RIMBED: Recommendation Incentive Mechanism Based on Evolutionary Dynamics in P2P Networks

Abstract

In autonomous environment (such as P2P, ad hoc, social networks and so on), all the rational individuals make independent decisions to maximize their profits. However, many interactions among individuals can be modeled as Prisoner's Dilemma game, which suppresses the emergence of cooperation. In order to provide scalable and robust services in such systems, incentive mechanisms need to be introduced. In this paper, we propose a novel incentive mechanism called recommendation incentive mechanism based on evolutionary dynamics(RIMBED). In our RIMBED system, players who pay an additional cost for recommendation service not only can get the information of the opponents, but also can have a higher probability to interact with cooperative individuals. Using the replicator dynamics equations in evolutionary game theory, we mathematically analyze the robustness and effectiveness of our RIMBED system. Meanwhile, simulation experiments can also validate our mathematical analysis. In our RIMBED system, players have three alternative strategies: always cooperative(ALLC), always defective(ALLD) and rational cooperative(RC). No one strategy can dominate the others forever and all the three strategies can survive in our system. When we bring in population invasion and a small mutation, our system can still work at an excellent level.