Tradeoffs in Uncertain Decision Making

Abstract

Abstract In modern workplaces, managers and employees are increasingly expected to make decisions under uncertainty. Theories of organizational behavior stand to benefit by incorporating an understanding of the information-processing mechanisms that underlie human decision making under uncertainty. Computational cognitive models specify and test fundamental principles of how humans learn and make decisions under uncertainty. Crucially, they provide information about ubiquitous tradeoffs that humans face when making uncertain decisions. Understanding such tradeoffs can facilitate productivity and mitigate human errors. In this chapter, the authors review insights provided by computational cognitive models into human information processing under uncertainty. The first half of the review focuses on state uncertainty, that is, uncertainty in the human’s knowledge of what state their environment is in. The authors review influential decision-making models that describe how humans resolve uncertainty in order to act. The second half of the review focuses on reward uncertainty, that is, uncertainty about the rewards associated with actions. The authors review influential accounts that describe learning under reward uncertainty, and the potential tradeoffs associated with such learning. The authors conclude by discussing an example in which a tradeoff identified by cognitive science was integrated into a theory of organizational behavior.