Integrating Equity Considerations into Agent-Based Modeling: A Conceptual Framework and Practical Guidance

T.G. Williams

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Advancing equity is a complex challenge for society, science, and policy. Agent-based models are increasingly used as scientific tools to advance understanding of systems, inform decision-making, and share knowledge. Yet, equity has not received due attention within the agent-based modeling (ABM) literature. In this paper, we develop a conceptual framework and provide guidance for integrating equity considerations into ABM research and modeling practice. The framework conceptualizes ABM as interfacing with equity outcomes at two levels (the science-society interface and within the model itself) and the modeler as a filter and lens that projects knowledge between the target system and the model. Within the framework, we outline three complementary, equity-advancing action pathways: (1) engage stakeholders, (2) acknowledge positionality and bias, and (3) assess equity with agent-based models. For Pathway 1, we summarize existing guidance within the participatory modeling literature. For Pathway 2, we introduce the positionality and bias document as a tool to promote modeler and stakeholder reflexivity throughout the modeling process. For Pathway 3, we synthesize a typology of approaches for modeling equity and offer a set of preliminary suggestions for best practice. By engaging with these action pathways, modelers both reduce the risks of inadvertently perpetuating inequity and harness the opportunities for ABM to play a larger role in creating a more equitable future.
Original languageEnglish
Article number1
Number of pages26
JournalJournal of Artificial Societies and Social Simulation
Volume25
Issue number3
Early online date31 Mar 2022
DOIs
Publication statusPublished - 30 Jun 2022

Funding

TW was supported by funding from the Rackham Predoctoral Fellowship and is grateful to discussions at the Peach Street Writing Group, which provided the head space and original impetus for this paper. BM was supported by funding from the German Federal Ministry of Education and Research (BMBF) within the research project NamTip (01LC1821D) and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 817501 (BESTMAP). NM was supported by the U.S. National Science Foundation through the award INFEWS-1856054. The authors would also like to thank Gunnar Dressler and Ruchie Pathak for their assistance in preparing the positionality and bias documents.

FundersFunder number
National Science FoundationINFEWS-1856054
Horizon 2020 Framework Programme817501
Bundesministerium für Bildung und Forschung01LC1821D

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