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.

中文翻译：

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基于空间代理的模型的方法论问题

基于代理的建模（ABM）是一个标准工具，可用于许多学科。尽管人们对ABM的兴趣日益浓厚，但一系列方法论挑战阻碍了更广泛的采用，这些挑战从围绕基本工具的问题到为该方法提供更完整的概念基础的需求。经过几十年的发展，ABM对于许多学生，学者和决策者来说仍然难以开发和使用。对于设计用来表示广泛的人类，自然和人类环境系统的空间模式和过程的模型而言，这一困难尤为重要。在本文中，我们描述了空间ABM（SABM）的进一步开发和使用所面临的方法挑战，并提出了来自多个学科的一些潜在解决方案。我们首先定义SABM以缩小我们的调查对象，然后探讨空间是如何同时带来优势和挑战的。我们研究时间如何与模型中的空间相互作用，并深入研究一般模型开发以及模型框架和工具的问题。我们从具有ABM贡献历史的领域吸取教训和见解，包括经济学，生态学，地理学，生态学，人类学和空间科学，目的是为这种强大的建模方法确定有前途的方法。