Article,
Methodological Issues of Spatial Agent-Based Models
Journal of Artificial Societies and Social Simulation, (2020)
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 ap-
proach. After several decades of progress, ABMs remain difficult to develop and use for many students, schol- ars,
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 de- scribe
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 eco- nomics, ecology,
geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for
this powerful means of modeling.
