Evolving decision strategies for dynamic environments: A genetic programming approach

A decision strategy is systematic way of choosing among alternatives or eliminating options in order to arrive at a goal. Individuals apply decision strategies in dynamic environments that require repeated decision making where decisions are path-dependent, time-constrained, and the environment changes not only in response to the actions taken by the decision maker but also autonomously. In addition to being used by individual agents, decision strategies are found in organizations in the form of policies, guidelines, and algorithms. In this paper, we consider decision strategies (specifically in the context of chronic disease care) as the product of an evolutionary process and outline an approach to develop strategies by means of a computational process based on genetic programming. By evolving strategies in a simulated environment, we find that the evolutionary process is capable of developing strategies comparable to those found in practice. Moreover, the proposed approach can serve as a useful tool for analyzing various incentive structures and identifying their potential unintended consequences.