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无编辑摘要
== Definitions ==
== Definitions ==
== Definitions ==
== 定义 ==
定义
定义
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。
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。
==历史==
==History==
{{see also|Sociology and complexity science}}
[[File:Complexity-map-with-sociolo.png|thumb|right|300px|Historical map of research paradigms and associated scientists in [[sociology and complexity science]].]]
=== Background ===
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"/>
===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.
===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"/>
===Cellular automata and agent-based modeling===
{{main|Cellular automata|agent-based modeling}}
The 1970s and 1980s were also a time when physicists and mathematicians were attempting to model and analyze how simple component units, such as atoms, give rise to global properties, such as complex material properties at low temperatures, in magnetic materials, and within turbulent flows.<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> 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 "[[complex systems|complexity theory]]" which, in turn, renewed interest in understanding complex physical and social systems across disciplinary boundaries.<ref name="SfSS1"/> 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.
This cellular automata paradigm gave rise to a third wave of social simulation emphasizing agent-based modeling. Like micro-simulations, these models emphasized bottom-up designs but adopted four key assumptions that diverged from microsimulation: autonomy, interdependency, simple rules, and adaptive behavior.<ref name="MW"/> Agent-based models are less concerned with predictive accuracy and instead emphasize theoretical development.<ref>{{cite journal |title=A simulation of the structure of academic science |journal=Sociological Research Online |volume=2 |issue=2 |pages=1–15 |year=1997 |first=Nigel |last=Gilbert |url=http://www.socresonline.org.uk/socresonline/2/2/3.html |doi=10.5153/sro.85 |access-date=2009-12-16 |archive-url=https://web.archive.org/web/19980524062306/http://www.socresonline.org.uk/socresonline/2/2/3.html |archive-date=1998-05-24 |url-status=dead }}</ref> In 1981, mathematician and political scientist [[Robert Axelrod]] and evolutionary biologist [[W.D. Hamilton]] published a major paper in ''[[Science (journal)|Science]]'' titled "The Evolution of Cooperation" which used an agent-based modeling approach to demonstrate how social cooperation based upon reciprocity can be established and stabilized in a [[prisoner's dilemma]] game when agents followed simple rules of self-interest.<ref>{{cite journal|title=The Evolution of Cooperation |first1=Robert |last1=Axelrod |first2=William D. |last2=Hamilton |journal=Science |volume=211 |issue=4489 |pages=1390–1396 |doi=10.1126/science.7466396|pmid=7466396 |date=March 27, 1981|bibcode=1981Sci...211.1390A }}</ref> Axelrod and Hamilton demonstrated that individual agents following a simple rule set of (1) cooperate on the first turn and (2) thereafter replicate the partner's previous action were able to develop "norms" of cooperation and sanctioning in the absence of canonical sociological constructs such as demographics, values, religion, and culture as preconditions or mediators of cooperation.<ref name="Cooperation"/> Throughout the 1990s, scholars like [[William Sims Bainbridge]], [[Kathleen Carley]], [[Michael Macy]], and [[John Skvoretz]] developed multi-agent-based models of [[generalized reciprocity]], [[prejudice]], [[social influence]], and organizational [[information processing]]. In 1999, [[Nigel Gilbert]] published the first textbook on Social Simulation: ''Simulation for the social scientist'' and established its most relevant journal: the [[Journal of Artificial Societies and Social Simulation]].
===Data mining and social network analysis===
{{main|Data mining|Social network analysis}}
Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like [[James 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.<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> 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.<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>]]
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"/>
===Computational content analysis===
[[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>
==研究方法==
==Methodologies==
Methodologically, social complexity is theory-neutral, meaning that it accommodates both local and global approaches to sociological research.<ref name="CCS-MMT"/> The very idea of social complexity arises out of the [[Historical comparative research|historical-comparative]] methods of early sociologists; obviously, this method is important in developing, defining, and refining the theoretical construct of social complexity. As complex social systems have many parts and there are many possible relationships between those parts, appropriate methodologies are typically determined to some degree by the research level of analysis [[Differentiation (sociology)|differentiated]]<ref>Luhmann, Niklas (1982). ''The Differentiation of Society.'' New York, NY: Columbia University Press.</ref> by the researcher according to the level of description or explanation demanded by the research hypotheses.
