Computational social science

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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]

Social science is a category of academic disciplines, concerned with society and the relationships among individuals within a society. Social science as a whole has many branches. These social sciences include, but are not limited to: anthropology, archaeology, communication studies, economics, history, human geography, jurisprudence, linguistics, political science, psychology, public health, and sociology. The term is also sometimes used to refer specifically to the field of sociology, the original "science of society", established in the 19th century. For a more detailed list of sub-disciplines within the social sciences see: Outline of social science.

Computational economics is a research discipline at the interface of computer science, economics, and management science. This subject encompasses computational modeling of economic systems, whether agent-based, general-equilibrium, macroeconomic, or rational-expectations, computational econometrics and statistics, computational finance, computational tools for the design of automated internet markets, programming tools specifically designed for computational economics, and pedagogical tools for the teaching of computational economics. Some of these areas are unique to computational economics, while others extend traditional areas of economics by solving problems that are difficult to study without the use of computers and associated numerical methods.

Computational sociology branch of sociology that uses computational methods to study social phenomena

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.

Contents

Definitions

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

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.

Scientific method mathematical and experimental techniques employed in the natural sciences; more specifically, techniques used in the construction and testing of scientific hypotheses

The scientific method is an empirical method of acquiring knowledge that has characterized the development of science since at least the 17th century. It involves careful observation, applying rigorous skepticism about what is observed, given that cognitive assumptions can distort how one interprets the observation. It involves formulating hypotheses, via induction, based on such observations; experimental and measurement-based testing of deductions drawn from the hypotheses; and refinement of the hypotheses based on the experimental findings. These are principles of the scientific method, as distinguished from a definitive series of steps applicable to all scientific enterprises.

Empirical research is research using empirical evidence. It is a way of gaining knowledge by means of direct and indirect observation or experience. Empiricism values such research more than other kinds. Empirical evidence can be analyzed quantitatively or qualitatively. Quantifying the evidence or making sense of it in qualitative form, a researcher can answer empirical questions, which should be clearly defined and answerable with the evidence collected. Research design varies by field and by the question being investigated. Many researchers combine qualitative and quantitative forms of analysis to better answer questions which cannot be studied in laboratory settings, particularly in the social sciences and in education.

Big data Information assets characterized by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value

Big data is a field that treats of ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed with big data are veracity and value.

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

Seshat (project)

The Seshat: Global History Databank is an international scientific research project of the nonprofit Evolution Institute. Founded in 2011, the Seshat: Global History Databank gathers data into a single, large database that can be used to test scientific hypotheses. The Databank consults directly with expert scholars to code what historical societies and their environments were like in the form of accessible datapoints and thus forms a digital storehouse for data on the political and social organization of all human groups from the early modern back to the ancient and neolithic periods. The organizers of this research project contend that the mass of data then can be used to test a variety of competing hypotheses about the rise and fall of large-scale societies around the globe which may help science provide answers to global problems.

The Evolution Institute, established in 2010, is a non-profit think tank based in San Antonio, Florida that seeks to apply evolutionary science to social issues. Founded by evolutionary biologist David Sloan Wilson, it is the current home of Seshat: Global History Databank.

Peter N. Peregrine American anthropologist

Peter N. Peregrine is an American anthropologist, registered professional archaeologist, and academic. He is well known for his staunch defense of science in anthropology, and for his popular textbook Anthropology. Peregrine did dissertation research on the evolution of the Mississippian culture of North America, and then did fieldwork on Bronze Age cities in Syria. He is currently Professor of Anthropology and Museum Studies at Lawrence University and Research Associate of the Human Relations Area Files at Yale University. From 2012 to 2018 he was an External Professor at the Santa Fe Institute.

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]

See also

Cliodynamics

Cliodynamics is a transdisciplinary area of research integrating cultural evolution, economic history/cliometrics, macrosociology, the mathematical modeling of historical processes during the longue durée, and the construction and analysis of historical databases. Cliodynamics treats history as science. Its practitioners develop theories that explain such dynamical processes as the rise and fall of empires, population booms and busts, spread and disappearance of religions. These theories are translated into mathematical models. Finally, model predictions are tested against data. Thus, building and analyzing massive databases of historical and archaeological information is one of the most important goals of cliodynamics.

