相互作用。 层级并不仅仅是指微观层面或者宏观层面。 一个社会可以存在一些中间层次，比如群体、网络、社区等等。+ − + − 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，问题是如何确定这些层级以及是如何产生的？ 一旦层级存在，它们如何在内部与其他层级产生相互作用？ − − 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.<ref>{{cite journal|last1=Egu´ıluz|first1=V. M.|last2=Zimmermann|first2=M. G.|last3=Cela-Conde|first3=C. J.|last4=San Miguel|first4=M.|title=American Journal of Sociology|issue=2005|pages=110, 977}}</ref> 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.<ref>{{cite journal|last1=Sichman|first1=J. S.|last2=Conte|first2=R.|title=Computational & Mathematical Organization Theory|issue=2002|pages=8(2)}}</ref><ref>{{cite journal|last1=Ehrhardt|first1=G.|last2=Marsili|first2=M.|last3=Vega-Redondo|first3=F.|title=Physical Review E|issue=2006|pages=74(3)}}</ref> − − − 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.[39] 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.[40][41] − − 如果我们把实体(主体)看作节点，把它们之间的连接看作连边，就可以看到网络的形成。这些网络中的连接并非基于实体之间的客观存在的连线，而是由这些选择实体的因素决定的。 [39]这个过程的挑战在于，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，异质性实体之间也能形成强大而有意义的网络。 [41] − − As discussed previously, societies fall into levels and in one such level, the individual level, a micro-macro link<ref>Billari, Francesco C. [https://books.google.com/books?hl=en&lr=&id=Tc8iL0b-OycC&oi=fnd&pg=PA1&dq=%22Agent-based+computational+modelling:+applications+in+demography,+social,+economic+and+environmental+sciences%22&ots=6MNVK7JP-e&sig=-o_iS9zCK3r-t1eHn3554UVadUs Agent-based computational modelling: applications in demography, social, economic and environmental sciences]. Taylor & Francis, 2006.</ref> 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[42] 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，社会中有层级，由微观层面的个人层级，通过微观和宏观之间的相互作用可以产生更高的层级。 针对这样微观-宏观之间的连接有一系列的问题需要回答：它们是如何形成的？它们什么时候会收敛？被推到了较低的层次会有什么样的反馈，以及它们是如何被推动的？ − − − 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. − − 针对这个主题下的另一个主要挑战即是验证信息的有效性及其追溯信息的来源。 近年来，信息收集和处理蓬勃发展，然而，人们很少关注社会之间虚假信息的传播。因为追溯资料来源并找到这些资料的所有权是很困难的。 − − − ===Culture modeling=== − 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.[43] 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? − − 在社会中，网络和层级的演化会带来文化的多样性。 [43]因此，自然而然的一个想法是，当人们倾向于交流，变得更能接受其他文化和信仰时，为什么多样性仍然存在？ 为什么没有趋同？针对这个问题的一个主要挑战就是如何对多样性建模。 比如是否存在诸如大众传媒、社会地域属性等外部因素会影响文化多样性的演化或持续？ − − − ===Experimentation and evaluation=== − 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。计算社会科学能够处理大规模的数据，但是随着规模的增长挑战也越来越大。如何设计一个大规模的信息模拟？即使提出了一个大规模的模拟，评估应该如何进行？ − − − ===Model choice and model complexities=== − 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.[44] − − 另一个挑战是确定最适合数据，且复杂度合适的模型。这些模型将帮助我们预测随着时间的推移社会将如何演化，并为事物如何运作提供可能的解释。 [44] − + − ====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.<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> − − 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. − − − 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.[45] − 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. − − 生成模型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提出的一个模型是基于主体的模拟，该模型通过识别一组初始的异质实体(主体) ，并根据简单的局部规则观察它们的演化和增长。 [45] − + − − − − ==== 异质模型或者集成模型 ==== − 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]. 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. − − − 集成那些在单个任务中表现更好的简单模型来形成一个混合模型是一种可以探索的好的研究方法[需要引用]。 这些模型可以提供更好的预测效果和并且增强对数据的理解。 然而这里需要在效果和可解释性之间做权衡，当需要提出一个组合的、性能良好的模型时，也需要识别并深入理解这些简单模型之间的相互作用。此外，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是另一个额外的挑战。 − − − − − 以下部分内容来自中文wiki：繁体字的部分主要用于插入文献即可 − 計算社會科學日益依賴逐漸增加的大型[[資料庫]]，目前正由幾個跨領域計畫建置中或維護中的資料庫有： − * 塞莎特（Seshat）：全球歷史的資料庫，內容系統性的收集了關於內容群體政治社會組織的資訊，以及社會如何演化等。塞莎特隸屬於演化研究所，演化研究所為非營利智庫，目標為「利用[[演化論|演化科學]]來解決現實世界問題」。 − * [https://web.archive.org/web/20160818082017/https://d-place.org/home D-PLACE]：地方、語言、文化和環境資料庫，提供超過1400個人類社會形態的資料<ref>{{Cite journal|title=D-PLACE: A Global Database of Cultural, Linguistic and Environmental Diversity|first=Kathryn R.|last2=Gray|first2=Russell D.|date=2016|journal=PLoS ONE|issue=7|volume=11|last3=Greenhill|first3=Simon J.|last4=Jordan|first4=Fiona M.|last5=Gomes-Ng|first5=Stephanie|last6=Bibiko|first6=Hans-Jörg|last7=Blasi|first7=Damián E.|last8=Botero|first8=Carlos A.|last9=Bowern|first9=Claire}}Authors list列表中的<code style="color:inherit; border:inherit; padding:inherit;">|first1=</code>缺少<code style="color:inherit; border:inherit; padding:inherit;">|last1=</code> ([[Help:引文格式1错误#first missing last|帮助]]) − [[Category:引文格式1错误：缺少作者或编者]]</ref>。 − * 文化演化圖集：是由彼得·百富勤（Peter N. Peregrie）所建立的考古資料庫<ref>Peter N. Peregrine, ''Atlas of Cultural Evolution'', ''World Cultures'' 14(1), 2003</ref>。 − * <span>[http://www.chia.pitt.edu/ CHIA]：即歷史分析的協作資訊（</span>[http://www.chia.pitt.edu/ Collaborative Information for Historical Analysis]），是由[[匹茲堡大學]]主持的多學科合作項目，旨在將歷資訊資訊建檔，將數據與全球各地的研究機構連結起來。 − * 國際社會歷史研究所（International Institute of Social History）：收集關於勞動關係，工人和勞動的全球社會歷史的資料。 − * Human Relations Area Files eHRAF Archaeology<ref>{{Cite web|url=http://www.yale.edu/hraf/archaeology.htm|title=eHRAF Archaeology|publisher=Human Relations Area Files}}</ref>。 − * Human Relations Area Files eHRAF World Cultures<ref>{{Cite web|url=http://ehrafworldcultures.yale.edu/ehrafe/|title=eHRAF World Cultures|publisher=Human Relations Area Files}}</ref>。 − * [https://www.clio-infra.eu/ Clio-Infra]：從公元前1800年到現在的全球社會樣本的經濟績效和社會福利其他方面的資料庫。 − 對大量歷史報紙內容的分析率先顯示了如何自動發現週期性結構<ref>{{Cite journal|title=Content analysis of 150 years of British periodicals|url=http://www.pnas.org/content/early/2017/01/03/1606380114|last=Lansdall-Welfare|first=Thomas|last2=Sudhahar|first2=Saatviga|date=2017-01-09|journal=Proceedings of the National Academy of Sciences|doi=10.1073/pnas.1606380114|pages=201606380|language=en|issn=0027-8424|pmid=28069962|last3=Thompson|first3=James|last4=Lewis|first4=Justin|last5=Team|first5=FindMyPast Newspaper|last6=Cristianini|first6=Nello}}</ref><ref>{{Cite journal|title=Discovering Periodic Patterns in Historical News|url=http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0165736|last=Dzogang|first=Fabon|last2=Lansdall-Welfare|first2=Thomas|date=2016-11-08|journal=PLOS ONE|issue=11|doi=10.1371/journal.pone.0165736|volume=11|pages=e0165736|issn=1932-6203|pmc=5100883|pmid=27824911|last3=Team|first3=FindMyPast Newspaper|last4=Cristianini|first4=Nello}}</ref> ，對社群媒體進行類似的分析，也能看到明顯的週期性結構<ref>Seasonal Fluctuations in Collective Mood Revealed by Wikipedia Searches and Twitter Posts F Dzogang, T Lansdall-Welfare, N Cristianini - 2016 IEEE International Conference on Data Mining, Workshop on ''Data Mining'' in Human Activity Analysis</ref>。 − − − + − + + − + − + − + − − The analysis of vast quantities of historical newspaper and book content have been pioneered in 2017, while other studies on similar data showed how periodic structures can be automatically discovered in historical newspapers. A similar analysis was performed on social media, again revealing strongly periodic structures. − − 对大量历史报纸和书籍内容的分析在2017年开创了先河，而对类似数据的其他研究表明，周期结构可以在历史报纸中自动发现。在社交媒体上也进行了类似的分析，再次揭示了强烈的周期性结构。 − − − Computational sociology can bring impacts to science, technology and society.<ref name="MCSS" /> − − 计算社会学可以给科学、技术和社会带来影响。<ref name="MCSS" />+ − − − ===Impact on science=== − 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}} − 为了使计算社会科学的研究有效，必须有一些有价值的创新。 这些创新可以是新的数据分析工具、更好的模型和算法。 这种创新的出现对整个科学界来说都会带来很多的好处。+ − − ===Impact on society=== − 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}}. − + − − ==Journals and academic publications== − 这个学科最相关的期刊是[Journal of Artificial Societies and Social Simulation]。+ − ==Associations, conferences and workshops== − ==Academic programs, departments and degrees== 第371行：
