DIMACS Workshop on Markets as Predictive Devices (Information Markets)

February 2-4, 2005
DIMACS Center, CoRE Building, Rutgers University

Robin Hanson, George Mason University,
John Ledyard, California Institute of Technology,
David Pennock, Yahoo! Research Labs,
Presented under the auspices of the Special Focus on Computation and the Socio-Economic Sciences, and the following sponsors:

Microsoft Research: http://research.microsoft.com

Newsfutures: http://www.newsfutures.com Hosting PM2, the Prediction Market Market

Yahoo! Research Labs: http://research.yahoo.com

For decades, economists have studied an astonishing "side effect'' of financial and wagering markets: their ability to serve as highly accurate forecasting devices. This workshop aims to explore the use of markets as a substitute for, or complement to, more traditional forecasting tools. We will examine how information flows from traders to the market and back again, how market mechanisms process information, how market prices communicate information and forecasts, and what mechanisms best foster accurate and statistically-testable predictions. The workshop will bring together researchers and practitioners from a variety of relevant fields, including economics, finance, computer science, and statistics, in both academia and industry, to discuss the state of the art today, and the challenges and prospects for tomorrow.

A market designed from the outset for information gathering and forecasting is called an information market. Information markets can be used to elicit a collective estimate of the expected value or probability of a random variable, reflecting information dispersed across an entire population of traders. The market prediction is not usually an average or median of individual opinions, but is a complex summarization reflecting the game-theoretic interplay of traders as they obtain and leverage information, and as they react to the actions of others obtaining and leveraging their own information, etc. In the best case scenario, the market price reflects a forecast that is a perfect Bayesian integration of all the information spread across all of the traders, properly accounting even for redundancy. This is the equilibrium scenario called rational expectations in the economics literature, and is the assumption underlying the strong form of the efficient markets hypothesis in finance.

The degree to which market forecasts approach optimality in practice, or at least surpass other known methods of forecasting, is remarkable. Supporting evidence can be found in empirical studies of options markets [Jackweth and Rubinstein (1996)], commodity futures markets [Roll (1984)], political stock markets [Forsythe, Rietz, and Ross (1999)] sports betting markets [Williams (1999)], horse racing markets [Thaler, R.H., and Ziemba (1988)], market games [Pennock, Lawrence, Giles, and Nielsen (2001)], laboratory investigations of experimental markets [Plott and Shyam (1988)], and field tests [Chen and Plott (2002)]. In nearly all these cases, to the extent that the financial instruments or bets are tied to real-world events, market prices reveal a reliable forecast about the likely unfolding of those events, often beating expert opinions or polls.

Despite a growing experimental literature, many questions remain regarding how best to design, deploy, analyze, and understand information markets, including both technical challenges (e.g., designing combinatorial exchanges [Bossaerts, Fine, and Ledyard (2002), Fortnow, Kilian, Pennock, and Wellman (2003), Hanson (2003)] and social challenges (e.g., overcoming legal and ethical concerns). The search for answers will benefit from input from economists (including specialists in mechanism design, experimental economics, financial markets, wagering markets, and rational expectations theory), statisticians and decision theorists (including experts in forecasting, belief aggregation, group decision making, Bayesian updating, and opinion polling), and computer scientists (including experts in combinatorial exchanges, distributed computing, information theory, and mixing worst-case and Bayesian analysis). This workshop will seek to bring together a variety of experts representing these fields, to engage in a dialog describing current and future research directions to facilitate the design, refinement, and proliferation of markets as predictive devices.

As part of the workshop, one or more tutorials are planned for the benefit of students and other newcomers to the field; little or no background knowledge will be assumed.


Bossaerts, P., Fine, L., and Ledyard, J., "Inducing liquidity in thin financial markets through combined-value trading mechanisms,'' European Economic Review, October 2002.

Chen, K-Y., and Plott, C.,R., "Information aggregation mechanisms: Concept, design and implementation for a sales forecasting problem," Lee Center Workshop, 2002.

Forsythe, R., Rietz, T.A., and Ross, T.W., "Wishes, expectations, and actions: A survey on price formation in election stock markets," J. of Economic Behavior and Organization, 39, 1999, 83-110.

Fortnow, L., Kilian, J., Pennock, D.M., and Wellman, M.P., "Betting boolean-style: A framework for trading in securities based on logical formulas," Proceedings of the Fourth Annual ACM Conference on Electronic Commerce, June 2003, 144-155.

Hanson, R., "Combinatorial information market design," Information Systems Frontiers, 5, 2003, 105-119.

Jackweth, J.C., and Rubinstein, M., "Recovering probability distributions from options prices," J. of Finance, 51, 1996, 1611-1631.

Pennock, D.M., Lawrence, S., Giles, C.L., and Nielsen, F.A., "The real power of artificial markets," Science, 291, February 9, 2001 (Letters), 987-988.

Plott, C.R., and Shyam, S., "Rational expectations and the aggregation of diverse information in laboratory security markets," Econometrica, 56, 1988, 1085-1118.

Roll, R., "Orange juice and weather," American Economic Review, 74, 1984, 861-880.

Thaler, R.H., and Ziemba, W.T., "Anomalies: Parimutuel betting markets: Racetracks and lotteries," J. of Economic Perspectives, 2, 1988, 161-174.

Williams, L. V., "Information efficiency in betting markets: A survey," Bulletin of Economic Research, 51, 1999, 1-30.

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Document last modified on October 12, 2004.