Incentive Mechanism Design for Heterogeneous Crowdsourcing Using All-Pay Contests

Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers' types (e.g., abilities or costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions each for a potential winner. We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, called strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property.