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Analytical sociology and computational social science

Author

Listed:
  • Marc Keuschnigg

    (Linköping University)

  • Niclas Lovsjö

    (Linköping University)

  • Peter Hedström

    (Linköping University)

Abstract

Analytical sociology focuses on social interactions among individuals and the hard-to-predict aggregate outcomes they bring about. It seeks to identify generalizable mechanisms giving rise to emergent properties of social systems which, in turn, feed back on individual decision-making. This research program benefits from computational tools such as agent-based simulations, machine learning, and large-scale web experiments, and has considerable overlap with the nascent field of computational social science. By providing relevant analytical tools to rigorously address sociology’s core questions, computational social science has the potential to advance sociology in a similar way that the introduction of econometrics advanced economics during the last half century. Computational social scientists from computer science and physics often see as their main task to establish empirical regularities which they view as “social laws.” From the perspective of the social sciences, references to social laws appear unfounded and misplaced, however, and in this article we outline how analytical sociology, with its theory-grounded approach to computational social science, can help to move the field forward from mere descriptions and predictions to the explanation of social phenomena.

Suggested Citation

  • Marc Keuschnigg & Niclas Lovsjö & Peter Hedström, 2018. "Analytical sociology and computational social science," Journal of Computational Social Science, Springer, vol. 1(1), pages 3-14, January.
  • Handle: RePEc:spr:jcsosc:v:1:y:2018:i:1:d:10.1007_s42001-017-0006-5
    DOI: 10.1007/s42001-017-0006-5
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    References listed on IDEAS

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    1. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    2. Peter Hedström & Gianluca Manzo, 2015. "Recent Trends in Agent-based Computational Research," Sociological Methods & Research, , vol. 44(2), pages 179-185, May.
    3. Grimmer, Justin, 2010. "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases," Political Analysis, Cambridge University Press, vol. 18(1), pages 1-35, January.
    4. Jose Cadena & Gizem Korkmaz & Chris J Kuhlman & Achla Marathe & Naren Ramakrishnan & Anil Vullikanti, 2015. "Forecasting Social Unrest Using Activity Cascades," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-27, June.
    5. W. Brian Arthur, 1994. "Inductive Reasoning, Bounded Rationality and the Bar Problem," Working Papers 94-03-014, Santa Fe Institute.
    6. Jose L Herrera & Ravi Srinivasan & John S Brownstein & Alison P Galvani & Lauren Ancel Meyers, 2016. "Disease Surveillance on Complex Social Networks," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-16, July.
    7. Xavier Gabaix, 2016. "Power Laws in Economics: An Introduction," Journal of Economic Perspectives, American Economic Association, vol. 30(1), pages 185-206, Winter.
    8. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-411, May.
    9. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    10. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    11. Manski, Charles F., 2013. "Public Policy in an Uncertain World: Analysis and Decisions," Economics Books, Harvard University Press, number 9780674066892, Spring.
    12. Elizabeth Bruch & Jon Atwell, 2015. "Agent-Based Models in Empirical Social Research," Sociological Methods & Research, , vol. 44(2), pages 186-221, May.
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