Small Profits and Quick Returns: A Practical SocialWelfare Maximizing Incentive Mechanism for Deadline-Sensitive Tasks in Crowdsourcing
This addresses the problem of inefficient incentives in deadline-sensitive crowdsourcing tasks for platforms and participants, but it is incremental as it builds on existing incentive mechanisms.
The paper tackles the problem of maximizing social welfare in crowdsourcing by modeling heterogeneous provider punctuality and task value depreciation, proposing an Expected Social Welfare Maximizing (ESWM) mechanism that runs in polynomial time. Simulation results show it achieves higher expected social welfare and platform utility by attracting more participants.
As the driving force of crowdsourcing is the interaction among participants, various incentive mechanisms have been proposed to attract sufficient participants. However, the existing works assume that all the providers always meet the deadline and the task value accordingly remains constant. To bridge the gap of such impractical assumption, we model the heterogeneous punctuality behavior of providers and the task value depreciation of requesters. Based on those models, we propose an Expected Social Welfare Maximizing (ESWM) mechanism that aims to maximize the expected social welfare in polynomial time. Simulation results show that our heuristic-based mechanism achieves higher expected social welfare and platform utility via attracting more participants.