At the most localized level of analysis, [[ethnographic]], [[Participant observation|participant-]] or non-participant observation, [[content analysis]] and other [[qualitative research]] methods may be appropriate. More recently, highly sophisticated [[quantitative research]] methodologies are being developed and used in sociology at both local and global [[level of analysis|levels of analysis]]. Such methods include (but are not limited to) [[bifurcation diagram]]s, [[Social network analysis|network analysis]], [[Nonlinear system|non-linear]] modeling, and [[Computational sociology|computational]] models including [[Cellular automaton|cellular automata]] programming, [[sociocybernetics]] and other methods of [[social simulation]].
===Complex social network analysis===
{{Main|Dynamic network analysis}}
Complex [[social network]] analysis is used to study the dynamics of large, complex social networks. [[Dynamic network analysis]] brings together traditional [[social network analysis]], [[link analysis]] and [[multi-agent system]]s within [[network science]] and [[network theory]].<ref>Carley, Kathleen M. (2003), "Dynamic Network Analysis." ''Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers'', Ronald Breiger, Kathleen Carley, and Philippa Pattison (eds.), National Research Council (Committee on Human Factors): Washington, D.C.: 133–145.</ref> Through the use of key concepts and methods in [[social network analysis]], [[agent-based modeling]], theoretical [[physics]], and modern [[mathematics]] (particularly [[graph theory]] and [[fractal geometry]]), this method of inquiry brought insights into the dynamics and structure of social systems. New computational methods of localized social network analysis are coming out of the work of [[Duncan Watts]], [[Albert-László Barabási]], [[Nicholas A. Christakis]], [[Kathleen Carley]] and others.
New methods of global network analysis are emerging from the work of [[John Urry (sociologist)|John Urry]] and the sociological study of globalization, linked to the work of [[Manuel Castells]] and the later work of [[Immanuel Wallerstein]]. Since the late 1990s, Wallerstein increasingly makes use of complexity theory, particularly the work of [[Ilya Prigogine]].<ref>Barabási, Albert-László (2003). ''Linked: The New Science of Networks.'' Cambridge, MA: Perseus Publishing.</ref><ref>Freeman, Linton C. (2004). ''The Development of Social Network Analysis: A Study in the Sociology of Science.'' Vancouver Canada: Empirical Press.</ref><ref>Watts, Duncan J. (2004). "The New Science of Networks." ''Annual Review of Sociology'', 30: 243–270.</ref> Dynamic social network analysis is linked to a variety of methodological traditions, above and beyond [[systems thinking]], including [[graph theory]], traditional [[social network]] analysis in sociology, and [[mathematical sociology]]. It also links to [[chaos theory|mathematical chaos]] and [[complex dynamics]] through the work of [[Duncan Watts]] and [[Steven Strogatz]], as well as fractal geometry through [[Albert-László Barabási]] and his work on [[scale-free networks]].
===Computational sociology===
{{Main|Computational sociology}}
The development of [[computational sociology]] involves such scholars as [[Nigel Gilbert]], [[Klaus G. Troitzsch]], [[Joshua M. Epstein]], and others. The foci of methods in this field include [[social simulation]] and [[data-mining]], both of which are sub-areas of computational sociology. Social simulation uses computers to create an artificial laboratory for the study of complex social systems; [[Data mining|data-mining]] uses machine intelligence to search for non-trivial patterns of relations in large, complex, real-world databases. The emerging methods of [[socionics]] are a variant of computational sociology.<ref>Gilbert, Nigel and Klaus G. Troitzsch (2005). ''Simulation for Social Scientists'', 2nd Edition. New York, NY: Open University Press.</ref><ref name=epstein07>Epstein, Joshua M. (2007). ''Generative Social Science: Studies in Agent-Based Computational Modeling''. Princeton, NJ: Princeton University Press.</ref>
Computational sociology is influenced by a number of micro-sociological areas as well as the macro-level traditions of systems science and systems thinking. The micro-level influences of [[symbolic interactionism|symbolic interaction]], [[exchange theory|exchange]], and [[rational choice theory|rational choice]], along with the micro-level focus of computational political scientists, such as [[Robert Axelrod]], helped to develop computational sociology's [[:wikt:bottom-up|bottom-up]], [[agent-based]] approach to modeling complex systems. This is what [[Joshua M. Epstein]] calls [[generative science]].<ref name=epstein07 /> Other important areas of influence include [[statistics]], [[mathematical modeling]] and computer [[simulation]].
