TRUTHFUL REPUTATION MECHANISMS FOR ONLINE SYSTEMS

The internet is moving rapidly towards an interactive milieu where online communities and economies gain importance over their traditional counterparts. While this shift creates opportunities and benefits that have already improved our day-to-day life, it also brings a whole new set of problems. For example, the lack of physical interaction that characterizes most electronic transactions, leaves the systems much more susceptible to fraud and deception. Reputation mechanisms offer a novel and effective way of ensuring the necessary level of trust which is essential to the functioning of any market. They collect information about the history (i.e., past transactions) of market participants and make public their reputation. Prospective partners guide their decisions by considering reputation information, and thus make more informative choices. Online reputation mechanisms enjoy huge success. They are present in most e-commerce sites available today, and are seriously taken into consideration by human users. The economical value of online reputation raises questions regarding the trustworthiness of mechanisms themselves. Existing systems were conceived with the assumption that users will share feedback honestly. However, we have recently seen increasing evidence that some users strategically manipulate their reports. This thesis describes ways of making online reputation mechanisms more trustworthy by providing incentives to rational agents for reporting honest feedback. Different kinds of reputation mechanisms are investigated, and for each, I present mechanisms for rewarding the agents that report truthfully. Problems related to collusion (i.e., several agents coordinate their strategies in order to manipulate reputation information) and robustness are also investigated. Moreover, this thesis describes a novel application of incentive compatible reputation mechanisms to the area of quality of service monitoring, and investigates factors that motivate and bias human users when reporting feedback in existing review forums.

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