Agent-based models as laboratories for spatially explicit planning policies

Agent-based modeling and simulation (ABMS) has been a part of geospatial sciences for over a decade. Most research activities so far have concentrated on either extending complexity theory to spatially explicit phenomena, or on designing computational models and software tools. Only a few of these activities have focused on using ABMS for spatially explicit modeling of real-world policy scenarios. In this paper we present a realistic application of ABMS to simulating alternative futures for a small community in Washington State, USA. We develop an ABMS assessment benchmark that comprehensively covers diverse aspects of a good operational agent-based model. Using an ABMS software tool-CommunityViz Policy Simulator-we generate future development scenarios in the municipality of Chelan, WA based on the County and the City Comprehensive Growth Plans. Simulation results are compared with Washington State projections for growth-management planning. The indication of the highest probability locations of urban growth in the studied community is crucial for environmental and economic planning and decisionmaking. Endangered salmon protection and recreational and retirement influxes of people from the Puget Sound metropolitan area have a direct impact on future growth of the region. The bottom-up microsimulation allows for interposition of individual decisions and actions into forecasting option generation. The 'heterogeneity, adaptability, and tractability' benchmark is instrumental in evaluating CommunityViz Policy Simulator and outlining possible challenges for future development of applied agent-based models.