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.
This research consists of three studies that apply a process control perspective to dynamic decision making. Study 1 investigates the features of decision strategies that affect performance. It finds that strategies perform well if they possess a strong mental model that accurately represents the decision problem or if they are well adapted to the problem environment. Based on these findings, Study 2 develops a machine learning approach to improve the mental model, and Study 3 develops an evolutionary approach to adapt decision strategies to a given environment. Both approaches are shown to be effective for constructing strategies with greater performance.