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无编辑摘要
计算社会科学（Computational Social Science）是一门融合社会科学、数据科学和统计学等学科的新兴交叉学科，强调用大数据的方式来研究社会科学中的核心问题，是社会科学在大数据时代下发展的产物。领域包括计算经济学、计算社会学、动态学、文化学以及社交和传统媒体的内容自动分析。它着重于通过社会模拟、建模、网络分析和媒体分析来调查社会和行为关系和互动。<ref>{{cite web|url=https://computationalsocialscience.org/|title=The Computational Social Science Society of the Americas official website}}</ref>
计算社会科学（Computational Social Science）是一门融合社会科学、数据科学和统计学等学科的新兴交叉学科，强调用大数据的方式来研究社会科学中的核心问题，是社会科学在大数据时代下发展的产物。领域包括计算经济学、计算社会学、动态学、文化学以及社交和传统媒体的内容自动分析。它着重于通过社会模拟、建模、网络分析和媒体分析来调查社会和行为关系和互动。<ref>{{cite web|url=https://computationalsocialscience.org/|title=The Computational Social Science Society of the Americas official website}}</ref>
== 定义 ==
== 定义 ==
'''Computational sociology''' is a branch of [[sociology]] that uses computationally intensive methods to analyze and model social phenomena. Using [[computer simulation]]s, [[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.<ref name="MW">{{cite journal|doi=10.1146/annurev.soc.28.110601.141117|title=From Factors to Actors: Computational Sociology and Agent-Based Modeling |first1=Michael W. |last1=Macy |first2=Robert |last2=Willer |journal=Annual Review of Sociology |volume=28 |pages=143–166 |jstor=3069238|year=2002}}</ref>
'''计算社会科学'''是社会学的一个分支，通过使用计算机模拟、人工智能、复杂的统计学方法，社会网络分析等技术分析计算社会学的发展，以及对社会互动自下而上的建模检验复杂社会过程的理论。<ref name="MW">{{cite journal|doi=10.1146/annurev.soc.28.110601.141117|title=From Factors to Actors: Computational Sociology and Agent-Based Modeling |first1=Michael W. |last1=Macy |first2=Robert |last2=Willer |journal=Annual Review of Sociology |volume=28 |pages=143–166 |jstor=3069238|year=2002}}</ref>
它涉及对社会主体的理解，这些主体之间的相互作用，以及这些相互作用对社会整体产生的影响。<ref name="SfSS1">{{cite book|last1=Gilbert |first1=Nigel |last2=Troitzsch |first2=Klaus |year=2005 |title=Simulation for Social Scientists |edition=2 |chapter=Simulation and social science |publisher=Open University Press |chapter-url=http://cress.soc.surrey.ac.uk/s4ss/}}</ref> 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，但当前社会仿真中使用的一些方法起源于物理学和人工智能等领域。 <ref name="Artificial Societies">{{cite book|last1=Epstein |first1=Joshua M. |last2=Axtell |first2=Robert |year=1996 |title=Growing Artificial Societies: Social Science from the Bottom Up |location=Washington DC |publisher=Brookings Institution Press|url=https://archive.org/details/growingartificia00epst|url-access=registration |isbn=978-0262050531 }}</ref><ref name="Cooperation">{{cite book|last1=Axelrod |first1=Robert |year=1997 |title=The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration |location=Princeton, NJ |publisher=Princeton University Press}}</ref>同时这个领域的一些方法也被应用于自然科学的其他领域，例如来自[[社会网络分析]]和[[网络科学]]领域的[[网络中心性]]度量。
在相关文献中，计算社会科学经常与[[社会复杂性]]的研究有关。 [[复杂系统]]、宏观和微观过程之间的非线性相互联系、[[涌现]]等社会复杂性概念已经进入了计算社会科学中。<ref>{{cite journal|last=Casti |first=J |year=1999 |title=The Computer as Laboratory: Toward a Theory of Complex Adaptive Systems |journal=Complexity |volume=4 |issue=5 |pages=12–14 |doi=10.1002/(SICI)1099-0526(199905/06)4:5<12::AID-CPLX3>3.0.CO;2-4}}</ref> 一个实际而著名的例子是以“人工社会”的形式建造一个计算模型，通过它研究人员可以分析一个社会系统的结构。<ref>{{cite journal|last=Goldspink |first=C |year=2002 |title=Methodological Implications of Complex Systems Approaches to Sociality: Simulation as a Foundation for Knowledge |url=http://jasss.soc.surrey.ac.uk/5/1/3.html |publisher=Journal of Artificial Societies and Social Simulation |volume=5 |issue=1}}</ref> <ref name="SfSS1"/><ref name="Generative">{{cite book|last=Epstein |first=Joshua |year=2007 |title=Generative Social Science: Studies in Agent-Based Computational Modeling |location=Princeton, NJ |publisher=Princeton University Press|url=https://www.researchgate.net/publication/283615593}}</ref>
计算社会科学彻底改变了科学方法的两个基本支柱: 实证研究，特别是通过大数据，通过分析社会在线活动留下的数据痕迹; 科学理论，特别是通过社会模拟建立计算机模拟模型。<ref>DT&SC 7-1: [https://www.youtube.com/watch?v=9x3d75ZMuYU . Introduction to e-Science]: From the DT&SC [https://canvas.instructure.com/courses/949415 online course] at the University of California</ref><ref name="HilbertICT4ICT4D">{{cite book|author=Hilbert, M.|year=2015|title=e-Science for Digital Development: ICT4ICT4D|publisher=Centre for Development Informatics, SEED, University of Manchester|url=http://www.seed.manchester.ac.uk/medialibrary/IDPM/working_papers/di/di-wp60.pdf|isbn=978-1-905469-54-3|url-status=dead|archiveurl=https://web.archive.org/web/20150924100018/http://www.seed.manchester.ac.uk/medialibrary/IDPM/working_papers/di/di-wp60.pdf|archivedate=2015-09-24}}</ref>
利用先进的信息技术进行以信息处理为核心的社会调查是一种多学科、综合的方法。包括对社交网络、社交地理系统<ref>{{cite journal|title=Computational social science |first=Claudio |last=Cioffi-Revilla |journal=[[Wiley Interdisciplinary Reviews: Computational Statistics]] |year=2010 |volume=2 |issue=3 |pages=259–271 |doi=10.1002/wics.95}}</ref> 、社交媒体内容和传统媒体内容的分析计算。
== 诞生 ==
计算社会科学的产生并非突然出现，而是在技术发展下催生出来的跨学科研究。如果大致来分，可以分为4个阶段来看计算社会科学的演化进程。
计算社会科学的产生并非突然出现，而是在技术发展下催生出来的跨学科研究。如果大致来分，可以分为4个阶段来看计算社会科学的演化进程。
2007年，“小世界网络之父”奠基人[[邓肯·瓦茨]]在 Nature 发表了题为《A twenty-first century science》的文章，这成为计算社会科学时代即将来临的标志之一。这篇文章采用了网络分析的方法来分析社会现象中的网络偏好以及个体选择的问题。<ref name="Decun">Duncan J. Watts (2007) [https://pattern.swarma.org/paper?id=08fa3492-5bcb-11ea-bd88-0242ac1a0005 A twenty-first century science].nature.445.7127:(489)</ref>
2007年，“小世界网络之父”奠基人[[邓肯·瓦茨]]在 Nature 发表了题为《A twenty-first century science》的文章，这成为计算社会科学时代即将来临的标志之一。这篇文章采用了网络分析的方法来分析社会现象中的网络偏好以及个体选择的问题。<ref name="Decun">Duncan J. Watts (2007) [https://pattern.swarma.org/paper?id=08fa3492-5bcb-11ea-bd88-0242ac1a0005 A twenty-first century science].nature.445.7127:(489)</ref>
=== 计算社会科学的诞生 ===
=== 计算社会科学的诞生 ===
另外，在2012年，R. Conte，C. Cioffi-Revilla等14位欧美学者在《The European Physical Journal Special Topics》（第1期）上联合发布了一份《计算社会科学宣言》（后文简称“宣言”），力图呼唤一场社会科学革命。《宣言》从机遇、技术发展、方法创新、面临的挑战和预期的影响等五个方面全景式的说明了计算社会科学发展现状及其未来的方面。<ref name="Conte">R. Conte,N. Gilbert,G. Bonelli,C. Cioffi-Revilla,G. Deffuant,J. Kertesz,V. Loreto,S. Moat,J. -P. Nadal,A. Sanchez,A. Nowak,A. Flache,M. San Miguel,D. Helbing (2012) [https://pattern.swarma.org/paper?id=8023ca82-5bcc-11ea-bb96-0242ac1a0005 Manifesto of computational social science].the european physical journal special topics.214.1:(325-346)</ref>
另外，在2012年，R. Conte，C. Cioffi-Revilla等14位欧美学者在《The European Physical Journal Special Topics》（第1期）上联合发布了一份《计算社会科学宣言》（后文简称“宣言”），力图呼唤一场社会科学革命。《宣言》从机遇、技术发展、方法创新、面临的挑战和预期的影响等五个方面全景式的说明了计算社会科学发展现状及其未来的方面。<ref name="Conte">R. Conte,N. Gilbert,G. Bonelli,C. Cioffi-Revilla,G. Deffuant,J. Kertesz,V. Loreto,S. Moat,J. -P. Nadal,A. Sanchez,A. Nowak,A. Flache,M. San Miguel,D. Helbing (2012) [https://pattern.swarma.org/paper?id=8023ca82-5bcc-11ea-bb96-0242ac1a0005 Manifesto of computational social science].the european physical journal special topics.214.1:(325-346)</ref>
==研究方法发展历史==
== 各个历史时期 ==
[[File:Complexity-map-with-sociolo.png|thumb|right|300px|Historical map of research paradigms and associated scientists in [[sociology and complexity science]].]]
