Sensemaking of causality in agent-based models
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https://hdl.handle.net/10037/25984Date
2022-03-25Type
Journal articleTidsskriftartikkel
Peer reviewed
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/10037/31866Publisher
Taylor & FrancisCitation
Antosz, Szczepanska, Bouman, J. Gareth, Jager. Sensemaking of causality in agent-based models. International Journal of Social Research Methodology: Theory and Practice. 2022:1-12Metadata
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