Cooperation Enhanced by Indirect Reciprocity and Spatial Reciprocity in Social P2P Reputation Systems

Abstract

The success of P2P systems ultimately depends on whether rational users will contribute their services. In five rules that Nowak generalized supporting cooperation, reciprocity mechanisms are usually utilized in P2P networks. As a tradition incentive mechanism, reputation based on indirect reciprocity is frequently designed to punish free-riders. Recent studies present social relationship among peers can be used to enhance the performances of P2P applications. In this paper we construct a spatial PD game with three strategies to investigate the impact of spatial reciprocity on the evolution of cooperation in reputation systems. In the practice case where learning environments differ from interaction environments, the structures of interaction and learning neighborhood have been discussed separately on ER networks. Simulation results demonstrate that the indirect mechanism by image score favors the cooperative behaviors among the structured population. In Particular, interaction network dramatically affects the stationary fractions of defectors. And learning networks take weaker impact on the evolution of cooperation. Similar conclusions have been drawn extensive simulations on regular random networks with different neighborhood size N and on BA scale-free networks as well. The current results are helpful to deeply understand the emergence of cooperative behaviors in social P2P systems.