2014 Poster Sessions : Incentives in Crowdsourcing Markets with Heterogeneous Tasks

Student Name : Afshin Nikzad
Advisor : Amin Saberi
Research Areas: Information Systems
Abstract:
Designing optimal pricing policies and mechanisms for allocating tasks to workers is central to the online crowdsourcing markets. In this paper, we consider the following realistic setting of online crowdsourcing markets - we are given a heterogeneous set of tasks requiring certain skills; each worker has certain expertise and interests which define the set of tasks she is interested in and willing to do. Given this information, we design two mechanisms for allocation of tasks to workers, while ensuring budget feasibility, efficiency and incentive-compatibility. Apart from strong theoretical guarantees, we carry out extensive experimentation using simulations on a realistic case study of Wikipedia translation project using Mechanical Turk workers. Our results demonstrate the practical applicability of our mechanisms for realistic crowdsourcing markets on the web.


Bio:
Afshin Nikzad is a second year PhD student at Stanford. He is interested in market design and auction design, particularly in matching markets. My advisor is Amin Saberi.