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Cited by 10Multi-Agent Systems and NegotiationSimulation Techniques and ApplicationsOpinion Dynamics and Social Influence

Computational approaches in rigorous sociology: agent-based computational modeling and computational social science

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Flache, Andreas, Mäs, Michael, Keijzer, Marijn A.·2022·Handbook of Sociological Science
DOI 10.4337/9781789909432.00011
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Abstract

We discuss two computational approaches of particular significance for rigorous sociology, Agent-Based Computational Modeling (ABCM) and Computational Social Science (CSS). CSS exploits novel sources of large-scale data from, for example, the Internet, telephone-communication records, or population register data, studying digital traces of social interaction with great precision. ABCM uses computer simulation to study how social regularities can arise from complex interactions among interdependent individuals. CSS and ABCM share a strong focus on computational methods, but only a few contributions combine them. We briefly introduce both approaches and argue that they can strongly benefit from more interaction and integration. ABCM can address the generalizability and theoretical explanation of empirical patterns identified in CSS, whilst empirical CSS can help validate and calibrate otherwise abstract models from ABCM. We discuss examples of a successful combination of both approaches and address directions for more theoretical and methodological integration in the future.

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  • To move from description to explanation, we cannot rely on empirical observation alone, but have to understand the dynamics of a system that led to the particular observed outcome.

    “To move from description to explanation, however, we cannot rely on empirical observation alone, but have to understand the dynamics of a system that led to the particular observed outcome (Keuschnigg et al. 2017;Flache et al. 2022).”

    From Computational Social Science of Social Cohesion and Polarization (2026) · cites this paper as Flache et al. 2022

  • Theory-driven agent-based modeling emphasize the generative role of formal models in hypothesis development, allowing researchers to explore how micro-level assumptions-such as bounded confidence or social influence-can produce unexpected macro-level dynamics like polarization.

    “Theory-driven agent-based modeling emphasize the generative role of formal models in hypothesis development (Flache et al. 2022), allowing researchers to explore how micro-level assumptions-such as bounded confidence or social influence-can produce unexpected macro-level dynamics like polarization (e.g., see Chap.empirical data to calibrate agent behavior (e.g., see Chap.9: Edmonds et al. 2025) or”

    From Computational Social Science of Social Cohesion and Polarization (2026) · cites this paper as Flache et al. 2022

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Research works printed but not linked: 38

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Cited by

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Authors
Flache, A., Mäs, M., & Keijzer, M. A.
Affiliations
University of Groningen (NL)
ORCID
Venue
Handbook of Sociological Science
Publisher
Edward Elgar Publishing
Published
2022-06-10
Pages
57-72
DOI
10.4337/9781789909432.00011
OpenAlex
W4282824274
Cited by
10
References
4
Citations by year
2026: 2 · 2025: 1 · 2024: 4 · 2023: 2 · 2022: 1
FWCI
3.71
Type
book chapter
Language
en
Open access
Licence
Locations
  • Edward Elgar Publishing eBooks (publishedVersion, cc-by-nc-nd)
  • Toulouse 1 Capitole Publications (Université Toulouse I Capitole) (submittedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by-nc-nd)
Funders
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (464.18.112)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
Keywords
computational social science, digital traces, computer simulation, social regularities, model calibration, generalizability, large-scale data

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