Algorithmic Game Theory: Manipulation-Resistant Reputation Systems

This chapter is an overview of the design and analysis of reputation systems for strategic users. We consider three specific strategic threats to reputation systems: the possibility of users with poor reputations starting afresh (whitewashing); lack of effort or honesty in providing feedback; and sybil attacks, in which users create phantom feedback from fake identities to manipulate their own reputation. In each case, we present a simple analytical model that captures the essence of the strategy, and describe approaches to solving the strategic problem in the context of this model. We conclude with a discussion of open questions in this research area. 1.1 Introduction: Why are reputation systems important? One of the major benefits of the Internet is that it enables potentially beneficial interactions, both commercial and non-commercial, between people, organizations, or computers that do not share any other common context. The actual value of an interaction, however, depends heavily on the ability and reliability of the entities involved. For example, an online shopper may obtain better or lower-cost items from remote traders, but she may also be defrauded by a low quality product for which redress (legal or otherwise) is difficult. If each entity's history of previous interactions is made visible to potential new interaction partners, several benefits ensue. First, a history may reveal information about an entity's ability, allowing others to make choices about whether to interact with that entity, and on what terms. Second, an expectation that current performance will be visible in the future may deter moral hazard in the present, that hazard being the temptation to cheat or exert low effort. In other words, visible histories create an incentive to 4