A genetic algorithm approach to policy design for consequence minimization

We characterize the policy design problem as an event network where a series of interrelated decisions are made in a sequential fashion. As such, each node represents a decision point and determines the next arc to be connected in the network. A key objective in designing new policies is the minimization of negative outputs (consequence minimization). To address this type of problem we have applied a genetic algorithm to generate multiple network configurations (policy alternatives) for evaluation by human decision-makers. Our approach differs from typical genetic algorithms because decision-maker participation has been intimately linked to the genetic search so that policies designed will simultaneously meet the objectives for which they have been designed and remain acceptable and implementable in practice. In this paper we construct a solid waste management example to illustrate the usefulness of our approach to policy design.