Events at CERGE-EI

Kim Sarnoff

Princeton University, United States

Abstract: Many real-world inference problems unfold over time: employers learn about ability across tasks, consumers evaluate products through repeated use, and policymakers revise beliefs as new data arrive. Yet despite its ubiquity, research on dynamic updating has largely focused on a single implication of Bayesian reasoning: order independence. This paper experimentally tests a broader set of restrictions implied by Bayes’ rule, emphasizing both order independence and the previously unexamined property of prior sufficiency: the principle that the most recent posterior should serve as a sufficient statistic for past information. In a multi-period updating experiment with a rich set of parameters, participants repeatedly revise beliefs after receiving signals of varying strength and structure. Three main results emerge. First, only roughly a third display order dependence, overreacting to conflicting signals. Second, violations of prior sufficiency are widespread: beliefs formed sequentially tend to grow more extreme, and models assuming prior sufficiency, such as Grether (1980), fit poorly beyond the first update. Finally, the data indicate that participants process signals in aggregate, explaining prior sufficiency violations.

Full Text: The Structure of Sequential Updating