[[File:Complexity-map-with-sociolo.png|thumb|right|300px|计算社会学历史上各中研究范式及学者]]
本词条改编自相应[https://en.wikipedia.org/wiki/Computational_sociology#Data_mining_and_social_network_analysis 维基百科页面]
本词条改编自相应[https://en.wikipedia.org/wiki/Computational_sociology 维基百科页面]
=== 大背景 ===
=== 总述 ===
计算社会学在过去的四十年里诞生并获得了极大的关注。它最先是用在建模和解释那些从简单的活动中涌现出复杂行为的社会过程。<ref name="EACICS">Salgado, Mauricio, and Nigel Gilbert. "[http://epubs.surrey.ac.uk/749319/1/Emergence%20and%20Communication%20-%20V3%201.pdf Emergence and communication in computational sociology]." Journal for the Theory of Social Behaviour 43.1 (2013): 87-110.</ref>
计算社会学在过去的四十年里诞生并获得了极大的关注。它最先是用在建模和解释那些从简单的活动中涌现出复杂行为的社会过程。<ref name="EACICS">Salgado, Mauricio, and Nigel Gilbert. "[http://epubs.surrey.ac.uk/749319/1/Emergence%20and%20Communication%20-%20V3%201.pdf Emergence and communication in computational sociology]." Journal for the Theory of Social Behaviour 43.1 (2013): 87-110.</ref>
“涌现”背后的思想就是一个大系统表现出来的属性并不一定格式其组成部分的属性。<ref>Macy, Michael W., and Robert Willer. "[http://sct.uab.cat/lsds/sites/sct.uab.cat.lsds/files/FROM%20FACTORS%20TO%20ACTORS%20(Macy%20and%20Willer%202002).pdf From factors to actors: computational sociology and agent-based modeling]." Annual review of sociology 28.1 (2002): 143-166.</ref>
“涌现”背后的思想就是一个大系统表现出来的属性并不一定格式其组成部分的属性。<ref>Macy, Michael W., and Robert Willer. "[http://sct.uab.cat/lsds/sites/sct.uab.cat.lsds/files/FROM%20FACTORS%20TO%20ACTORS%20(Macy%20and%20Willer%202002).pdf From factors to actors: computational sociology and agent-based modeling]." Annual review of sociology 28.1 (2002): 143-166.</ref>
引入涌现思想的人是Alexander, Morgan, 和 Broad，这些都是古典的涌现学家（emergentist），他们在二十世纪初期提出了这个概念和方法，目的是为[https://zh.wikipedia.org/wiki/%E5%90%8C%E4%B8%80%E7%90%86%E8%AE%BA 同一论（reductionist materialism）]和[https://zh.wikipedia.org/wiki/%E4%BA%8C%E5%85%83%E8%AB%96 二元论（dualism）]这两个针锋相对的观念体系寻找一个足够好的平衡。
引入涌现思想的人是Alexander, Morgan, 和 Broad，这些都是古典的涌现学家（emergentist），他们在二十世纪初期提出了这个概念和方法，目的是为[https://zh.wikipedia.org/wiki/%E5%90%8C%E4%B8%80%E7%90%86%E8%AE%BA 同一论（reductionist materialism）]和[https://zh.wikipedia.org/wiki/%E4%BA%8C%E5%85%83%E8%AB%96 二元论（dualism）]这两个针锋相对的观念体系寻找一个足够好的平衡。
尽管[[涌现]]思想在计算社会学的建立中扮演着重要的角色，却也有人不同意这个思想，代表人物就是[https://en.wikipedia.org/wiki/Joshua_M._Epstein 爱泼斯坦（Joshua M. Epstein）]。爱泼斯坦怀疑涌现思想的作用，因为有些方面是无法解释的。他作出了一番反对涌现思想的宣言：“各个部分的生成性自足构成了全部现象的解释（the generative sufficiency of the parts that constitutes the whole's explanation）”<ref name="EACICS"/>
尽管[[涌现]]思想在计算社会学的建立中扮演着重要的角色，却也有人不同意这个思想，代表人物就是[https://en.wikipedia.org/wiki/Joshua_M._Epstein 爱泼斯坦（Joshua M. Epstein）]。爱泼斯坦怀疑涌现思想的作用，因为有些方面是无法解释的。他作出了一番反对涌现思想的宣言：“各个部分的生成性自足构成了全部现象的解释（the generative sufficiency of the parts that constitutes the whole's explanation）”<ref name="EACICS"/>
[[基于主体的建模]]（Agent-based models）对计算社会学有着历史性的意义。这些模型最早出现在20世纪六十年代，用于模拟组织和城市等的控制和反馈机制。在七十年代时，基于主体的建模引入了个体（individual）作为主要的建模单元进行分析，闭关使用自底向上的策略来对行为建模。八十年代时发生的主要改变则是主体们的交互时独立的。<ref name="EACICS"/>
===系统论和功能主义时期===
在战后时期，万尼瓦尔·布希的微分分析器、[[约翰·冯·诺伊曼]] John von Neumann的细胞自动机、[[诺伯特·维纳]]的模控学 与[[克劳德·香农]]的[[信息论]]在技术系统中成为模拟与了解复杂度具有影响力的典范。相对应地，在像是物理学、生物学、电子学，和经济学等学门的科学家开始表述一种一般性的系统理论，其中所有自然与物理现象皆为一个系统中具有相同模式与性质的相关元素的展现。随着艾弥尔·涂尔干以实事求是的方式分析复杂现代社会的呼声<ref>{{cite book|first=Émile |last=Durkheim |title=The Division of Labor in Society |location=New York, NY |publisher=Macmillan}}</ref> ，战后结构功能主义社会学家如塔尔科特·帕森斯利用这些构成元素之间系统化与阶层化互动的理论，来尝试生成宏大而统一的社会学理论，例如四种功能（AGIL paradigm）<ref name="Bailey">{{cite book|first1=Kenneth D. |last1=Bailey |editor=Jonathan H. Turner |chapter=Systems Theory |title=Handbook of Sociological Theory |publisher=Springer Science |year=2006 |location=New York, NY |isbn=978-0-387-32458-6 |pages=379–404}}</ref>。 如George Homans等社会学家辩称社会理论应该被形式化（正规化）（formalized），成为命题和精确术语的阶层结构，其他的命题与假设可以从中被推演出来并操作化以进行实证研究。<ref name="Blackwell">{{cite encyclopedia|year=2007 |title=Computational Sociology |last=Bainbridge |first=William Sims |encyclopedia=Blackwell Encyclopedia of Sociology |publisher=Blackwell Reference Online |url=http://www.sociologyencyclopedia.com/subscriber/tocnode?id=g9781405124331_chunk_g97814051243319_ss1-85 |doi=10.1111/b.9781405124331.2007.x |hdl=10138/224218 |editor=Ritzer, George|isbn=978-1-4051-2433-1|hdl-access=free }}</ref> 由于电脑算法与程式早在1956年就已用来测试和验证数学定理，<ref>{{cite book|last=Crevier |first=D. |year=1993 |title=AI: The Tumultuous History of the Search for Artificial Intelligence |url=https://archive.org/details/aitumultuoushist00crev |url-access=registration |publisher=Basic Books |location=New York, NY}}</ref>例如四色定理，社会科学家与系统动力学家预期类似的计算取径可以类比地“解决”与“证明”正规化的问题，和社会结构与动力的理论。
