计算社会科学

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  • 计算社会科学定义————
  • 计算社会科学发展历史————
  • 计算社会科学的研究方法————
  • 计算社会科学的应用与分支————

翻译整理截止日期：2020.5.8 18：00


Computational social science refers to the academic sub-disciplines concerned with computational approaches to the social sciences. This means that computers are used to model, simulate, and analyze social phenomena. Fields include computational economics, computational sociology, cliodynamics, culturomics, and the automated analysis of contents, in social and traditional media. It focuses on investigating social and behavioral relationships and interactions through social simulation, modeling, network analysis, and media analysis.[1]

Computational social science refers to the academic sub-disciplines concerned with computational approaches to the social sciences. This means that computers are used to model, simulate, and analyze social phenomena. Fields include computational economics, computational sociology, cliodynamics, culturomics, and the automated analysis of contents, in social and traditional media. It focuses on investigating social and behavioral relationships and interactions through social simulation, modeling, network analysis, and media analysis.

计算社会科学是指与社会科学计算方法有关的学术分支学科。这意味着计算机被用来建模、模拟和分析社会现象。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。



Definitions

定义

定义

There are two terminologies that relate to each other: Social Science Computing (SSC) and Computational Social Science (CSS). In literature, CSS is referred to the field of social science that uses the computational approaches in studying the social phenomena.

There are two terminologies that relate to each other: Social Science Computing (SSC) and Computational Social Science (CSS). In literature, CSS is referred to the field of social science that uses the computational approaches in studying the social phenomena.

有两个相互关联的术语: 社会科学计算(SSC)和计算社会科学(CSS)。在文献中，CSS 指的是社会科学领域，它使用计算方法来研究社会现象。

On the other hand, SSC is the field in which computational methodologies are created to assist in explanations of social phenomena.

On the other hand, SSC is the field in which computational methodologies are created to assist in explanations of social phenomena.

另一方面，南南合作是一个领域，其中计算方法创建，以协助解释社会现象。



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.[2][3] 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,[4] 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。计算任务包括分析社交网络、社交地理系统、社交媒体内容和传统媒体内容。



Computational social science work increasingly relies on the greater availability of large databases, currently constructed and maintained by a number of interdisciplinary projects, including:

Computational social science work increasingly relies on the greater availability of large databases, currently constructed and maintained by a number of interdisciplinary projects, including:

计算社会科学工作越来越依赖于大型数据库的更大可用性，这些数据库目前由一些跨学科项目建立和维护，包括:

  • The Seshat: Global History Databank, which systematically collects state-of-the-art accounts of the political and social organization of human groups and how societies have evolved through time into an authoritative databank.[5] Seshat is affiliated also with the Evolution Institute, a non-profit think-tank that "uses evolutionary science to solve real-world problems."


  • D-PLACE: the Database of Places, Languages, Culture and Environment, which provides data on over 1,400 human social formations[6]







  • Clio-Infra a database of measures of economic performance and other aspects of societal well-being on a global sample of societies from 1800 CE to the present


  • The Google Ngram Viewer, an online search engine that charts frequencies of sets of comma-delimited search strings using a yearly count of n-grams as found in the largest online body of human knowledge, the Google Books corpus.




The analysis of vast quantities of historical newspaper[10] and book content[11] have been pioneered in 2017, while other studies on similar data[12] showed how periodic structures can be automatically discovered in historical newspapers. A similar analysis was performed on social media, again revealing strongly periodic structures.[13]

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。

历史

History

See also: Sociology and complexity science
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 模板:According to whom. 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.[14] 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.[15] 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.[14]

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".[14]

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.[14]

Systems theory and structural functionalism

Main articles: Systems theory and Structural functionalism

In the post-war era, Vannevar Bush's differential analyser, John von Neumann's cellular automata, Norbert Wiener's cybernetics, and Claude Shannon's information theory became influential paradigms for modeling and understanding complexity in technical systems. In response, scientists in disciplines such as physics, biology, electronics, and economics began to articulate a general theory of systems in which all natural and physical phenomena are manifestations of interrelated elements in a system that has common patterns and properties. Following Émile Durkheim's call to analyze complex modern society sui generis,[16] 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.[17] 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.[18] Because computer algorithms and programs had been used as early as 1956 to test and validate mathematical theorems, such as the four color theorem,[19] 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 articles: System dynamics and Microsimulation

