This paper introduces a model for analyzing marketplaces, such as eBay, which rely on binary reputation mechanisms for quality signaling and quality control. In our model sellers keep their actual quality private and choose what quality to advertise. The reputation mechanism is primarily used to determine whether sellers advertise truthfully. Buyers may exercise some leniency when rating sellers, which needs to be compensated by corresponding strictness when judging sellers' feedback profiles.

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... t is shown that, the more lenient buyers are when rating sellers, the more likely it is that sellers will find it optimal to settle down to steady-state quality levels, as opposed to oscillating between good quality and bad quality. Furthermore, the fairness of the market outcome is determined by the relationship between rating leniency and strictness when assessing a seller's feedback profile. If buyers judge sellers too strictly (relative to how leniently they rate) then, at steady state, sellers will be forced to understate their true quality. On the other hand, if buyers judge too leniently then sellers can get away with consistently overstating their true quality. An optimal judgment rule, which results in outcomes where at steady state buyers accurately estimate the true quality of sellers, is analytically derived. However, it is argued that this optimal rule depends on several system parameters, which are difficult to estimate from the information that marketplaces, such as eBay, currently make available to their members. It is therefore questionable to what extent unsophisticated buyers are capable of deriving and applying it correctly in actual settings.