Solving The Crowdsourcing Dilemma Using The Zero-Determinant Strategies

Crowdsourcing is a promising technology to accomplish a complex task via eliciting services from a large group of contributors. Recent observations indicate that the success of crowdsourcing has been threatened by the malicious behaviors of the contributors. In this paper, we analyze the attack problem using an iterated prisoner's dilemma (IPD) game and propose a reward-penalty expected payoff algorithm based on zero-determinant (ZD) strategies to reward a worker's cooperation or penalize its defection in order to entice the final cooperation. Both theoretical analysis and simulation studies are performed, and the results indicate that the proposed algorithm has the following two attractive characteristics: 1) the requestor can incentivize the worker to become cooperative without any long-term extra cost; and 2) the proposed algorithm is fair so that the requestor cannot arbitrarily penalize an innocent worker to increase its payoff even though it can dominate the game. To the best of our knowledge, we are the first to adopt the ZD strategies to stimulate both players to cooperate in an IPD game. Moreover, our proposed algorithm is not restricted to solve only the problem of crowdsourcing dilemma - it can be employed to tackle any problem that can be formulated into an IPD game.