An Efficient and Truthful Pricing Mechanism for Team Formation in Crowdsourcing Markets
This addresses the challenge of forming cost-effective and reliable teams for complex tasks in crowdsourcing, though it appears incremental as it compares four mechanisms within a known framework.
The paper tackled the problem of selecting workers and determining payments for team formation in crowdsourcing markets, and found that the TruTeam mechanism is superior due to its computational efficiency and truthfulness, as confirmed by simulations.
In a crowdsourcing market, a requester is looking to form a team of workers to perform a complex task that requires a variety of skills. Candidate workers advertise their certified skills and bid prices for their participation. We design four incentive mechanisms for selecting workers to form a valid team (that can complete the task) and determining each individual worker's payment. We examine profitability, individual rationality, computational efficiency, and truthfulness for each of the four mechanisms. Our analysis shows that TruTeam, one of the four mechanisms, is superior to the others, particularly due to its computational efficiency and truthfulness. Our extensive simulations confirm the analysis and demonstrate that TruTeam is an efficient and truthful pricing mechanism for team formation in crowdsourcing markets.