Agent Mediated Consensus-Building for Environmental Problems: A Genetic Algorithm Approach

Abstract

"Environmental problems often result from the distributed and uncoordinated land use management practices of individual decision-makers that, when taken together, cause significant environmental impacts. To develop feasible and politically acceptable solutions to such problems it is often necessary to foster compromise and consensus among a diverse set of special interest groups who possess overlapping sets of objectives; some quantifiable, some not. The union of these sets forms a criteria space that constrains the set of feasible solutions that may be adopted by resource managers. As interest groups work toward the common goal of a mutually acceptable resource management plan they often require assistance in the development, representation, and analysis of this criteria space. Furthermore, new tools are needed that form explicit links between criteria space and the geographic space that is being managed. This paper illustrates how genetic algorithms are used to construct a link between criteria and geographic space and to evolve mutually acceptable solutions to complex environmental problems. Intelligent agents help decision-makers invoke various criteria and learn from the successes and failures of generated solutions in both geographic and criteria space. This knowledge is used to help users assess the fitness of alternative solutions and generate improved solutions to complex environmental problems."