Methodological Issues of Spatial Agent-Based Models
Steven M. Manson,Li An,Keith C. Clarke,Alison J. Heppenstall,Jennifer Koch,Brittany Krzyzanowski,Fraser Morgan,David O'Sullivan,Bryan C. Runck,Eric Shook,Leigh Tesfatsion +10 more
TL;DR: The methodological challenges facing further development and use of spatial ABM (SABM) are described and some potential solutions from multiple disciplines are suggested and how spatiality is a source of both advantages and challenges are explored.
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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.
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Challenges, tasks, and opportunities in modeling agent-based complex systems
Li An,Volker Grimm,Abigail Sullivan,Billie Turner,Nicolas Malleson,Alison J. Heppenstall,Christian E. Vincenot,Derek T. Robinson,Xinyue Ye,Jianguo Liu,Emilie Lindkvist,Wenwu Tang +11 more
TL;DR: This article reviews the advances of ABM in social, ecological, and socio-ecological systems, compares ABM with other traditional, equation-based models, provides guidelines for ABM novice, modelers, and reviewers, and point out the challenges and impending tasks that need to be addressed for the ABM community.
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Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities
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TL;DR: It is argued that although agent‐based models continue to have enormous promise as a means of developing dynamic spatial simulations, the field needs to fully embrace the potential offered by approaches from machine learning to allow us to fully broaden and deepen the authors' understanding of geographical systems.
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Spatial simulation: exploring pattern and process
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TL;DR: The state of the science about agent-based modelling (ABM) is reviewed, pointing out the strengths and weaknesses, and several impending tasks that warrant special attention in order to improve the science and application of ABM.
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