Robust incentives via multi‐level Tit‐for‐Tat
Qiao Lian, Peng Yu, Mao Yang, Zheng Zhang, Yafei Dai, Xiaoming Li · Concurrency and Computation Practice and Experience · 2007
Abstract Much work has been done to address the need for incentive models in real deployed peer‐to‐peer networks. In this paper, we discuss problems found with the incentive model in a large, deployed peer‐to‐peer network, Maze. We evaluate several alternatives, and propose an incentive system that generates preferences for well‐behaved nodes while correctly punishing colluders. We discuss our proposal as a hybrid between Tit‐for‐Tat and EigenTrust, and show its effectiveness through simulation of real traces of the Maze system. Copyright © 2007 John Wiley & Sons, Ltd.