Informativeness and Incentive Compatibility for Reputation Systems
نویسنده
چکیده
Reputation systems, which rank agents based on feedback from past interactions, play a crucial role in aggregating and sharing trust information online. Reputation systems are used to find authoritative web sites and ensure socially beneficial behavior on auction sites. The main problem faced by reputation system researchers is a lack of good metrics for comparison and evaluation. This thesis defines a novel “informativeness” metric for reputation systems which happens to approximate a crucial economic efficiency metric. This is then applied to the problem of finding optimal reputation systems. We show empirically that this metric enables meaningful comparisons between reputation systems, and present a technique for generating hybrid reputation systems with variable incentive-compatibility and efficiency properties.
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