Working Paper: CEPR ID: DP15477
Authors: Christoph Becker; Tigran Melkonyan; Eugenio Proto; Andis Sofianos; Stefan Trautmann
Abstract: Bayesian Updating is the dominant theory of learning in economics. The theory is silent about how individuals react to events that were previously unforeseeable or unforeseen. Recent theoretical literature has put forth axiomatic frameworks to analyze the unknown. In particular, we test if subjects update their beliefs in a way that is consistent reverse Bayesian, which ensures that the old information is used correctly after an unforeseen event materializes. We find that participants do not systematically deviate from reverse Bayesianism, but they do not seem to expect an unknown event when this is reasonably unforeseeable, in two pre-registered experiments that entail unforeseen events. We argue that participants deviate less from the reverse Bayesian updating than from the usual Bayesian updating. We provide further evidence on the moderators of belief updating.
Keywords: reverse bayesianism; unforeseen; unawareness; bayesian updating
JEL Codes: C11; C91; D83; D84
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Unforeseen events (G14) | belief updating (D83) |
| Participants maintain probability ratios (C11) | Reverse bayesian updating (C11) |
| Foreseeability of events (G14) | belief updates (D83) |
| Less deviation from reverse bayesian updating (C11) | Traditional bayesian updating (C11) |