Year: 2020
Type: article
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.A er 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.
DOI: 10.18564/jasss.4174
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,300
Awards: Modelling Individual Consumer Behaviour, Consumer Data Research Support Service (CDRSS), CDRC, Bringing the Social City to the Smart City, System-science Informed Public Health and Economic Research for Non-communicable Disease Prevention (the SIPHER Consortium), System-science Informed Public Health and Economic Research for Non-communicable Disease Prevention (the SIPHER Consortium)
OpenAlex ID: W3003765837