在战后时期，万尼瓦尔·布希的微分分析器、[[约翰·冯·诺伊曼]] John von Neumann的细胞自动机、[[诺伯特·维纳]]的模控学 与[[香农]]的[[信息论]]在技术系统中成为模拟与了解复杂度具有影响力的典范。相对应地，在像是物理学、生物学、电子学，和经济学等学门的科学家开始表述一种一般性的系统理论，其中所有自然与物理现象皆为一个系统中具有相同模式与性质的相关元素的展现。随着艾弥尔·涂尔干以实事求是的方式分析复杂现代社会的呼声<ref>{{cite book|first=Émile |last=Durkheim |title=The Division of Labor in Society |location=New York, NY |publisher=Macmillan}}</ref> ，战后结构功能主义社会学家如塔尔科特·帕森斯利用这些构成元素之间系统化与阶层化互动的理论，来尝试生成宏大而统一的社会学理论，例如四种功能（AGIL paradigm）<ref name="Bailey">{{cite book|first1=Kenneth D. |last1=Bailey |editor=Jonathan H. Turner |chapter=Systems Theory |title=Handbook of Sociological Theory |publisher=Springer Science |year=2006 |location=New York, NY |isbn=978-0-387-32458-6 |pages=379–404}}</ref>。 如George Homans等社会学家辩称社会理论应该被形式化（正规化）（formalized），成为命题和精确术语的阶层结构，其他的命题与假设可以从中被推演出来并操作化以进行实证研究。<ref name="Blackwell">{{cite encyclopedia|year=2007 |title=Computational Sociology |last=Bainbridge |first=William Sims |encyclopedia=Blackwell Encyclopedia of Sociology |publisher=Blackwell Reference Online |url=http://www.sociologyencyclopedia.com/subscriber/tocnode?id=g9781405124331_chunk_g97814051243319_ss1-85 |doi=10.1111/b.9781405124331.2007.x |hdl=10138/224218 |editor=Ritzer, George|isbn=978-1-4051-2433-1|hdl-access=free }}</ref> 由于电脑算法与程式早在1956年就已用来测试和验证数学定理，<ref>{{cite book|last=Crevier |first=D. |year=1993 |title=AI: The Tumultuous History of the Search for Artificial Intelligence |url=https://archive.org/details/aitumultuoushist00crev |url-access=registration |publisher=Basic Books |location=New York, NY}}</ref>例如四色定理，社会科学家与系统动力学家预期类似的计算取径可以类比地“解决”与“证明”正规化的问题，和社会结构与动力的理论。
===宏观模拟与微观模拟===
===宏观模拟与微观模拟===
然而，这些微观模拟模型并未允许个体进行互动或适应，其目的也非基本理论研究<ref name="MW">{{cite journal|doi=10.1146/annurev.soc.28.110601.141117|title=From Factors to Actors: Computational Sociology and Agent-Based Modeling |first1=Michael W. |last1=Macy |first2=Robert |last2=Willer |journal=Annual Review of Sociology |volume=28 |pages=143–166 |jstor=3069238|year=2002}}</ref>。
然而，这些微观模拟模型并未允许个体进行互动或适应，其目的也非基本理论研究<ref name="MW">{{cite journal|doi=10.1146/annurev.soc.28.110601.141117|title=From Factors to Actors: Computational Sociology and Agent-Based Modeling |first1=Michael W. |last1=Macy |first2=Robert |last2=Willer |journal=Annual Review of Sociology |volume=28 |pages=143–166 |jstor=3069238|year=2002}}</ref>。
===[[元胞自动机]]与基于主体的建模===
===[[元胞自动机]]与基于主体的建模时期===
20世纪七十到八十年代，数学家和物理学家尝试建模和分析怎样从简单的单元，比如原子中，产生全局现象，比如复杂材料才低温、磁场、和[[湍流]]中的属性。<ref>{{cite book|title=Cellular automata machines: a new environment for modeling |url=https://archive.org/details/cellularautomata00toff |url-access=registration |first1=Tommaso |last1=Toffoli |first2=Norman |last2=Margolus | author2-link = Norman Margolus |year=1987 |publisher=MIT Press |location=Cambridge, MA}}</ref>
20世纪七十到八十年代，数学家和物理学家尝试建模和分析怎样从简单的单元，比如原子中，产生全局现象，比如复杂材料才低温、磁场、和[[湍流]]中的属性。<ref>{{cite book|title=Cellular automata machines: a new environment for modeling |url=https://archive.org/details/cellularautomata00toff |url-access=registration |first1=Tommaso |last1=Toffoli |first2=Norman |last2=Margolus | author2-link = Norman Margolus |year=1987 |publisher=MIT Press |location=Cambridge, MA}}</ref>
科学家们使用[[元胞自动机]]（Celluer Automata），设定了一个只由方格组成的系统，每个方格就是一个“元胞（cell）”。每个元胞只能有有限个状态，元胞在各个状态间的转换条件只由紧贴着该元胞的周围元胞状态决定。元胞自动机与人工智能技术和微型计算机的所获得的进步一同为[[混沌理论]]和[[复杂系统]]等研究领域的建立做出重大贡献，同时也重新唤起了人们在理解交叉学科的复杂物理和社会系统的兴趣。众多致力于研究复杂科学的科研组织也是建立于这个时候：[[圣塔菲研究所]]由一群来自[https://www.lanl.gov/ 洛斯阿拉莫斯国家实验室]的物理学家在1984年发起，密歇根大学的[https://lsa.umich.edu/cscs/about-us/history.html BACH小组]也是在八十年代中期成立的。
科学家们使用[[元胞自动机]]（Celluer Automata），设定了一个只由方格组成的系统，每个方格就是一个“元胞（cell）”。每个元胞只能有有限个状态，元胞在各个状态间的转换条件只由紧贴着该元胞的周围元胞状态决定。元胞自动机与人工智能技术和微型计算机的所获得的进步一同为[[混沌理论]]和[[复杂系统]]等研究领域的建立做出重大贡献，同时也重新唤起了人们在理解交叉学科的复杂物理和社会系统的兴趣。众多致力于研究复杂科学的科研组织也是建立于这个时候：[[圣塔菲研究所]]由一群来自[https://www.lanl.gov/ 洛斯阿拉莫斯国家实验室]的物理学家在1984年发起，密歇根大学的[https://lsa.umich.edu/cscs/about-us/history.html BACH小组]也是在八十年代中期成立的。
这一轮元胞自动机的研究范式催生了使用[[基于主体建模]]（Agent-based Modeling）的第三次社会模拟浪潮。和宏观模拟类似，这些模型强调了自底向上的设计思想，但采用了四个不同于宏观建模的假设：自主（autonomy）、独立（interdependency）、简单规则（simple rules）、和适应性行为（adaptive behavior）。<ref name="MW"/> 相比于预测的准确度，基于主体的建模更加强调理论的建立。<ref>{{cite journal |title=A simulation of the structure of academic science |journal=Sociological Research Online |volume=2 |issue=2 |pages=1–15 |year=1997 |first=Nigel |last=Gilbert |url=http://www.socresonline.org.uk/socresonline/2/2/3.html |doi=10.5153/sro.85 |access-date=2009-12-16 |archive-url=https://web.archive.org/web/19980524062306/http://www.socresonline.org.uk/socresonline/2/2/3.html |archive-date=1998-05-24 |url-status=dead }}</ref>
这一轮元胞自动机的研究范式催生了使用[[基于主体的建模]]（Agent-based Modeling）的第三次社会模拟浪潮。和宏观模拟类似，这些模型强调了自底向上的设计思想，但采用了四个不同于宏观建模的假设：自主（autonomy）、独立（interdependency）、简单规则（simple rules）、和适应性行为（adaptive behavior）。<ref name="MW"/> 相比于预测的准确度，基于主体的建模更加强调理论的建立。<ref>{{cite journal |title=A simulation of the structure of academic science |journal=Sociological Research Online |volume=2 |issue=2 |pages=1–15 |year=1997 |first=Nigel |last=Gilbert |url=http://www.socresonline.org.uk/socresonline/2/2/3.html |doi=10.5153/sro.85 |access-date=2009-12-16 |archive-url=https://web.archive.org/web/19980524062306/http://www.socresonline.org.uk/socresonline/2/2/3.html |archive-date=1998-05-24 |url-status=dead }}</ref>
在1981年，数学家与政治学家[[罗伯特·阿克塞尔罗德]]与演化生物学家[https://zh.wikipedia.org/wiki/%E5%A8%81%E5%BB%89%C2%B7%E5%94%90%E7%B4%8D%C2%B7%E6%BC%A2%E5%BD%8C%E7%88%BE%E9%A0%93 威廉·汉密尔顿]一同在《Science》杂志上发表了一篇名为《合作的进化（The Evolution of Cooperation）》的经典论文，其中使用了基于主体的建模来展示了在囚徒困境的博弈中，当主体们（agents）只遵循简单的、自利的规则时，也可以在互惠的原则上建立稳定的社会合作。<ref name="Cooperation">{{cite journal|title=The Evolution of Cooperation |first1=Robert |last1=Axelrod |first2=William D. |last2=Hamilton |journal=Science |volume=211 |issue=4489 |pages=1390–1396 |doi=10.1126/science.7466396|pmid=7466396 |date=March 27, 1981|bibcode=1981Sci...211.1390A }}</ref>
