Methodological issues of spatial agent-based models

Manson, S. et al. (2020) Methodological issues of spatial agent-based models. Journal of Artificial Societies and Social Simulation, 23(1), 3. (doi: 10.18564/jasss.4174)

Abstract

Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling.

• Item Type: Articles
• Additional Information: This work is supported in part by the United States National Science Foundation (NSF) Method, Measure & Statistics (MMS) and Geography and Spatial Sciences (GSS) programs (BCS #1638446); the National Institutes of Health (NIH)-supported Minnesota Population Center (R24 HD041023); NIH National Spatiotemporal Population Research Infrastructure (2R01HD057929-11); ESRC-Alan Turing Fellowship (ES/R007918/1); and the New Zealand Ministry of Business, Innovation and Employment (C09X1307).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heppenstall, Professor Alison
• Authors: Manson, S., An, L., Clarke, K. C., Heppenstall, A., Koch, J., Krzyzanowski, B., Morgan, F., O'Sullivan, D., Runck, B. C., Shook, E., and Tesfatsion, L.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Journal of Artificial Societies and Social Simulation
• Publisher: SimSoc Consortium
• ISSN: 1460-7425
• ISSN (Online): 1460-7425
• Copyright Holders: Copyright © 2019 The Author(s)
• First Published: Journal of Artificial Societies and Social Simulation 23(1):3
• Publisher Policy: Reproduced under a creative commons licence