Uncertainty and information: Biases, measurement, and regulation

This PhD thesis studies how people and institutions make decisions when the truth is uncertain and information is incomplete. In everyday life, beliefs are formed—about the economy, health risks, or other people’s intentions—and revised when new information arrives. Yet belief updating is often biased, and different biases can look similar in observed behavior while having very different underlying causes. The first chapter develops a framework to distinguish these mechanisms, which matters for designing effective interventions (for example in political polarization or financial decision-making). The second chapter introduces a simple survey tool that measures not only what people expect, but also how uncertain they feel. Applied to inflation expectations, this tool yields more consistent estimations than the standard approaches and is rated as easier and more engaging by respondents. The final chapter studies how institutional oversight and regulation shape which information-gathering procedures are permitted, and how approval processes affect the design of tests and investigations.