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Agent-based models and causality: a methodological appraisal
University of Geneva, Switzerland.
CNRS and University of Paris-Sorbonne, France.
2016 (English)Report (Other academic)
Abstract [en]

Computational agent-based models are entering the toolbox of quantitative sociologists. However, markedly contrasting views still exist as to its capacity to contribute to causally-oriented empirical research. Building on selected works across disciplines ranging from computer science to philosophy, we connect scholarship on causality, mechanisms, and simulation methods, and provide the 15 first systematic discussion on how, if at all, computational agent-based models warrant causal inference. First, we argue that this method can produce causallyrelevant evidence when (and only when) specific conditions are met. Then, we show that data-driven methods for causal inference face analogous challenges. Finally, upon endorsing a pragmatist view of evidence, we defend an approach 20 to causal analysis that combines evidence from agent-based modeling and datadriven methods. This evidential variety lends credibility to causal inference in virtue of drawing on complementary, and equally important, kinds of evidence.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. , p. 80
Series
The IAS Working Paper Series ; 2016:7
National Category
Sociology
Identifiers
URN: urn:nbn:se:liu:diva-133332Libris ID: 20012428OAI: oai:DiVA.org:liu-133332DiVA, id: diva2:1058813
Available from: 2016-12-21 Created: 2016-12-20 Last updated: 2017-01-26Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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