Future Developments in Geographical Agent-Based Models: Challenges and Opportunities.
Future Developments in Geographical Agent-Based Models: Challenges and Opportunities.
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DOI：
10.1111/gean.12267
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发表时间：
2021-01
影响因子：
3.6
通讯作者：
Batty M
中科院分区：
文献类型：
--
作者：
Heppenstall A;Crooks A;Malleson N;Manley E;Ge J;Batty M
尽管作为地理和社会科学的研究工具已被接受，但基于主体的模型仍然存在重大的方法论挑战。其中包括识别和模拟突发现象、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仍然具有巨大的前景，但该领域需要充分利用机器学习方法所提供的潜力，以使我们能够充分拓宽和加深对地理系统的理解。
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.
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影响因子：
3.1
作者：
An, Li
通讯作者：
An, Li
DOI：
10.1080/13658816.2010.495076
发表时间：
2011-01-01
影响因子：
5.7
作者：
Bone, Christopher;Dragicevic, Suzana;White, Roger
通讯作者：
White, Roger
影响因子：
3.6
作者：
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通讯作者：
Hatna, Erez
影响因子：
3.7
作者：
Dosi, Giovanni;Pereira, Marcelo C.;Virgillito, Maria Enrica
通讯作者：
Virgillito, Maria Enrica
DOI：
10.1111/j.1467-8306.2005.00450.x
发表时间：
2005-03-01
期刊：
ANNALS OF THE ASSOCIATION OF AMERICAN GEOGRAPHERS
影响因子：
--
作者：
An, L;Linderman, M;Liu, J
通讯作者：
Liu, J