A Unified Framework for Dynamic Prediction Market Design

Shipra Agrawal (), Erick Delage (), Mark Peters (), Zizhuo Wang () and Yinyu Ye ()
Additional contact information
Shipra Agrawal: Department of Computer Science, Stanford University, Stanford, California 94305
Erick Delage: HEC Montréal, Montréal, Quebec H3T 2A7, Canada
Mark Peters: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Zizhuo Wang: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Yinyu Ye: Department of Management Science and Engineering, Stanford University, Stanford, California 94305

Operations Research, 2011, vol. 59, issue 3, 550-568

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, utility-based markets, and the sequential convex pari-mutuel mechanism (SCPM). In this work, we present a convex optimization framework that unifies these seemingly unrelated models for centrally organizing contingent claims markets. The existing mechanisms can be expressed in our unified framework by varying the choice of a concave value function. We 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 unified framework also leads to easy implementation because we can now find the cost function of a market maker in polynomial time by solving 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 (independent of the number of states) mechanism; none of the previously proposed mechanisms possessed all these properties simultaneously. Thus, our work provides an effective tool for designing new prediction market mechanisms. We also discuss possible applications of our framework to dynamic resource pricing and allocation in general trading markets.

Keywords: programming; convex; applications; games/group decisions; bidding/auctions; gambling; risk; decision analysis; risk; finance; asset pricing (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (5)

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