Using Truth Detection to Incentivize Workers in Mobile Crowdsourcing

Mobile crowdsourcing platforms often want to incentivize workers to finish tasks with high quality and truthfully report their solutions by providing proper rewards. Most existing incentive mechanisms reward workers based on the comparison among workers' reported solutions. However, these mechanisms are vulnerable to worker collusion, i.e., workers coordinate to misreport their solutions. We address such an issue by proposing a novel rewarding mechanism based on a truth detection truthdetection technology, which relies on the independent verification of the correctness of each worker's response to some question with an imperfect accuracy. We model the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform optimizes the reward mechanism parameters associated with truth detection to maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs (which depend on the output of the truth detector). We analyze the game's equilibrium and show that our proposed mechanism can effectively mitigate worker collusion. We also propose a novel rule, named filtered majority, for the platform to more effectively aggregate the workers' solutions. Our proposed aggregation rule utilizes truth detection and outperforms the conventional simple majority rule. We further characterize the impact of the truth detection accuracy on the platform's decisions. Surprisingly, under the simple majority rule, we show that as the truth detection accuracy improves, the platform should always incentivize more workers to exert effort and truthfully report. However, under our proposed filtered majority rule, we show that as the truth detection accuracy improves, in some cases, the platform should incentivize fewer workers and save costs. We further examine the impact of the workers' imperfect estimation of the truth detection accuracy on the platform's decisions.