Computational cognition is the study of the computational basis of learning and inference by mathematical modeling, computer simulation, and behavioral experiments. In psychology, it is an approach which develops computational models based on experimental results. It seeks to understand the basis behind the human method of processing of information. Early on computational cognitive scientists sought to bring back and create a scientific form of Brentano’s psychology

Digital sociology is a sub-discipline of sociology that focuses on understanding the use of digital media as part of everyday life, and how these various technologies contribute to patterns of human behavior, social relationships and concepts of the self.

Related Research Articles

Complexity characterises the behaviour of a system or model whose components interact in multiple ways and follow local rules, meaning there is no reasonable higher instruction to define the various possible interactions.

Computational archaeology describes computer-based analytical methods for the study of long-term human behaviour and behavioural evolution. As with other sub-disciplines that have prefixed 'computational' to their name, the term is reserved for methods that could not realistically be performed without the aid of a computer.

Outline of academic disciplines Overview of and topical guide to academic disciplines

An academic discipline or field of study is a branch of knowledge, taught and researched as part of higher education. A scholar's discipline is commonly defined by the university faculties and learned societies to which he or she belongs and the academic journals in which he or she publishes research.

Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.

Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology, organizational behavior, sociology, political science, economics, anthropology, geography, engineering, archaeology and linguistics.

The Human Relations Area Files, Inc. (HRAF), located in New Haven, Connecticut is a nonprofit international membership organization with over 500 member institutions in the U.S. and more than 20 other countries. A financially autonomous research agency based at Yale University since 1949, its mission is to promote understanding of cultural diversity and commonality in the past and present. To accomplish this mission, the Human Relations Area Files produces scholarly resources and infrastructure for research, teaching and learning, and supports and conducts original research on cross-cultural variation.

In sociology, social complexity is a conceptual framework used in the analysis of society. Contemporary definitions of complexity in the sciences are found in relation to systems theory, in which a phenomenon under study has many parts and many possible arrangements of the relationships between those parts. At the same time, what is complex and what is simple is relative and may change with time.

Artificial society is the specific agent based computational model for computer simulation in social analysis. It is mostly connected to the theme in complex system, emergence, Monte Carlo method, computational sociology, multi-agent system, and evolutionary programming. The concept itself is simple enough. Actually reaching this conceptual point took a while. Complex mathematical models have been, and are, common; deceivingly simple models only have their roots in the late forties, and took the advent of the microcomputer to really get up to speed.

Macrosociology is an approach to sociology which emphasizes the analysis of social systems and populations on a large scale, at the level of social structure, and often at a necessarily high level of theoretical abstraction. Microsociology, by contrast, focuses on the individual social agency. Macrosociology also concerns individuals, families, and other constituent aspects of a society, but always does so in relation to larger social system of which they are a part. Macrosociology can also be the analysis of large collectivities. Human populations are considered a society to the degree that is politically autonomous and its members to engage in a broad range of cooperative activities. For example, this definition would apply to the population of Germany being deemed a society, but German-speaking people as a whole scattered about different countries would not be considered a society. Macrosociology deals with broad societal trends that can later be applied to the smaller features of a society. To differentiate, macrosociology deals with issues such as war, distress of Third World nations, poverty, and environmental deprivation, whereas microsociology analyses issues such as the role of women, the nature of the family, and immigration.

Nello Cristianini is a Professor of Artificial Intelligence in the Department of Computer Science at the University of Bristol.

Quantitative history is an approach to historical research that makes use of quantitative, statistical and computer tools. It is considered a branch of social science history and has four leading journals: Historical Methods, Journal of Interdisciplinary History, the Social Science History, and Cliodynamics: The Journal of Quantitative History and Cultural Evolution.

Digital humanities an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities

Digital humanities (DH) is an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities. It includes the systematic use of digital resources in the humanities, as well as the reflection on their application. DH can be defined as new ways of doing scholarship that involve collaborative, transdisciplinary, and computationally engaged research, teaching, and publishing. It brings digital tools and methods to the study of the humanities with the recognition that the printed word is no longer the main medium for knowledge production and distribution.

Sociology Scientific study of human society and its origins, development, organizations, and institutions

Sociology is the scientific study of society, patterns of social relationships, social interaction, and culture of everyday life. It is a social science that uses various methods of empirical investigation and critical analysis to develop a body of knowledge about social order, acceptance, and change or social evolution. While some sociologists conduct research that may be applied directly to social policy and welfare, others focus primarily on refining the theoretical understanding of social processes. Subject matter ranges from the micro-sociology level of individual agency and interaction to the macro level of systems and the social structure.

