Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent‐based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, and calibration and validation. While advances in individual‐level data and computing power have opened up new research avenues, they have also brought with them a new set of challenges. This article reviews some of the challenges that the field has faced, the opportunities available to advance the state‐of‐the‐art, and the outlook for the field over the next decade. We argue 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 our understanding of geographical systems.

中文翻译：

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基于地理主体的模型的未来发展：挑战与机遇

尽管作为跨地理和社会科学的研究工具已达到一定程度，但基于代理的模型仍然存在重大的方法论挑战。这些包括识别和模拟紧急现象、代理表示、行为规则的构建以及校准和验证。虽然个人数据和计算能力的进步开辟了新的研究途径，但它们也带来了一系列新的挑战。本文回顾了该领域面临的一些挑战、推进最先进技术的机会以及该领域未来十年的前景。我们认为，尽管基于代理的模型作为开发动态空间模拟的一种手段继续具有巨大的前景，