更改

==历史==

==历史==

==History==

==历史==

 

[[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|Historical map of research paradigms and associated scientists in [[sociology and complexity science]].]]

{{main|Systems theory|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.

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===

===Macrosimulation and microsimulation===

{{main|System dynamics|Microsimulation}}

{{main|System dynamics|Microsimulation}}

By the late 1960s and early 1970s, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. These models used differential equations to predict population distributions as holistic functions of other systematic factors such as inventory control, urban traffic, migration, and disease transmission.<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"/>

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===

===Cellular automata and agent-based modeling===

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]].

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===

===Data mining and social network analysis===