在1981年，数学家与政治学家[[罗伯特·阿克塞尔罗德]]与演化生物学家[https://zh.wikipedia.org/wiki/%E5%A8%81%E5%BB%89%C2%B7%E5%94%90%E7%B4%8D%C2%B7%E6%BC%A2%E5%BD%8C%E7%88%BE%E9%A0%93 威廉·汉密尔顿]一同在《Science》杂志上发表了一篇名为《合作的进化（The Evolution of Cooperation）》的经典论文，其中使用了基于主体的建模来展示了在囚徒困境的博弈中，当主体们（agents）只遵循简单的、自利的规则时，也可以在互惠的原则上建立稳定的社会合作。<ref name="Cooperation">{{cite journal|title=The Evolution of Cooperation |first1=Robert |last1=Axelrod |first2=William D. |last2=Hamilton |journal=Science |volume=211 |issue=4489 |pages=1390–1396 |doi=10.1126/science.7466396|pmid=7466396 |date=March 27, 1981|bibcode=1981Sci...211.1390A }}</ref>
阿克塞尔罗德和汉密尔顿展示了每个主体只要遵循（1）第一轮时选择合作（2）下一轮重复上一轮对方的做法这两条简单规则，就可以在没有社会权威的情况下建立起合作与惩罚的规范。<ref name="Cooperation"/> 九十年代学者们如William Sims Bainbridge, Kathleen Carley, Michael Macy,和John Skvoretz建立起了广义互惠、偏见、社会影响和组织信息处理等主题的基于主体的模型。在1999年，Nigel Gilbert发表了第一本关于社会模拟的教科书《Simulation for the social scientist》，并创立了与其相关的期刊《Journal of Artificial Societies and Social Simulation》。
阿克塞尔罗德和汉密尔顿展示了每个主体只要遵循（1）第一轮时选择合作（2）下一轮重复上一轮对方的做法这两条简单规则，就可以在没有社会权威的情况下建立起合作与惩罚的规范。<ref name="Cooperation"/> 九十年代学者们如William Sims Bainbridge, Kathleen Carley, Michael Macy,和John Skvoretz建立起了广义互惠、偏见、社会影响和组织信息处理等主题的基于主体的模型。在1999年，Nigel Gilbert发表了第一本关于社会模拟的教科书《Simulation for the social scientist》，并创立了与其相关的期刊《Journal of Artificial Societies and Social Simulation》。
===数据挖掘与社交网络分析时期===
===数据挖掘与社交网络分析===
和其他社会系统计算模型的发展轨迹不同，社交网络分析（Social Network Analysis）诞生自20世纪七十到八十年代，是图论、统计学和社会结构研究等科研进展所催生出来的分析方法，被许多社会学家，如 James Samuel Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman等采用。<ref>{{cite book|title=The Development of Social Network Analysis: A Study in the Sociology of Science |first=Linton C. |last=Freeman |publisher=Empirical Press |location=Vancouver, BC |year=2004}}</ref>
和其他社会系统计算模型的发展轨迹不同，社交网络分析（Social Network Analysis）诞生自20世纪七十到八十年代，是图论、统计学和社会结构研究等科研进展所催生出来的分析方法，被许多社会学家，如 James Samuel Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman等采用。<ref>{{cite book|title=The Development of Social Network Analysis: A Study in the Sociology of Science |first=Linton C. |last=Freeman |publisher=Empirical Press |location=Vancouver, BC |year=2004}}</ref>
八十到九十年代计算和通信技术的持续普及呼唤着[[网络科学]]，[[多层次建模]]等可以适用于越来越复杂和大体量数据集的分析技术。最近的计算社会学浪潮并没有使用计算机模拟，而是使用了网络分析和高级统计技术对计算机数据库里的行为数据做分析。电子邮件、即时通信消息、万维网上的超链接、手机使用数据、新闻组内的讨论内容等电子记录让社会学家们得以在多时间点多个层面上直接观察和分析社会行为，避免了访谈、参与观察等传统实证方法（traditional empirical methods）的约束。<ref>{{cite journal|title=Life in the network: the coming age of computational social science|first9=J|last10=Gutmann|first10=M.|last11=Jebara|first11=T.|last12=King|first12=G.|last13=Macy|first13=M.|last14=Roy|first14=D.|last15=Van Alstyne|first15=M.|last9=Fowler|first8=N|last8=Contractor|first7=N|last7=Christakis|first6=D|last6=Brewer|first5=AL|last5=Barabasi|first4=S |journal=Science|last4=Aral |date=February 6, 2009|first3=L |volume=323|pmid=19197046 |issue=5915|last3=Adamic |pages=721–723|pmc=2745217 |doi=10.1126/science.1167742 |first1=David |last1=Lazer |first2=Alex |last2=Pentland |display-authors=8}}</ref>
八十到九十年代计算和通信技术的持续普及呼唤着[[网络科学]]，[[多层次建模]]等可以适用于越来越复杂和大体量数据集的分析技术。最近的计算社会学浪潮并没有使用计算机模拟，而是使用了网络分析和高级统计技术对计算机数据库里的行为数据做分析。电子邮件、即时通信消息、万维网上的超链接、手机使用数据、新闻组内的讨论内容等电子记录让社会学家们得以在多时间点多个层面上直接观察和分析社会行为，避免了访谈、参与观察等传统实证方法（traditional empirical methods）的约束。<ref>{{cite journal|title=Life in the network: the coming age of computational social science|first9=J|last10=Gutmann|first10=M.|last11=Jebara|first11=T.|last12=King|first12=G.|last13=Macy|first13=M.|last14=Roy|first14=D.|last15=Van Alstyne|first15=M.|last9=Fowler|first8=N|last8=Contractor|first7=N|last7=Christakis|first6=D|last6=Brewer|first5=AL|last5=Barabasi|first4=S |journal=Science|last4=Aral |date=February 6, 2009|first3=L |volume=323|pmid=19197046 |issue=5915|last3=Adamic |pages=721–723|pmc=2745217 |doi=10.1126/science.1167742 |first1=David |last1=Lazer |first2=Alex |last2=Pentland |display-authors=8}}</ref>
[[机器学习]]算法的持续进步则更进一步允许社会学家和企业发现大规模数据集中隐藏的社会交互和演化的模式。
[[机器学习]]算法的持续进步则更进一步允许社会学家和企业发现大规模数据集中隐藏的社会交互和演化的模式。
<ref>{{cite journal|first1=Jaideep |last1=Srivastava |first2=Robert |last2=Cooley |first3=Mukund |last3=Deshpande |first4=Pang-Ning |last4=Tan |journal=Proceedings of the ACM Conference on Knowledge Discovery and Data Mining |title=Web usage mining: discovery and applications of usage patterns from Web data|volume=1 |year=2000 |pages=12–23 |doi=10.1145/846183.846188|issue=2}}</ref><ref>{{cite journal|doi=10.1016/S0169-7552(98)00110-X|title=The anatomy of a large-scale hypertextual Web search engine |first1=Sergey |last1=Brin |first2=Lawrence |last2=Page |journal=Computer Networks and ISDN Systems |volume=30 |issue=1–7 |pages=107–117 |date=April 1998|citeseerx=10.1.1.115.5930 }}</ref>
<ref>{{cite journal|first1=Jaideep |last1=Srivastava |first2=Robert |last2=Cooley |first3=Mukund |last3=Deshpande |first4=Pang-Ning |last4=Tan |journal=Proceedings of the ACM Conference on Knowledge Discovery and Data Mining |title=Web usage mining: discovery and applications of usage patterns from Web data|volume=1 |year=2000 |pages=12–23 |doi=10.1145/846183.846188|issue=2}}</ref><ref>{{cite journal|doi=10.1016/S0169-7552(98)00110-X|title=The anatomy of a large-scale hypertextual Web search engine |first1=Sergey |last1=Brin |first2=Lawrence |last2=Page |journal=Computer Networks and ISDN Systems |volume=30 |issue=1–7 |pages=107–117 |date=April 1998|citeseerx=10.1.1.115.5930 }}</ref>
