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Methodological Issues of Spatial Agent-Based Models

Steven Manson (), Li An (), Keith C. Clarke (), Alison Heppenstall (), Jennifer Koch (), Brittany Krzyzanowski (), Fraser Morgan (), David O'Sullivan (), Bryan C Runck (), Eric Shook () and Leigh Tesfatsion ()
Additional contact information
Steven Manson: http://www.stevenmanson.com
Li An: http://complexities.org/An/
Keith C. Clarke: http://www.geog.ucsb.edu/~kclarke
Alison Heppenstall: https://www.turing.ac.uk/people/researchers/alison-heppenstall
Jennifer Koch: http://landusechange.net
Brittany Krzyzanowski: http://brittanykrzyzanowski.weebly.com/
Fraser Morgan: https://www.landcareresearch.co.nz/about/people/staff-details?id=bW9yZ2FuZg
David O'Sullivan: http://southosullivan.com/geodos
Bryan C Runck: http://www.bryanrunck.com

Journal of Artificial Societies and Social Simulation, 2020, vol. 23, issue 1, 3

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.

Keywords: Spatial; Agent-Based Model; Methods; Human-Environment Systems (search for similar items in EconPapers)
Date: 2020-01-31
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