A unified framework for dynamic pari-mutuel information market design

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A unified framework for dynamic pari-mutuel information market design

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Electronic Commerce archive
Proceedings of the tenth ACM conference on Electronic commerce table of contents

Stanford, California, USA

SESSION: Session 8 table of contents

Pages 255-264

Year of Publication: 2009

ISBN:978-1-60558-458-4

Authors

Shipra Agrawal	Stanford University, Stanford, CA, USA
Erick Delage	HEC Montréal, Montréal (Québec), Canada
Mark Peters	Stanford University, Stanford, CA, USA
Zizhuo Wang	Stanford University, Stanford, CA, USA
Yinyu Ye	Stanford University, Stanford, CA, USA

Sponsors

ACM: Association for Computing Machinery
SIGEcom: ACM Special Interest Group on Electronic Commerce

Publisher

ACM New York, NY, USA

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ABSTRACT

Recently, coinciding with and perhaps driving the increased popularity of prediction markets, several novel pari-mutuel mechanisms have been developed such as the logarithmic market scoring rule (LMSR), the cost-function formulation of market makers, and the sequential convex parimutuel mechanism (SCPM). In this work, we present a unified convex optimization framework which connects these seemingly unrelated models for centrally organizing contingent claims markets. The existing mechanisms can be expressed in our unified framework using classic utility functions. We also show that this framework is equivalent to a convex risk minimization model for the market maker. This facilitates a better understanding of the risk attitudes adopted by various mechanisms. The utility framework also leads to easy implementation since we can now find the useful cost function of a market maker in polynomial time through the solution of a simple convex optimization problem.

In addition to unifying and explaining the existing mechanisms, we use the generalized framework to derive necessary and sufficient conditions for many desirable properties of a prediction market mechanism such as proper scoring, truthful bidding (in a myopic sense), efficient computation, controllable risk-measure, and guarantees on the worst-case loss. As a result, we develop the first proper, truthful, risk controlled, loss-bounded (in number of states) mechanism; none of the previously proposed mechanisms possessed all these properties simultaneously. Thus, our work could provide an effective tool for designing new market mechanisms.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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