[[File:Tripletsnew2012.png|thumb|right|Narrative network of US Elections 2012<ref name="ReferenceA">{{cite journal|title=Automated analysis of the US presidential elections using Big Data and network analysis|author1=S Sudhahar|author2=GA Veltri|author3=N Cristianini|journal=Big Data & Society|volume=2|issue=1|pages=1–28|year=2015|doi=10.1177/2053951715572916|doi-access=free}}</ref>]]
[[File:Tripletsnew2012.png|thumb|right|Narrative network of US Elections 2012<ref name="ReferenceA">{{cite journal|title=Automated analysis of the US presidential elections using Big Data and network analysis|author1=S Sudhahar|author2=GA Veltri|author3=N Cristianini|journal=Big Data & Society|volume=2|issue=1|pages=1–28|year=2015|doi=10.1177/2053951715572916|doi-access=free}}</ref>]]
语料库自动解析技术可以大规模地抽取文本中的实体，以及实体间的关系，以将文本形式数据转化成网络形式数据。生成的网络可以包含成千上万个节点，随后应用网络理论等工具加以分析，即可发现关键结点、重点社群等，以及更加广泛的网络属性，比如健壮性和结构稳定性，或者结构洞等。<ref>{{cite journal|title=Network analysis of narrative content in large corpora|author1=S Sudhahar|author2=G De Fazio|author3=R Franzosi|author4=N Cristianini|journal=Natural Language Engineering|volume=21|issue=1|pages=1–32|year=2013|doi=10.1017/S1351324913000247 |url=https://research-information.bristol.ac.uk/files/129621186/Network_Analysis_of_Narrative_Content_in_Large_Corpora.pdf}}</ref>如此，我们可以自动执行定量叙事分析（quantitative narrative analysis）中的技术，<ref>{{cite book|title=Quantitative Narrative Analysis|last=Franzosi|first=Roberto|publisher=Emory University|year=2010}}</ref>识别“主语-谓语-宾语”这样的三元组或者“主语-宾语”这样的二元组。<ref name="ReferenceA"/>
语料库自动解析技术可以大规模地抽取文本中的实体，以及实体间的关系，以将文本形式数据转化成网络形式数据。生成的网络可以包含成千上万个节点，随后应用网络理论等工具加以分析，即可发现关键结点、重点社群等，以及更加广泛的网络属性，比如健壮性和结构稳定性，或者结构洞等。<ref>{{cite journal|title=Network analysis of narrative content in large corpora|author1=S Sudhahar|author2=G De Fazio|author3=R Franzosi|author4=N Cristianini|journal=Natural Language Engineering|volume=21|issue=1|pages=1–32|year=2013|doi=10.1017/S1351324913000247 |url=https://research-information.bristol.ac.uk/files/129621186/Network_Analysis_of_Narrative_Content_in_Large_Corpora.pdf}}</ref>如此，我们可以自动执行定量叙事分析（quantitative narrative analysis）中的技术，<ref>{{cite book|title=Quantitative Narrative Analysis|last=Franzosi|first=Roberto|publisher=Emory University|year=2010}}</ref>识别“主语-谓语-宾语”这样的三元组或者“主语-宾语”这样的二元组。<ref name="ReferenceA"/>
===计算内容分析===
===计算内容分析时期===
内容分析（content analysis）一直以来都是社会科学和媒体研究的传统组成部分。内容分析的自动化通过研究社交媒体和报刊杂志上数百万计的新闻内容，使得“大数据革命”惠及社会科学。性别偏向、可读性、内容相似度、读者偏好、甚至情绪等都文本挖掘方法在数百万文档里研究过了。<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>
内容分析（content analysis）一直以来都是社会科学和媒体研究的传统组成部分。内容分析的自动化通过研究社交媒体和报刊杂志上数百万计的新闻内容，使得“大数据革命”惠及社会科学。性别偏向、可读性、内容相似度、读者偏好、甚至情绪等都文本挖掘方法在数百万文档里研究过了。<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>
</ref>
</ref>
==主流研究方法==
==当前主流研究方法==
就像伽利略利用望远镜作为关键的观察工具最终获得对物质世界更深刻、更真实的理解一样，计算社会科学家正在学习利用先进和日益强大的计算工具来超越传统的学科。<ref>Claudio Cioffi‐Revilla (2010) [https://test.pattern.swarma.org/paper?id=62b3c07e-5bcc-11ea-9bda-0242ac1a0005 Computational social science].</ref>
就像伽利略利用望远镜作为关键的观察工具最终获得对物质世界更深刻、更真实的理解一样，计算社会科学家正在学习利用先进和日益强大的计算工具来超越传统的学科。<ref>Claudio Cioffi‐Revilla (2010) [https://test.pattern.swarma.org/paper?id=62b3c07e-5bcc-11ea-9bda-0242ac1a0005 Computational social science].</ref>
将人或者社区看作一个点，用边表示人和人之间或者社区和社区之间可能存在的相互依赖关系，这样就可以构成一个社会网络，利用网络科学的方法对社会网络进行分析，挖掘出背后的逻辑，就是社交网络分析。联盟、恐怖组织、贸易体系、认知信仰体系和国家社会体系本身都是常见的社会网络，是社会科学家们感兴趣的研究对象。
将人或者社区看作一个点，用边表示人和人之间或者社区和社区之间可能存在的相互依赖关系，这样就可以构成一个社会网络，利用网络科学的方法对社会网络进行分析，挖掘出背后的逻辑，就是社交网络分析。联盟、恐怖组织、贸易体系、认知信仰体系和国家社会体系本身都是常见的社会网络，是社会科学家们感兴趣的研究对象。
=== 3.地理空间分析 ===
=== 3.地理空间分析（又被称为社会地理信息系统、地理信息系统、社会GIS] ===
地理信息系统（GIS），又被称为社会地理信息系统、地理信息系统、社会GIS，最初是社会地理学家和制图员研究地理现象的可视化工具和空间分析的工具。目前在社会科学中有了许多应用，比如在犯罪学和区域经济学应用社会GIS可以有效的量化冲突，与其他的量化技术结合在一起可以产生一些使用数学和统计模型无法获得的有趣的见解。
=== 4.复杂系统建模 ===
=== 4.复杂系统建模 ===
复杂系统建模是指采用复杂系统的基本方法，比如神经网络建模、基于主体的建模方法、遗传算法、粒子群优化算法、蚁群优化算法应用在社会科学网络中，为社会科学中的非均衡系统的动态分析提供了理论支持。如恐怖袭击、发展中国家的财富和贫困，政治不稳定，外国援助分布和国内和国际冲突等。
复杂系统建模是指采用复杂系统的基本方法，比如神经网络建模、基于主体的建模方法、遗传算法、粒子群优化算法、蚁群优化算法应用在社会科学网络中，为社会科学中的非均衡系统的动态分析提供了理论支持。如恐怖袭击、发展中国家的财富和贫困，政治不稳定，外国援助分布和国内和国际冲突等。
=== 5.社会仿真模型===
=== 5.社会仿真模型===
因为很多社会事件是无法在系统上进行实验的， 所以采用仿真模拟的办法来对研究分析某一特定的系统和策略，从而达到分析社会现象的办法，成为社会仿真模型。
因为很多社会事件是无法在系统上进行实验的， 所以采用仿真模拟的办法来对研究分析某一特定的系统和策略，从而达到分析社会现象的办法，成为社会仿真模型。
同样的，每个方法下面也被系统的划分为多个模型，例如计算社会模拟模型包括系统动力学，微观分析模型，排队模型，细胞自动机，多智能体模型，学习和演化模型，包括一些混合动力，例如，结合系统动力学和代理模型（Agent Based Models）。另外，这五种方法之间的几种组合也很常见，如在由反弹道导弹模拟时引入表达社会复杂性的幂律分布模型。
同样的，每个方法下面也被系统的划分为多个模型，例如计算社会模拟模型包括系统动力学，微观分析模型，排队模型，细胞自动机，多智能体模型，学习和演化模型，包括一些混合动力，例如，结合系统动力学和代理模型（Agent Based Models）。另外，这五种方法之间的几种组合也很常见，如在由反弹道导弹模拟时引入表达社会复杂性的幂律分布模型。
这部分的详细内容可以看社会复杂性中心，Krasnow先进研究所，美国乔治梅森大学教授Claudio Cioffi-Revilla 出版的[https://onlinelibrary.wiley.com/doi/full/10.1002/wics.95#accessDenialLayout Introduction to Computational Social Science: Principles and Applications]》，现在也有了中文的翻译版本，《计算社会科学原则与应用》。