===Sociocybernetics===
{{Main|Sociocybernetics}}
[[Sociocybernetics]] integrates sociology with [[second-order cybernetics]] and the work of [[Niklas Luhmann]], along with the latest advances in [[complexity science]]. In terms of scholarly work, the focus of sociocybernetics has been primarily conceptual and only slightly methodological or empirical.<ref>[[Geyer, Felix]] and [[Johannes van der Zouwen]] (1992). "Sociocybernetics." ''Handbook of Cybernetics'', C.V. Negoita (ed.): 95–124. New York: Marcel Dekker.</ref> Sociocybernetics is directly tied to [[Systems thinking|systems thought]] inside and outside of sociology, specifically in the area of second-order cybernetics.
==应用与分支==
==Areas of application==
As a [[Middle range theory (sociology)|middle-range]] theoretical platform, social complexity can be applied to any research in which [[social interaction]] or the outcomes of such interactions can be observed, but particularly where they can be [[Measurement|measured]] and expressed as [[Continuous function (set theory)|continuous]] or [[Discrete mathematics|discrete]] data points. One common criticism often cited regarding the usefulness of complexity science in sociology is the difficulty of obtaining adequate data.<ref>Stewart, Peter (2001). "Complexity Theories, Social Theory, and the Question of Social Complexity." ''Philosophy of the Social Sciences'', 31(3): 323–360.</ref> Nonetheless, application of the concept of social complexity and the analysis of such complexity has begun and continues to be an ongoing field of inquiry in sociology. From [[childhood]] friendships and [[teen pregnancy]]<ref name="CCS-MMT" /> to [[criminology]]<ref>Lee, Ju-Sung. (2001). "Evolving Drug Networks." [http://www.casos.cs.cmu.edu/ Carnegie Mellon Center for Computational Analysis of Social and Organizational Systems (CASOS)] Conference Presentation (unpublished).</ref> and [[counter-terrorism]],<ref>Carley, Kathleen (2003). "Destabilizing Terrorist Networks." ''Proceedings of the 8th International Command and Control Research and Technology Symposium''. Conference held at the National Defense War College: Washington D.C., Evidence Based Research, Track 3. [http://www.dodccrp.org/events/2003/8th_ICCRTS/pdf/021.pdf (Electronic Publication).] {{webarchive|url=https://web.archive.org/web/20041218014917/http://www.dodccrp.org/events/2003/8th_ICCRTS/pdf/021.pdf |date=2004-12-18 }}</ref> theories of social complexity are being applied in almost all [[Subfields of sociology|areas of sociological research]].
In the area of [[Communications theory|communications research]] and [[informetrics]], the concept of self-organizing systems appears in mid-1990s research related to scientific communications.<ref>[[Loet Leydesdorff|Leydesdorff, Loet]] (1995). ''The Challenge of Scientometrics: The development, measurement, and self-organization of scientific communications''. Leiden: DSWO Press, Leiden University.</ref> [[Scientometrics]] and [[bibliometrics]] are areas of research in which discrete data are available, as are several other areas of social communications research such as [[sociolinguistics]].<ref name="CCS-MMT" /> Social complexity is also a concept used in [[semiotics]].<ref>Dimitrov, Vladimir and Robert Woog (1997). "Studying Social Complexity: From Soft to Virtual Systems Methodology." [http://www.complex-systems.com/pdf/11-6-5.pdf Complex Systems, 11:(6)].</ref>
In the first decade of the 21st century, the diversity of areas of application has grown<ref>Saberi, Mohammad Karim, Alireza Isfandyari-Moghaddam and Sedigheh Mohamadesmaeil (2011). "Web Citations Analysis of the JASSS: the First Ten Years." [http://jasss.soc.surrey.ac.uk/14/4/22.html ''Journal of Artificial Societies and Social Simulation'', 14:(4), 22].</ref> as more sophisticated methods have developed. Social complexity theory is applied in studies of social [[cooperation]] and [[public goods]];<ref>Nowak, Martin and Roger Highfield (2011). ''Super Cooperators: Altruism, Evolution, and Why We Need Each Other to Succeed''. New York, NY: Free Press.