Non-Bayesian Optimal Search and Dynamic Implementation
نویسندگان
چکیده
We show that two non-Bayesian learning procedures lead to very permissive implementation results concerning the e¢ cient allocation of resources in a dynamic environment where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of agents values. This contrasts the rather restrictive results that have been obtained for Bayesian learning in the same environment.
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