Dynamic Decision Making
Presenter
Associate Professor of Technology and Operations
Ross School of Business
University of Michigan
Time and location
North Quad 4330, Wednesday (1:00-2:00) pm
Abstract
Many decisions in economics and operations management are dynamic decision problems - decisions made sequentially with new information revealed and payoffs shaped by past decisions. Identifying the optimal decision strategy (e.g. using dynamic programming) is quite complicated for a human decision maker. We identify a set of behaviorally plausible simplifying heuristics that can be applied across a range of dynamic decision problems. We then conduct experiments using four distinct tasks: technology adoption, resource allocation, price search and a stopping problem. We identify which decision heuristics are commonly adopted, and whether they are common across tasks. We find that subjects tend to use simple target- and rule-based heuristics for search and other stopping problems, while using more sophisticated forward-looking and state-contingent heuristics for other decision tasks.