Modeling-in-the-middle: bridging the gap between agent-based modeling and multi-objective decision-making for land use change
Modeling-in-the-middle: bridging the gap between agent-based modeling and multi-objective decision-making for land use change
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DOI：
10.1080/13658816.2010.495076
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发表时间：
2011-01-01
影响因子：
5.7
通讯作者：
White, Roger
中科院分区：
文献类型：
--
作者：
Bone, Christopher;Dragicevic, Suzana;White, Roger
有一系列方法可用于调查土地利用变化并提供解决办法。根据土地利用变化的建模和分析方法，这些方法可以大致分为“自上而下”或“自下而上”的方法。虽然近年来有很多研究在理论和实际应用中推进了这些技术的使用，结合自上而下和自下而上的方法来增强土地利用变化建模的关注很少。本研究的目的是通过弥合基于主体的建模的自下而上的模拟和多目标决策的自上而下的分析能力来解决文献中的这一差距。通过一种称为强化学习（RL）的启发式建模方法进行。一个模型的开发，代表家庭和商业企业的计算机代理选择居住的人口密度和吸引力偏好的基础上的位置。由这些动态产生的土地利用变化由一组代表不同利益相关者的代理进行评估，这些利益相关者嵌入了RL算法，使他们能够影响土地利用变化过程，从而解决他们的目标。研究结果表明，自下而上和自上而下的模型之间的桥梁导致协商的土地利用模式，其中所有个人的愿望和目标受到他人行为的约束。这项研究表明，一个运动走向“建模在中间”的方法是可取的，以纳入真实的，但相互冲突的力量，塑造土地利用变化，很少被认为是一致的。
A spectrum of methods exists for investigating and providing solutions for land use change. These methods can be broadly categorized as either 'top-down' or 'bottom-up' approaches according to how land use change is modeled and analyzed. Although there has been much research in recent years advancing the use of these techniques for both theoretical and practical applications, integrating top-down and bottom-up approaches for enhancing land use change modeling has received minimal attention.The objective of this study is to address this gap in the literature by bridging the bottom-up simulation of agent-based modeling and the top-down analytical capabilities of multi-objective decision-making by means of a heuristic modeling approach called reinforcement learning (RL). A model is developed in which computer agents representing households and commercial enterprises select locations to inhabit based on population densities and attractivity preferences. The land use change resulting from these dynamics is evaluated by a set of agents representing different stakeholders who are embedded with RL algorithms that allow them to influence the land use change process so that their objectives are addressed. The results demonstrate that bridging bottom-up and top-down models leads to negotiated land use patterns in which the desires and objectives of all individuals are constrained by behaviors of others. This study suggests that a movement toward a 'modeling-in-the-middle' approach is desirable to incorporate the real yet conflicting forces that shape land use change and that are rarely considered in unison.