Mechanism Design for Cross-Market Task Crowdsourcing

Crowdsourcing is a new paradigm which divides work between participants to achieve a cumulative result. To achieve good service quality for a crowdsourcing system, incentive mechanisms are necessary to attract more workers to participate. Most of existing mechanisms apply only for the crowdsourcing scenario where the crowdsourcer will employ the workers to perform certain tasks only in a single market. In this paper, we consider that the crowdsourcer would like to allocate tasks to several different sub-markets with a shared budget. The crowdsourcer wants to maximize her utility through the crowdsourcing campaign with certain budget constraint. We design two optimization objectives for our problem, maximin and weighted average. We propose a cross-market profit extract mechanism based on random sampling. Furthermore, we extend our algorithm to the online condition, and give the performance lowerbound for both offline and online cases for the cross-market crowdsourcing system. We also conduct extensive numerical experiments to demonstrate the effectiveness of our approach.