Crowdsourcing with heterogeneous workers in social networks

Many online social networking platforms are leveraging crowdsourcing to enhance the user experience. These platforms seek to incentivize heterogeneous workers to exert efforts to complete tasks (e.g., moderation of posts and articles) and truthfully report their solutions. Output agreement mechanism (e.g., majority voting) is a common approach to this end. In an output agreement mechanism, a worker is rewarded according to whether his solution matches those of his peers. However, prior related work has not studied the workers' heterogeneous solution accuracy and how this heterogeneity affects the platform's payoff. We fill this void by modeling and analyzing the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for the majority voting to maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs. We show that as a worker's solution accuracy increases, he is more likely to exert effort and truthfully report his solution under the equilibrium reward mechanism. However, given a fixed total worker population, it is surprising that the platform's overall payoff does not monotonically increase in the number of high-accuracy workers. This is because a larger number of high-accuracy workers brings marginally decreasing benefit to the platform, but the rewards required to incentivize them may significantly grow. Moreover, we show that as the solutions of the high-accuracy workers become more accurate, the platform needs a smaller number of such workers to achieve the maximum payoff.