Eliciting Information From Heterogeneous Mobile Crowdsourced Workers Without Verification

• DOI: 10.1109/tmc.2021.3062425
• Publication Date: 2021-02-26T20:53:20Z
• Authors: Chao Huang; Haoran Yu; Jianwei Huang; Randall A. Berry
• Keywords: Crowdsourcing; Stackelberg competition; Majority Rule; Stochastic game

In mobile crowdsourcing, platforms seek to incentivize heterogeneous workers complete tasks (e.g., road traffic sensing) and truthfully report their solutions. When cannot verify the quality of workers' solutions, crowdsourcing problem is known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information elicitation without verification</i> (IEWV). an IEWV problem, a platform needs provide incentives motivate high-quality solutions truthful reporting from workers. A common approach solve majority voting, where each worker rewarded according whether his solution matches majority's solution. However, previous work has not considered with accuracy. This unrealistic in many domains, one would expect differ judgment, expertise, reliability. Moreover, prior how this heterogeneity affects platform's tradeoff between cost achieving this. We address these gaps by studying interactions two-stage Stackelberg game. Stage I, chooses reward level for voting. II, decide effort levels strategies. show that worker's accuracy increases, he more likely, equilibrium, exert given fixed total population, surprisingly, payoff may decrease number high-accuracy further characterize value knowing terms improving optimal design maximizing its payoff. Knowing such information enables effective aggregation discriminatory policy Surprisingly, can improve both payoffs, hence social welfare.