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GMU C4I Center Seminar

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Combinatorial Prediction Markets by Graphical Model:
Algorithms and Auto-Traders

Dr. Wei Sun and Walter Powell

Friday, March 29, 2013 at 1:30 PM

Nguyen Engineering building, Room 4705

ABSTRACT

Prediction markets are defined as speculative markets created for the purpose of making predictions. The current market prices can be interpreted as estimates of the probability of the event, or the expected value of the parameter. Public prediction markets such as the Iowa Electronic Market or the Foresight Exchange have been in place for over two decades. More recently, Intrade, Inkling, and Betfair have been in the news.

All of these prediction markets ignore the relationships between questions, but combinatorial prediction markets explicitly consider and exploit dependencies among base events. This allow us to collect more information and promises better accuracy. A combinatorial market can integrate partial information from many people, and update a joint probability distribution that is far larger than any one person can fully edit or consider. However, we must tame the combinatorial explosion before the problem is beyond computers as well.

In this talk, we show how to use Bayesian networks to represent and update combinatorial markets -- including user assets. We also present results of a recent murder-mystery experiment where participants used either a regular prediction market or a combinatorial market to solve the mystery. Finally, in order to further improve the market's accuracy, we designed an auto-trader based on user's input and/or belief expressed as a Bayesian network fragment. We show results that simple auto-traders can encourage participation, and new work on a "Kelly Rule" auto-trader that finds optimal trades given a user's joint beliefs.

BIO

A Research Assistant Professor in the Center of Excellence in C4I at George Mason University, since August 2009, DR. WEI SUN is currently involved as a core researcher in a government funded research project called DAGGRE, which has awarded the GMU research team with more than $5 million dollars in research funding. An expert in Bayesian inference, Dr. Sun obtained his Ph.D. in Information Technology in 2007 and has developed several efficient inference algorithms for hybrid Bayesian networks. He has a rich experience in predictive modeling, probabilistic reasoning, nonlinear filtering, sampling methods and simulation. Applications of his research include sensor fusion, tracking, classification, forecasting, performance modeling, and recently prediction markets. Dr. Sun has published 20 technical papers in referred journals and prestigious conferences, and two book chapters.

WALTER POWELL is a Ph.D. candidate and Research Instructor in George Mason UniversityÕs C4I center. A retired naval officer, his is completing his doctoral research in the evaluation of the quality of decisions. As part of his research he has developed numerous experiments and evaluations that assessed the usefulness of various decisions support systems. As a Senior Research Engineer with RTSync, Inc., he consults on modeling and simulation projects for various governmental and industry entities.