Optimal Windows for Aggregating Ratings in Electronic Marketplaces

Abstract

Aseller in an online marketplace with an effective reputation mechanism should expect that dishonest behavior results in higher payments now whereas honest behavior results in a better reputation—and thus higher payments—in the future. We study the Window Aggregation Mechanism, a widely used class of mechanisms that shows the average value of the seller's ratings within some fixed window of past transactions. We suggest approaches for choosing the window size that maximizes the range of parameters for which it is optimal for the seller to be truthful. We show that mechanisms that use information from a larger number of past transactions tend to provide incentives for patient sellers to be more truthful but for higher-quality sellers to be less truthful.

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