Date of Award
2018
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Graduate Group
Operations & Information Management
First Advisor
Uri Simonsohn
Abstract
Understanding decision making under uncertainty is crucial for researchers in the social sciences, policymakers, and anyone trying to make sense of another’s (or their own) choices. In this dissertation, my coauthors and I make three contributions to understanding preferences for uncertainty regarding (a) how preferences are measured, (b) how these preferences may (or may not) manifest in a consequential real-world context, and (c) how different types of advice influence opinions about uncertain events. In Chapter 1, we examine methods that researchers use to study preferences for uncertainty. We find that the presence of uncertainty is often confounded with the presence of “weird” transaction features, dramatically overstating the presence of uncertainty aversion in these experiments. In Chapter 2, we show that extreme uncertainty does not exist in the context of corporate experimentation, despite speculation by pundits and researchers. In fact, people judge experiments similarly to how they would judge simple gambles, with the experiment being judged near the “expected value” of the policies it implements. In Chapter 3, we find that the format in which uncertainty is presented impacts how people combine forecasts from multiple sources. Numeric probability forecasts are averaged, while verbal forecasts are combined additively, with people making more extreme judgments as they see additional forecasts.
Recommended Citation
Mislavsky, Robert, "Measuring Preferences For Uncertainty" (2018). Publicly Accessible Penn Dissertations. 2744.
https://repository.upenn.edu/edissertations/2744