这部分的详细内容可以看社会复杂性中心，Krasnow先进研究所，美国乔治梅森大学教授Claudio Cioffi-Revilla 出版的[https://onlinelibrary.wiley.com/doi/full/10.1002/wics.95#accessDenialLayout 《Introduction to Computational Social Science: Principles and Applications]》，现在也有了中文的翻译版本，《计算社会科学原则与应用》。
==挑战==
==挑战==
计算社会科学，和其他研究领域一样，面临着一系列的挑战。<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> 这些挑战需要得到有意义的处理，以便对社会产生最大的影响。
=== 层级及其相互作用 ===
=== 层级及其相互作用 ===
每个社会形成后往往处于一个层级，并且在层级之间存在着相互作用。 层级并不仅仅是指微观层面或者宏观层面，可以存在一些中间层次，比如群体、网络、社区等等。<ref name="MCSS" /> 然而，问题是如何确定这些层级以及这些层级是如何产生的？ 一旦层级存在，它们如何在内部，或与其他层级产生相互作用？
如果我们把实体(主体)看作节点，把它们之间的连接看作连边，就可以看到网络的形成。这些网络中的连接并非基于实体之间的客观存在的连线，而是由这些实体选择的因素决定的。 <ref>{{cite journal|last1=Egu´ıluz|first1=V. M.|last2=Zimmermann|first2=M. G.|last3=Cela-Conde|first3=C. J.|last4=San Miguel|first4=M.|title=American Journal of Sociology|issue=2005|pages=110, 977}}</ref>这个过程的挑战在于，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，异质性实体之间也能形成强大而有意义的网络。<ref>{{cite journal|last1=Sichman|first1=J. S.|last2=Conte|first2=R.|title=Computational & Mathematical Organization Theory|issue=2002|pages=8(2)}}</ref><ref>{{cite journal|last1=Ehrhardt|first1=G.|last2=Marsili|first2=M.|last3=Vega-Redondo|first3=F.|title=Physical Review E|issue=2006|pages=74(3)}}</ref>
正如前面所讨论的，社会中有层级。而这一层级，即微观层面的个体层级中，微观-宏观之间的连接<ref>Billari, Francesco C. [https://books.google.com/books?hl=en&lr=&id=Tc8iL0b-OycC&oi=fnd&pg=PA1&dq=%22Agent-based+computational+modelling:+applications+in+demography,+social,+economic+and+environmental+sciences%22&ots=6MNVK7JP-e&sig=-o_iS9zCK3r-t1eHn3554UVadUs Agent-based computational modelling: applications in demography, social, economic and environmental sciences]. Taylor & Francis, 2006.</ref> 指的是可以产生更高的层级那种相互作用。 针对这样微观-宏观之间的连接有一系列的问题需要回答：它们是如何形成的？它们什么时候会收敛？传递到较低的层级的反馈是什么，以及它们是如何被传递的？
另一个主要挑战是验证信息的有效性及其追溯信息的来源。 近年来，信息收集和处理蓬勃发展，然而，人们很少关注社会中虚假信息的传播。追溯信息来源并找到信息的所有者是很困难的事情。
=== 文化建模 ===
=== 文化建模 ===
在社会中，网络和层级的演化会带来文化的多样性。<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> 因此，一个自然而然的想法是，当人们倾向于交流，变得更能接受其他文化和信仰时，为什么多样性仍然存在？ 为什么没有趋同？针对这个问题的一个主要挑战就是如何对多样性建模。 比如是否存在诸如大众传媒、社会地域属性等外部因素会影响文化多样性的演化或持续？
=== 实验和评估 ===
=== 实验和评估 ===
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。计算社会科学能够处理大规模的数据，但是随着规模的增长挑战也越来越大：如何设计一个大规模的信息模拟？即使提出了一个大规模的模拟，如何评估这一大规模的模拟？
=== 模型选择和模型复杂度 ===
=== 模型选择和模型复杂度 ===
另一个挑战是确定能拟合数据数据，而复杂度又合适的模型。这些模型将帮助我们预测随着时间的推移社会将如何演化，并为社会运转的机制提供可能的解释。 <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>
==== 生成式模型 ====
生成式模型帮助我们以一种受控的方式进行广泛的定性分析。爱泼斯坦提出了一个基于主体的模拟，该模型通过识别一组初始的异质实体(主体) ，并根据简单的局部规则观察它们的演化和增长。 <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>
但是这些局部的规则是什么呢？ 如何在一组异质的主体中识别它们？ 这些规则的评估和影响提出是一系列新的难点。
但是这些局部的规则是什么呢？ 如何在一组异质的主体中识别它们？ 这些规则的评估和影响提出是一系列新的难点。
==== 异质或集成模型 ====
====Heterogeneous or ensemble models====
[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）Heterogeneous or ensemble models 不确定这个翻译对不对[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）
[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）Heterogeneous or ensemble models 不确定这个翻译对不对[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）
将那些在单个任务中表现更好的简单模型集成而形成一个混合模型是一种值得探索的研究方法。 这些模型可以提供更好的预测效果并且增强对数据的理解。 然而这里需要在效果和可解释性之间做权衡，当需要提出一个组合的、性能良好的模型时，也需要识别并深入理解这些简单模型之间的相互作用。此外，开发出用来帮助分析和可视化这些混合模型的数据的工具和应用程序又将是另一个额外的挑战。
== 数据库 ==
== 数据库 ==
计算社会科学日益依赖大型的资料库，目前正由几个跨领域计划或维护中的资料库：
计算社会科学日益依赖大型的资料库，目前正由几个跨领域计划或维护中的资料库：
* Seshat：全球历史的资料库，内容系统性的收集了关于内容群体政治社会组织的资讯，以及社会如何演化等。Seshat隶属于演化研究所，演化研究所为非营利智库，目标为“利用演化科学来解决现实世界问题”。
* Seshat：全球历史的资料库，内容系统性的收集了关于内容群体政治社会组织的资讯，以及社会如何演化等。Seshat隶属于演化研究所，演化研究所为非营利智库，目标为“利用演化科学来解决现实世界问题”。
* [https://web.archive.org/web/20160818082017/https://d-place.org/home D-PLACE]：地方、语言、文化和环境资料库，提供超过1400个人类社会形态的资料[5]。
* [https://web.archive.org/web/20160818082017/https://d-place.org/home D-PLACE]：地方、语言、文化和环境资料库，提供超过1400个人类社会形态的资料<ref>{{Cite journal|title=D-PLACE: A Global Database of Cultural, Linguistic and Environmental Diversity|first=Kathryn R.|last2=Gray|first2=Russell D.|date=2016|journal=PLoS ONE|issue=7|volume=11|last3=Greenhill|first3=Simon J.|last4=Jordan|first4=Fiona M.|last5=Gomes-Ng|first5=Stephanie|last6=Bibiko|first6=Hans-Jörg|last7=Blasi|first7=Damián E.|last8=Botero|first8=Carlos A.|last9=Bowern|first9=Claire}}Authors list列表中的<code style="color:inherit; border:inherit; padding:inherit;">|first1=</code>缺少<code style="color:inherit; border:inherit; padding:inherit;">|last1=</code> ([[Help:引文格式1错误#first missing last|帮助]])
* 文化演化图集：是由彼得·百富勤（Peter N. Peregrie）所建立的考古资料库[6]。
[[Category:引文格式1错误：缺少作者或编者]]</ref>。
* 文化演化图集：是由彼得·百富勤（Peter N. Peregrie）所建立的考古资料库<ref>Peter N. Peregrine, ''Atlas of Cultural Evolution'', ''World Cultures'' 14(1), 2003</ref>。
* CHIA：即历史分析的协作资讯（Collaborative Information for Historical Analysis），是由匹兹堡大学主持的多学科合作项目，旨在将历资讯资讯建档，将数据与全球各地的研究机构连结起来。
* CHIA：即历史分析的协作资讯（Collaborative Information for Historical Analysis），是由匹兹堡大学主持的多学科合作项目，旨在将历资讯资讯建档，将数据与全球各地的研究机构连结起来。
* 国际社会历史研究所（International Institute of Social History）：收集关于劳动关系，工人和劳动的全球社会历史的资料。
* 国际社会历史研究所（International Institute of Social History）：收集关于劳动关系，工人和劳动的全球社会历史的资料。
* Human Relations Area Files eHRAF Archaeology[7]。
* Human Relations Area Files eHRAF Archaeology<ref>{{Cite web|url=http://www.yale.edu/hraf/archaeology.htm|title=eHRAF Archaeology|publisher=Human Relations Area Files}}</ref>。
* Human Relations Area Files eHRAF World Cultures[8]。