</ref> [[Altruism (ethics)|altruism]];<ref>Hang, Ye, Fei Tan, Mei Ding, Yongmin Jia and Yefeng Chen (2011). "Sympathy and Punishment: Evolution of Cooperation in Public Goods Game." [http://jasss.soc.surrey.ac.uk/14/4/20.html ''Journal of Artificial Societies and Social Simulation'', 14(4): 20].</ref> [[voting behavior]];<ref>Braha, D., & de Aguiar, M. A. (2016). [https://arxiv.org/abs/1610.04406 Voting Contagion]. arXiv preprint arXiv:1610.04406.</ref><ref>Braha, D., & de Aguiar, M. A. (2017). [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177970 Voting contagion: Modeling and analysis of a century of U.S. presidential elections]. PLoS ONE 12(5): e0177970. https://doi.org/10.1371/journal.pone.0177970</ref> [[education]];<ref>Mason, Mark (2008). ''Complexity Theory and the Philosophy of Education''. Hoboken, NJ: Wiley-Blackwell (Educational Philosophy and Theory Special Issues).</ref> global civil society <ref>Castellani, Brian. (2018). "The Defiance of Global Commitment: A Complex Social Psychology. Routledge complexity in social science series." doi:10.4324/9781351137140.</ref>
and global [[civil unrest]];<ref>Braha, Dan. (2012). [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0048596 "Global Civil Unrest: Contagion, Self-Organization, and Prediction."] PLoS ONE 7(10): e48596. doi:10.1371/journal.pone .0048596.</ref> [[collective action]] and [[social movement]]s;<ref>Lohmann Susanne (1994). "Dynamics of Informational Cascades: The Monday Demonstrations in Leipzig, East Germany, 1989–1991," ''World Politics'', 47: 42–101.</ref><ref>Chesters, Graeme and Ian Welsh (2006). ''Complexity and Social Movements: Protest at the Edge of Chaos." London: Routledge (International Library of Sociology).</ref> [[social inequality]];<ref>Castellani, Brian et al. (2011). "Addressing the U.S. Financial/Housing Crisis: Pareto, Schelling and Social Mobility."[http://cch.ashtabula.kent.edu/publications/Addressing%20the%20U.S.%20Financial%20&%20Housing%20Crisis.pdf Working Paper].</ref> workforce and [[unemployment]];<ref>Hedström, Peter and Yvonne Åberg (2011). "Social interaction and youth unemployment." ''Analytical Sociology and Social Mechanisms'', Pierre Demeulenaere (ed.). Cambridge: Cambridge University Press.</ref><ref>Yilmaz, Levent (2011). "Toward Multi-Level, Multi-Theoretical Model Portfolios for Scientific Enterprise Workforce Dynamics." [http://jasss.soc.surrey.ac.uk/14/4/2.html ''Journal of Artificial Societies and Social Simulation'', 14(4): 2.]</ref> [[economic geography]] and [[economic sociology]];<ref>Dan Braha, Blake Stacey and Yaneer Bar-Yam. (2011). [http://necsi.edu/affiliates/braha/Journal_Version_SON_Braha.pdf "Corporate Competition: A Self-Organizing Network."] Social Networks, 33(3): 219-230.</ref> [[policy analysis]];<ref>Jervis, Robert (1998). ''System Effects: Complexity in Political and Social Life''. Princeton, NJ: Princeton University Press.</ref><ref>Elliott, Euel and L. Douglas Kiel (eds.) (2000). ''Nonlinear Dynamics, Complexity and Public Policy''. Hauppauge NY: Nova Science Publishers.</ref> [[health care systems]];<ref>Brian Castellani, Rajeev Rajaram, J. Galen Buckwalter, Michael Ball and Frederic Hafferty (2012). [https://www.springer.com/public+health/book/978-3-319-09733-6 "Place and Health as Complex Systems: A Case Study and Empirical Test"]. ''SpringerBriefs in Public Health.''</ref> and [[innovation]] and [[social change]],<ref>Leydesdorff, Loet (2006). ''The Knowledge-Based Economy Modeled, Measured, Simulated''. Boca Raton, FL: Universal-Publishers .</ref><ref>Lane, D.; Pumain, D.; Leeuw, S.E. van der; West, G. (eds.) (2009). ''Complexity Perspectives in Innovation and Social Change''. New York, NY: Springer (Methodos Series, Vol. 7).</ref> to name a few. A current international scientific research project, the [[Seshat (project)|Seshat: Global History Databank]], was explicitly designed to analyze changes in social complexity from the [[Neolithic Revolution]] until the [[Industrial Revolution]].
==See also==
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==References==
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== External links ==
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