Methodological Issues of Spatial Agent-Based Models

Steven Manson,Brittany Krzyzanowski,Alison Heppenstall,Jennifer Koch,Keith C Clarke,Li An,David O'Sullivan,Bryan C Runck,Eric Shook,Leigh Tesfatsion,Fraser Morgan

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.

Highlights

This paper identifies and structures key methodological challenges in the development and use of spatial Agent based modeling (ABM) and o ers potential solutions from many disciplines
We examine the various challenges of including space within ABMs, including the various ways of understanding and representing space, and look to geography and its concepts of scale for insight in dealing with spatiality
O’Sullivan et al ( ) described the ideal characteristics of a system suited to ABM, including heterogeneity of the decision-makers and context of agents; importance of interaction e ects among agents and their environment; and being medium-sized in terms of numbers of components, threading the needle between being too large and complicated for mathematical tractability and too small for statistical averaging

Summary

Introduction

. ABMs o en represent the environment as spatial, including models without a geographic representation of space but possessing agents with coordinate locations. Regardless of conceptualization or data representation, there is an ongoing need to more clearly define the role that distance plays on agent decision making, especially since it can be measured in so many ways, including Euclidian distance, cost, perceptual distance, travel time, and network distance (Illenberger et al ).

Results

Conclusion