Yiling Chen, Harvard University

Abstract

Prediction markets elicit and aggregate beliefs by paying agents based on how close their predictions are to a verifiable future outcome. However, many important questions involve outcomes that are difficult or impossible to verify. This includes questions about causal effects where running randomized trials is infeasible or unethical, as well as those posed over long time horizons where delayed outcomes distort agents’ incentives to report truthfully. We present a result showing that it is possible to run an ε−incentive compatible prediction market to elicit and efficiently aggregate information from a pool of agents without observing the outcome by paying agents the negative cross-entropy between their prediction and that of a carefully chosen reference agent. Our key insight is that a reference agent with access to more information can serve as a reasonable proxy for the ground truth. We use this insight to propose self-resolving prediction markets that terminate with some probability after every report and pay all but a few agents based on the final prediction. We show that it is an ε−Perfect Bayesian Equilibrium for all agents to report truthfully in our mechanism.

This talk is based on joint work with Siddarth Srinivasan and Ezra Karger. 

Bio: 
Yiling Chen is a Gordon McKay Professor of Computer Science at Harvard University. Her research sits at the intersection of computer science, economics, and other social sciences, with a focus on the social dimensions of computational systems. Her work has earned best paper awards at conferences, including ACM EC, AAMAS, ACM FAT* (now ACM FAccT), and ACM CSCW. She has co-chaired several major conferences, including WINE’13, EC’16, HCOMP’18, and AAAI-23, and served as an associate editor for multiple journals.