Negotiating a Future that is not like the Past

Elsenbroich, Corinna; Badham, Jennifer

Abstract

Agent-based models combine data and theory during both development and use of the model. As models have become increasingly data driven, it is easy to start thinking of agent-based modelling as an empirical method, akin to statistical modelling, and reduce the role of theory. We argue that both types of information are important in modelling dynamic complex systems, where the past is not a reliable blueprint for the future. By balancing theory and data, agent-based modelling is a tool to describe plausible futures, that we call “justified stories”. We conclude that this balance must be maintained if agent-based models are to serve a useful decision support role for policy makers.

Citation

Elsenbroich, C., & Badham, J. (2023). Negotiating a Future that is not like the Past. International Journal of Social Research Methodology, 26(2), 207-213. https://doi.org/10.1080/13645579.2022.2137935

• Journal Article Type: Article
• Acceptance Date: Sep 1, 2022
• Online Publication Date: Nov 4, 2022
• Publication Date: 2023
• Deposit Date: Oct 14, 2022
• Publicly Available Date: Mar 22, 2023
• Journal: International Journal of Social Research Methodology
• Print ISSN: 1364-5579
• Electronic ISSN: 1464-5300
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 2
• Pages: 207-213
• DOI: https://doi.org/10.1080/13645579.2022.2137935
• Public URL: https://durham-repository.worktribe.com/output/1189008