Nigel Gilbert British sociologist and a pioneer in the use of agent-based models in the social sciences

Geoffrey Nigel Gilbert is a British sociologist and a pioneer in the use of agent-based models in the social sciences. He is the founder and director of the Centre for Research in Social Simulation, author of several books on computational social science, social simulation and social research and past editor of the Journal of Artificial Societies and Social Simulation (JASSS), the leading journal in the field.

The social data revolution is the shift in human communication patterns towards increased personal information sharing and its related implications, made possible by the rise of social networks in the early 2000s. This phenomenon has resulted in the accumulation of unprecedented amounts of public data.

Culturomics is a form of computational lexicology that studies human behavior and cultural trends through the quantitative analysis of digitized texts. Researchers data mine large digital archives to investigate cultural phenomena reflected in language and word usage. The term is an American neologism first described in a 2010 Science article called Quantitative Analysis of Culture Using Millions of Digitized Books, co-authored by Harvard researchers Jean-Baptiste Michel and Erez Lieberman Aiden.

References

  1. "The Computational Social Science Society of the Americas official website".
  2. DT&SC 7-1: . Introduction to e-Science: From the DT&SC online course at the University of California
  3. Hilbert, M. (2015). e-Science for Digital Development: ICT4ICT4D (PDF). Centre for Development Informatics, SEED, University of Manchester. ISBN   978-1-905469-54-3. Archived from the original (PDF) on 2015-09-24.
  4. Cioffi-Revilla, Claudio (2010). "Computational social science". Wiley Interdisciplinary Reviews: Computational Statistics . 2 (3): 259–271. doi:10.1002/wics.95.
  5. Turchin, Peter; Brennan, Rob; Currie, Thomas E.; Feeney, Kevin C.; Francois, Pieter; Hoyer, Daniel; Manning, J. G.; Marciniak, Arkadiusz; Mullins, Daniel; Palmisano, Alessio; Peregrine, Peter; Turner, Edward A. L.; Whitehouse, Harvey (2015). "Seshat: The Global History Databank". Cliodynamics. 6: 77. https://escholarship.org/uc/item/9qx38718
  6. Kirby, Kathryn R.; Gray, Russell D.; Greenhill, Simon J.; Jordan, Fiona M.; Gomes-Ng, Stephanie; Bibiko, Hans-Jörg; Blasi, Damián E.; Botero, Carlos A.; Bowern, Claire; Ember, Carol R.; Leehr, Dan; Low, Bobbi S.; McCarter, Joe; Divale, William (2016). "D-PLACE: A Global Database of Cultural, Linguistic and Environmental Diversity". PLoS ONE. 11 (7): e0158391. doi:10.1371/journal.pone.0158391. PMC   4938595 . PMID   27391016.
  7. Peter N. Peregrine, Atlas of Cultural Evolution, World Cultures 14(1), 2003
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  9. "eHRAF World Cultures". Human Relations Area Files.
  10. Lansdall-Welfare, Thomas; Sudhahar, Saatviga; Thompson, James; Lewis, Justin; Team, FindMyPast Newspaper; Cristianini, Nello (2017-01-09). "Content analysis of 150 years of British periodicals". Proceedings of the National Academy of Sciences. 114 (4): E457–E465. doi:10.1073/pnas.1606380114. ISSN   0027-8424. PMC   5278459 . PMID   28069962.
  11. Roth, Steffen; et al. (2017). "Futures of a distributed memory. A global brain wave measurement (1800-2000)". Technological Forecasting and Social Change. 118: 307–323. doi:10.1016/j.techfore.2017.02.031.
  12. Dzogang, Fabon; Lansdall-Welfare, Thomas; Team, FindMyPast Newspaper; Cristianini, Nello (2016-11-08). "Discovering Periodic Patterns in Historical News". PLOS ONE. 11 (11): e0165736. doi:10.1371/journal.pone.0165736. ISSN   1932-6203. PMC   5100883 . PMID   27824911.
  13. 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

The European Social Simulation Association (ESSA) is a scientific society aimed at promoting the development of social simulation research, education and application in Europe. It has over 350 members from several European countries. The association organizes a European conference every two years, and — in joint action with the Computational Social Science Society of the Americas (CSSSA) and the Pacific Asian Association for Agent-based Approach in Social Systems Sciences (PAAA) — a World Congress on Social Simulation (WCSS) every other year.