Reputation-Aware Task Allocation for Human Trustees

Compared to automated entities, human trustees have two distinct characteristics: 1) they are resource constrained (with limited time and effort to serve requests), and 2) their utility is not linearly related to income. Existing research in reputation-aware task delegation did not consider these two issues together. This limits their effectiveness in humanagent collectives such as crowdsourcing systems. In this paper, we propose a distributed reputation-aware task allocation approach-RATA-NL-to address these issues simultaneously. It is designed to help an individual human trustee determine the optimal number of task requests to accept at each time step based on his situation to maximize his long term well-being. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of the trustees, and provides provable performance guarantees. RATA-NL has been compared with five stateof-the-art approaches through extensive simulations based on human task delegation behavior abstracted from a user study involving over 100 trustees for eight weeks. The results demonstrated significant advantages of RATA-NL, especially under high workload conditions.