* Human Relations Area Files eHRAF World Cultures<ref>{{Cite web|url=http://ehrafworldcultures.yale.edu/ehrafe/|title=eHRAF World Cultures|publisher=Human Relations Area Files}}</ref>。
* [https://www.clio-infra.eu/ Clio-Infra]：从公元前1800年到现在的全球社会样本的经济绩效和社会福利其他方面的资料库。
* [https://www.clio-infra.eu/ Clio-Infra]：从公元前1800年到现在的全球社会样本的经济绩效和社会福利其他方面的资料库。
* 对大量历史报纸内容的分析率先显示了如何自动发现周期性结构[9][10] ，对社群媒体进行类似的分析，也能看到明显的周期性结构[11]。
* 对大量历史报纸内容的分析率先显示了如何自动发现周期性结构<ref>{{Cite journal|title=Content analysis of 150 years of British periodicals|url=http://www.pnas.org/content/early/2017/01/03/1606380114|last=Lansdall-Welfare|first=Thomas|last2=Sudhahar|first2=Saatviga|date=2017-01-09|journal=Proceedings of the National Academy of Sciences|doi=10.1073/pnas.1606380114|pages=201606380|language=en|issn=0027-8424|pmid=28069962|last3=Thompson|first3=James|last4=Lewis|first4=Justin|last5=Team|first5=FindMyPast Newspaper|last6=Cristianini|first6=Nello}}</ref><ref>{{Cite journal|title=Discovering Periodic Patterns in Historical News|url=http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0165736|last=Dzogang|first=Fabon|last2=Lansdall-Welfare|first2=Thomas|date=2016-11-08|journal=PLOS ONE|issue=11|doi=10.1371/journal.pone.0165736|volume=11|pages=e0165736|issn=1932-6203|pmc=5100883|pmid=27824911|last3=Team|first3=FindMyPast Newspaper|last4=Cristianini|first4=Nello}}</ref> ，对社群媒体进行类似的分析，也能看到明显的周期性结构<ref>Seasonal Fluctuations in Collective Mood Revealed by Wikipedia Searches and Twitter Posts F Dzogang, T Lansdall-Welfare, N Cristianini - 2016 IEEE International Conference on Data Mining, Workshop on ''Data Mining'' in Human Activity Analysis</ref>。
==Impact==
==影响==
==影响==
计算社会学将给科学、技术和社会带来诸多影响。<ref name="MCSS" />
=== 对科学的影响 ===
=== 对科学的影响 ===
为了使计算社会科学的研究卓有成效，必须用到一些有价值的创新。 这些创新可以是新的数据分析工具、更好的模型和算法。 这种创新的出现对整个科学界来说都会带来很多的好处。
=== 对社会的影响 ===
=== 对社会的影响 ===
计算社会科学的主要挑战之一是社会过程的建模。 各种法律和政策制定者将能够有效且高效的发布新指导方针，广大群众将能够评价和充分理解摆在他们面前的各种备选方案，从而实现公开和平衡的决策进程。
计算社会科学的主要挑战之一是社会过程的建模。 若能解决这一挑战，各种法律和政策制定者将能够有效且高效的发布新指导方针，广大群众将能够评价和充分理解摆在他们面前的各种备选方案，从而实现公开和平衡的决策进程。
== 期刊及学术刊物 ==
== 期刊及学术刊物 ==
这个学科最相关的期刊是《Journal of Artificial Societies and Social Simulation》。
* [https://web.archive.org/web/20100611121626/http://www.ccsr.uiuc.edu/web/Journals/Journals.html 复杂性科学研究期刊列表], from UIUC, IL
* [https://web.archive.org/web/20100611121626/http://www.ccsr.uiuc.edu/web/Journals/Journals.html 复杂性科学研究期刊列表], from UIUC, IL
* [https://web.archive.org/web/20100611091342/http://www.ccsr.uiuc.edu/web/Groups/Groups.html 相关研究组列表], from UIUC, IL
* [https://web.archive.org/web/20100611091342/http://www.ccsr.uiuc.edu/web/Groups/Groups.html 相关研究组列表], from UIUC, IL
== 协会，会议以及工作坊 ==
== 协会，会议以及工作坊 ==
*[http://www.casos.cs.cmu.edu/naacsos/ 北美计算社会和组织科学协会 ''North American Association for Computational Social and Organization Sciences'']
*[http://www.casos.cs.cmu.edu/naacsos/ 北美计算社会和组织科学协会 ''North American Association for Computational Social and Organization Sciences'']
*[http://www.essa.eu.org/ 欧洲社会仿真协会 ''ESSA: European Social Simulation Association'']
*[http://www.essa.eu.org/ 欧洲社会仿真协会 ''ESSA: European Social Simulation Association'']
== 学术项目，部门和学位 ==
== 学术项目，部门和学位 ==
* [http://mediapatterns.enm.bris.ac.uk/ University of Bristol "Mediapatterns" project]
* [http://mediapatterns.enm.bris.ac.uk/ University of Bristol "Mediapatterns" project]
* [http://www.pdx.edu/sysc/resources-other-systems-science-programs Systems Sciences Programs List], Portland State. List of other worldwide related programs.
* [http://www.pdx.edu/sysc/resources-other-systems-science-programs Systems Sciences Programs List], Portland State. List of other worldwide related programs.
==中心和机构==
==科研中心和机构==
=== 北美地区 ===
=== 北美地区 ===
* [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 ==
== 更多资料 ==
* [[人工社会]]Artificial society
* [[人工社会]]
* [[社会仿真]]Social simulation
* [[社会仿真]]
* [[基于主体的社会仿真]] Agent-based social simulation
* [[基于主体的社会仿真]]
* [[社会复杂性]]Social complexity
* [[社会复杂性]]
* [[计算经济学]]Computational economics
* [[计算经济学]]
* [[计算流行病学]]Computational epidemiology
* [[计算流行病学]]
* [[可预测分析]]Predictive analytics
* [[可预测分析]]
* [[计算认知科学]]Computational cognition
* [[计算认知科学]]
** [[社交网络分析]]Social network analysis
** [[社交网络分析]]
==参考资料==
==参考资料==
{{reflist}}
{{reflist}}
== 外部链接 ==
== External links ==
*[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]
*[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]
*[http://jasss.soc.surrey.ac.uk/JASSS.html Journal of Artificial Societies and Social Simulation]
*[http://jasss.soc.surrey.ac.uk/JASSS.html Journal of Artificial Societies and Social Simulation]
*[https://web.archive.org/web/20110516072744/http://cmol.nbi.dk/models/ Agent based models for social networks, interactive java applets]
*[https://web.archive.org/web/20110516072744/http://cmol.nbi.dk/models/ Agent based models for social networks, interactive java applets]
*[https://web.archive.org/web/20090827052722/http://www.personal.kent.edu/~bcastel3/ Sociology and Complexity Science Website]
*[https://web.archive.org/web/20090827052722/http://www.personal.kent.edu/~bcastel3/ Sociology and Complexity Science Website]
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