Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems

Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task’s requester, and may be adjusted based on the quality of the completed work, for example, through the use of “bonus” payments. In this paper, we study the requester’s problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each “arm” representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contrac...

• Type: Journal
• Year: 2016
• Where: JAIR
• Authors: Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
• Keywords: JAIR 2016, Software Engineering