Blake Riley | Econ @ UIUC

PhD Student
University of Illinois at Urbana-Champaign

briley2 at illinois.edu
blakejriley at gmail.com

Research Interests

Microeconomic theory, game theory, mechanism design, market design, collective decision making, information elicitation, prediction markets, scoring rules, statistical decision theory, two-sided matching markets.

Working Papers

Finding Wisdom in Biased Crowds via Peer-Prediction Mechanisms

This paper studies mechanisms for determining the true answer to a binary question using the opinions of biased agents. In particular, I study peer-prediction mechanisms, which ask agents to predict the opinions of others in addition to providing their own opinion. I first characterize all neutral, anonymous, and robustly incentive compatible mechanisms depending on the type of output. If the output of the mechanism is a real number, moderate improvements in mean squared error are available for dominance-solvable peer-prediction estimators over estimators that use only opinions. Alternatively, if a dominance solvable mechanism makes a decision between the two states, I show it is equivalent to majority rule. I also consider a simple decision rule based on the median prediction that substantially improves on majority rule and is strategically robust, despite not being fully incentive-compatible.
Current version (October 2014)

Minimum Truth Serums with Optional Predictions

In this paper, I introduce a class of mechanisms for eliciting private correlated signals from a group of expected score maximizers without external verification or knowledge about the agents’ belief structure. Built on proper scoring rules, these ask agents to report a signal and a prediction of the signals of others. If two agents with the same signal have the same expectations about the signals of others, the Bayesian incentive compatibility of these mechanisms follows with no further assumptions on the agents’ belief structure. With a slight modification, the mechanism is still feasible and incentive compatible when the prediction portion of the report is optional.
Current version (June 2014)

Uncoordinated Matching Markets (with Juan Fung)

We study decentralized, two-sided matching markets as an asychronous, uncoordinated proposal process. The standard focus of the literature is on stable matchings, such as those produced by the Gale-Shapley algorithm. Since in a decentralized setting we arguable should not expect to observe stable matchings, we investigate the typical outcomes of uncoordinated proposals. Though not stable, there is minimal overall welfare loss, with substantially more equitable outcomes between the long and short sides of the market compared to stable matchings. These results suggest there is little to gain from implementing formal matching procedures in markets that are not obviously failing.