Active learning with a misspecified prior
We study learning and information acquisition by a Bayesian agent whose prior belief is misspecified in the sense that it assigns probability 0 to the true state of the world. At each instant, the agent takes an action and observes the corresponding payoff, which is the sum of a fixed but unknown fu...
Main Authors: | , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
The Econometric Society,
2018-03-02T21:32:13Z.
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Subjects: | |
Online Access: | Get fulltext |
Summary: | We study learning and information acquisition by a Bayesian agent whose prior belief is misspecified in the sense that it assigns probability 0 to the true state of the world. At each instant, the agent takes an action and observes the corresponding payoff, which is the sum of a fixed but unknown function of the action and an additive error term. We provide a complete characterization of asymptotic actions and beliefs when the agent's subjective state space is a doubleton. A simple example with three actions shows that in a misspecified environment a myopic agent's beliefs converge while a sufficiently patient agent's beliefs do not. This illustrates a novel interaction between misspecification and the agent's subjective discount rate. National Science Foundation (U.S.) (Grant 1643517) |
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