Work

Sensemaking of causality in agent-based models

Year: 2022

Type: article

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.

Source: International Journal of Social Research Methodology

DOI: 10.1080/13645579.2022.2049510

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

Institutions: NORCE Research AS, UiT The Arctic University of Norway, University of Groningen, James Hutton Institute

Language: English

Topic: Philosophy and History of Science

Subfield: History and Philosophy of Science

Field: Arts and Humanities

Domain: Social Sciences

Open Access status: hybrid

Funders: Universitetet i Tromsø, Scottish Government, Norges Forskningsråd, H2020 European Research Council

Awards: G3477780734, G3774794136, G5228953455