%0 Journal Article %1 manson2020methodological %A Manson, Steven %A An, Li %A Clarke, Keith C. %A Heppenstall, Alison %A Koch, Jennifer %A Krzyzanowski, Brittany %A Morgan, Fraser %A O’Sullivan, David %A Runck, Bryan C %A Shook, Eric %A Tesfatsion, Leigh %D 2020 %I Journal of Artificial Societies and Social Simulation %J Journal of Artificial Societies and Social Simulation %K agent-based_simulation review spatial_simulations %N 1 %R 10.18564/jasss.4174 %T Methodological Issues of Spatial Agent-Based Models %U http://dx.doi.org/10.18564/jasss.4174 %V 23 %X 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.

@article{manson2020methodological, 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.}, added-at = {2024-01-13T18:04:59.000+0100}, author = {Manson, Steven and An, Li and Clarke, Keith C. and Heppenstall, Alison and Koch, Jennifer and Krzyzanowski, Brittany and Morgan, Fraser and O’Sullivan, David and Runck, Bryan C and Shook, Eric and Tesfatsion, Leigh}, biburl = {https://www.bibsonomy.org/bibtex/2b924613ee6916fa1e2068ae1e5b779a3/peter.ralph}, doi = {10.18564/jasss.4174}, interhash = {2020b15c3707a3818a7849843ad1ebc5}, intrahash = {b924613ee6916fa1e2068ae1e5b779a3}, issn = {1460-7425}, journal = {Journal of Artificial Societies and Social Simulation}, keywords = {agent-based_simulation review spatial_simulations}, number = 1, publisher = {Journal of Artificial Societies and Social Simulation}, timestamp = {2024-01-13T18:04:59.000+0100}, title = {Methodological Issues of Spatial Agent-Based Models}, url = {http://dx.doi.org/10.18564/jasss.4174}, volume = 23, year = 2020 }