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Sensemaking of causality in agent-based models

Permanent link
https://hdl.handle.net/10037/25984
DOI
https://doi.org/10.1080/13645579.2022.2049510
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Published version (PDF)
Date
2022-03-25
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Antosz, Patrycja; Szczepanska, Timo; Bouman, Loes; J. Gareth, Polhill; Jager, Wander
Abstract
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.
Is part of
Szczepanska, T. (2023). Foundations of GAM Research. Methodological Guidelines for Designing and Conducting Research that Combines Games and Agent-based Models. (Doctoral thesis). https://hdl.handle.net/10037/31866
Publisher
Taylor & Francis
Citation
Antosz, Szczepanska, Bouman, J. Gareth, Jager. Sensemaking of causality in agent-based models. International Journal of Social Research Methodology: Theory and Practice. 2022:1-12
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