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α: calibrated so average coauthorship-adjusted count equals average raw count
We show that a non-Bayesian learning procedure leads to very permissive implementation results concerning the efficient 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, and highlights the role of the learning procedure in dynamic mechanism design problems.