Past Events

Qualifying Exam

Characterizations of Experts Algorithms That Are Incentive Compatible

 

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Thursday, May 05, 2022, 10:00am - 11:30am
 

Speaker: Chun Lau

Location : Via Zoom

Committee: 

David Pennock (advisor)

Sepehr Assadi

Yongfeng Zhang

Desheng Zhang

Event Type: Qualifying Exam

Abstract: Experts algorithms are online learning algorithms that combine the predictions of multiple experts to produce an aggregate prediction. These algorithms have strong accuracy guarantees: the loss of the aggregate is at most a small amount more than the loss of the best expert. Most analyses assume that the experts volunteer their best information completely and truthfully. Experts are treated like thermometers reporting temperatures, without any agency of their own. Recently, authors have explored new algorithms that take the experts’ motivations into account. They treat experts as rational, not altruistic: experts maximize their own influence on the algorithm and thus their own reputations as forecasters. In this paper, we look at many standard expert algorithms and ask what happens if experts act strategically. We prove several sufficient conditions under which standard expert algorithms are incentive compatible, meaning that the expert’s best strategy is to report their true belief about the realization of each event. Some conditions lead to incentive compatibility in the limit of the number of experts, while other more specific conditions produce incentive compatibility regardless of the number of experts and across all external parameters on the expert algorithms. We illustrate our main results through simulation.

Organization: 

Rutgers University School of Arts and Sciences

Contact  David Pennock