Yang Liu: Machine-Learning Aided Incentive Design

Dartmouth Events

Yang Liu: Machine-Learning Aided Incentive Design

Incentive design is often viewed as an economics problem where the designer would like to “engineer” the rules of interactions....

Tuesday, May 9, 2017
4:30pm-5:30pm
Kemeny Hall 007
Intended Audience(s): Public
Categories: Lectures & Seminars

Abstract: Incentive design is often viewed as an economics problem where the designer would like to “engineer” the rules of interactions so that the behavior of strategic agents leads to some optimal outcome. For example, information elicitation without verification (IEWV) is a classic problem where a principal wants to design reward rules to truthfully elicit high-quality answers of some tasks (e.g. experience of a hotel stay) from strategic agents despite that she cannot evaluate the quality of agents’ contributions. This is also a prevailing problem due to the wide adoption of crowdsourcing. This talk focuses on some of our recent efforts on integrating machine learning techniques into the incentive design for IEWV. In particular, I’ll discuss how we leverage surrogate loss functions to design the reward rules as well as how we develop a bandits framework for aligning long-term incentives.

Bio:  Yang Liu is currently a postdoctoral fellow at Harvard University. He obtained his PhD degree from the Department of EE:Systems, University of Michigan Ann Arbor in 2015. Before went to Ann Arbor, he got a Bachelor degree from Shanghai Jiao Tong University, China in 2010. Then he obtained his Master of Science in EE:Systems and Mathematics in 2012 and 2014 respectively, both from University of Michigan. He is author and coauthor of several technical papers in top journals and conferences of IEEE/ACM/USENIX/AAAI. His research interests include developing learning theory and optimal decision making strategies towards acquiring and processing noisy data via human computation.

For more information, contact:
Sandra Hall

Events are free and open to the public unless otherwise noted.