Reputation-based policies that provide the right incentives in peer-to-peer environments

Peer-to-peer are popular environments for exchanging set-vices. A reputation mechanism is a proper means of discovering low-performing peers that fail to provide their services. In this paper, we present an in-depth and innovative study of how reputation can be exploited so that the right incentives for high performance are provided to peers. Such incentives do not arise if peers exploit reputation only when selecting the best providing peer; this approach may lead high-performing peers to receive unfairly low value from the system. We argue and justify experimentally that the calculation of reputation values has to be complemented by proper reputation-based policies that determine the pairs of peers eligible to interact with each other. We introduce two different dimensions of reputation-based policies, namely "provider selection" and "contention resolution", its well as specific policies for each dimension. We perform extensive comparative assessment of a wide variety of policy pairs and identify the most effective ones by means of simulations of dynamically varying peer-to-peer environments. We show that both dimensions have considerable impact on both the incentives for peers and the efficiency attained. In particular, when peers follow fixed strategies, certain policy pairs differentiate the value received by different types of peers in accordance to the value offered to the system per peer of each type. Moreover, when peers follow dynamic strategies, incentive compatibility applies under certain pairs of reputation-based policies: each peer is provided with the incentive to improve her performance in order to receive a higher value. Finally, we show experimentally that reputation values can be computed quickly and accurately by aggregating only a small randomly selected subset of the rating feedback provided by the peers, thus reducing the associated communication overhead. (c) 2005 Elsevier B.V. All rights reserved.