Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus
Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus
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DOI：
10.3390/land9120519
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发表时间：
2020-12
期刊：
影响因子：
3.9
通讯作者：
N. Magliocca
中科院分区：
文献类型：
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作者：
N. Magliocca
食物、能源和水系统(FEWS)的关系已成为一个突出的研究主题，也是一个紧迫的社会和政策挑战。计算建模是解决这些挑战的关键工具，FEWS建模作为一个子领域现在已经建立。然而，在FEWS建模和研究中，FEWS关系问题的社会维度，如个人或社会学习、技术采用决策和适应行为，仍然相对不发达。近年来，为了更好地表示人类行为并将其整合到FEWS研究中，基于代理的模型(ABM)得到了越来越多的使用。一项系统的审查确定了29篇文章，其中至少两个食品、能源或水部门明确地用ABM和/或ABM耦合的建模方法进行了考虑。代理人的决策和行为从被动到主动，主要受经济目标的驱使，本质上是多标准的，并由基于个人的实体实施到高度聚合的实体。然而，很大一部分模型不包含代理交互，或者没有基于现有的行为理论进行代理决策。由于数据限制、与其他仿真模型耦合的结构要求或应用的空间和/或时间尺度的强加，模型设计选择导致代理表示缺乏明确的决策过程或社会交互。相比之下，还注意到一些方法创新，这些创新是由于与开发多尺度、跨部门模式有关的挑战而促成的。基于这些发现，提出了未来在FEWS研究中使用ABM进行研究的几个途径。经过审查的ABM应用程序代表着进步，但在FEWS环境中仍有许多机会进行更丰富的基于行为的基于代理的建模。
The nexus of food, energy, and water systems (FEWS) has become a salient research topic, as well as a pressing societal and policy challenge. Computational modeling is a key tool in addressing these challenges, and FEWS modeling as a subfield is now established. However, social dimensions of FEWS nexus issues, such as individual or social learning, technology adoption decisions, and adaptive behaviors, remain relatively underdeveloped in FEWS modeling and research. Agent-based models (ABMs) have received increasing usage recently in efforts to better represent and integrate human behavior into FEWS research. A systematic review identified 29 articles in which at least two food, energy, or water sectors were explicitly considered with an ABM and/or ABM-coupled modeling approach. Agent decision-making and behavior ranged from reactive to active, motivated by primarily economic objectives to multi-criteria in nature, and implemented with individual-based to highly aggregated entities. However, a significant proportion of models did not contain agent interactions, or did not base agent decision-making on existing behavioral theories. Model design choices imposed by data limitations, structural requirements for coupling with other simulation models, or spatial and/or temporal scales of application resulted in agent representations lacking explicit decision-making processes or social interactions. In contrast, several methodological innovations were also noted, which were catalyzed by the challenges associated with developing multi-scale, cross-sector models. Several avenues for future research with ABMs in FEWS research are suggested based on these findings. The reviewed ABM applications represent progress, yet many opportunities for more behaviorally rich agent-based modeling in the FEWS context remain.