Modeling-in-the-middle: bridging the gap between agent-based modeling and multi-objective decision-making for land use change
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.
Document Type: Research Article
Affiliations: 1: Department of Biology,University of Alaska Anchorage, AnchorageAK, USA 2: Department of Geography,Simon Fraser University, BurnabyBritish Columbia, Canada 3: Department of Geography,Memorial University of Newfoundland, St. John'sNewfoundland, Canada
Publication date: May 1, 2011