Truthful Profit Maximization Mechanisms for Mobile Crowdsourcing

Abstract

Crowdsourcing is an effective tool to allocate tasks among workers to obtain a cumulative outcome. Algorithmic game theory is widely used as a powerful tool to ensure the service quality of a crowdsourcing campaign. By this paper, we consider a more general optimization objective for the budget-free crowdsourcer, profit maximization, where profit is defined as the difference between the benefit obtained by crowdsourcer and payments to workers. Based on the framework of random sampling and profit extraction, we proposed a strategy-proof profit-oriented mechanism for our problem, which also satisfies computational tractability and individual rationality and has a performance guarantee. We also extend the profit extract algorithm to the online case through a two-stage sampling. Also, we study the setting in which workers are not trusted, and untrustworthy workers would infer others’ true type. For untrustworthy workers, we introduce a differentially private mechanism, which also has desired properties. Finally, we will conduct numerical simulations to show the effectiveness of our proposed profit maximization mechanisms. By this work, we enrich the class of competitive auctions by considering a more general optimization objective and a more general demand valuation function.