Sensemaking of causality in agent-based models

Patrycja Antosz*, Timo Szczepanska, Loes Bouman, J. Gareth Polhill, Wander Jager

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Even though agent-based modelling is seen as committing to a mechanistic, generative type of causation, the methodology allows for representing many other types of causal explanations. Agent-based models are capable of integrating diverse causal relationships into coherent causal mechanisms. They mirror the crucial, multi-level component of emergent phenomena and recognize the important role of single-level causes without limiting the scope of the offered explana- tion. Implementing various types of causal relationships to complement the generative causation offers insight into how a multi-level phenomenon happens and allows for building more complete causal explanations. The capacity to work with multiple approaches to causality is crucial when tackling the complex problems of the modern world.

Original languageEnglish
Pages (from-to)557-567
Number of pages11
JournalInternational Journal of Social Research Methodology
Issue number4
Publication statusPublished - 2022


  • agent-based modelling
  • Causality
  • complexity

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