Crowdsourcing is a new distributed problem solving pattern brought by the Internet. However, intrinsic incentive problems reside in crowdsourcing applications as workers and requester are selfish and aim to maximize their own benefit. In this paper, the following key contributions are made. A reputation-based incentive model is designed using repeated game theory, based on thorough analysis for current research on reputation and incentive mechanism; and a punishment mechanism is established to counter selfish workers. The experiment results show that the new established model can efficiently motivate the rational workers and counter the selfish ones. By setting punishment parameters appropriately, the overall performance of crowdsourcing system can be improved up to 90%, even if the fraction of selfish workers is 20%.
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