GIS and Intelligent Agents for Multiobjective Natural Resource Allocation: A Reinforcement Learning Approach

An important component of natural resource management is determining how to allocate resources within a landscape to different stakeholders in a manner that satisfies multiple objectives. Developing decision making tools for assisting natural resource allocation is a challenging endeavor as stakeholders' objectives typically exist at varying spatial scales, their actions are defined by the spatial constraints in which they operate, and the spatial distribution of resources can be altered due to system disturbances. The nature of such challenges suggests the need for a geographic approach that can investigate these spatial complexities in order to generate a suitable set of solutions. The objective of this study is to develop and evaluate an Intelligent Agent Model for multiobjective natural resource allocation. The model integrates agent-based modeling in a GIS environment with reinforcement learning – a heuristic method for generating, evaluating, and improving multiobjective decision making solutions. The model is implemented by simulating a forest management scenario in which agents that represent forest companies learn how to harvest trees in a manner that maximizes economic return while minimizing the adverse ecological impact to the surrounding landscape. In addition, the model simulates forest disturbances of varying frequencies and intensities to determine how disturbance events affect the decision-making ability of agents. The model is validated to demonstrate that it can provide practical solutions to natural resource decision making.