By the late 1960s and early 1970s, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. These models used differential equations to predict population distributions as holistic functions of other systematic factors such as inventory control, urban traffic, migration, and disease transmission.[20][21] 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,[22] the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific.[23][24] 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.[25] However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.[26]

Cellular automata and agent-based modeling

Main articles: Cellular automata and agent-based modeling

The 1970s and 1980s were also a time when physicists and mathematicians were attempting to model and analyze how simple component units, such as atoms, give rise to global properties, such as complex material properties at low temperatures, in magnetic materials, and within turbulent flows.[27] Using cellular automata, scientists were able to specify systems consisting of a grid of cells in which each cell only occupied some finite states and changes between states were solely governed by the states of immediate neighbors. Along with advances in artificial intelligence and microcomputer power, these methods contributed to the development of "chaos theory" and "complexity theory" which, in turn, renewed interest in understanding complex physical and social systems across disciplinary boundaries.[23] 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.[26] Agent-based models are less concerned with predictive accuracy and instead emphasize theoretical development.[28] In 1981, mathematician and political scientist Robert Axelrod and evolutionary biologist W.D. Hamilton published a major paper in Science titled "The Evolution of Cooperation" which used an agent-based modeling approach to demonstrate how social cooperation based upon reciprocity can be established and stabilized in a prisoner's dilemma game when agents followed simple rules of self-interest.[29] 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.[30] 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 articles: Data mining and Social network analysis

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman.[31] The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments.[32] 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.[33][34]

Narrative network of US Elections 2012[35]

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.[36] This automates the approach introduced by quantitative narrative analysis,[37] whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.[35]

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.[38][39][40][41][42] The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al.[43] 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.[44]

The analysis of vast quantities of historical newspaper content has been pioneered by Dzogang et al.,[45] 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.[46]

研究方法

Methodologies

Methodologically, social complexity is theory-neutral, meaning that it accommodates both local and global approaches to sociological research.[47] The very idea of social complexity arises out of the 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 differentiated[48] 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- 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 levels of analysis. Such methods include (but are not limited to) bifurcation diagrams, network analysis, non-linear modeling, and computational models including cellular automata programming, sociocybernetics and other methods of social simulation.

Complex social network analysis

Main article: 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 systems within network science and network theory.[49] 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 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.[50][51][52] 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 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 article: 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 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.[53][54]

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 interaction, exchange, and rational choice, along with the micro-level focus of computational political scientists, such as Robert Axelrod, helped to develop computational sociology's bottom-up, agent-based approach to modeling complex systems. This is what Joshua M. Epstein calls generative science.[54] Other important areas of influence include statistics, mathematical modeling and computer simulation.

Sociocybernetics

Main article: 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.[55] Sociocybernetics is directly tied to 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 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 measured and expressed as continuous or discrete data points. One common criticism often cited regarding the usefulness of complexity science in sociology is the difficulty of obtaining adequate data.[56] 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[47] to criminology[57] and counter-terrorism,[58] theories of social complexity are being applied in almost all areas of sociological research.

In the area of communications research and informetrics, the concept of self-organizing systems appears in mid-1990s research related to scientific communications.[59] 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.[47] Social complexity is also a concept used in semiotics.[60]

In the first decade of the 21st century, the diversity of areas of application has grown[61] as more sophisticated methods have developed. Social complexity theory is applied in studies of social cooperation and public goods;[62] altruism;[63] voting behavior;[64][65] education;[66] global civil society [67] and global civil unrest;[68] collective action and social movements;[69][70] social inequality;[71] workforce and unemployment;[72][73] economic geography and economic sociology;[74] policy analysis;[75][76] health care systems;[77] and innovation and social change,[78][79] to name a few. A current international scientific research project, the Seshat: Global History Databank, was explicitly designed to analyze changes in social complexity from the Neolithic Revolution until the Industrial Revolution.



See also

参见

参见














References

参考资料

参考资料

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  2. ↑ DT&SC 7-1: . Introduction to e-Science: From the DT&SC online course at the University of California
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Category:Social sciences

类别: 社会科学

Category:Computational science

类别: 计算科学

Category:Computational fields of study

类别: 研究的计算领域



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