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A Unified Framework for Dynamic Pari-Mutuel Information Market Design


  • Shipra Agrawal
  • Erick Delage
  • Mark Peters
  • Zizhuo Wang
  • Yinyu Ye


Recently, several new pari-mutuel mechanisms have been introduced to organize markets for contingent claims. Hanson introduced a market maker derived from the logarithmic scoring rule, and later Chen and Pennock developed a cost function formulation for the market maker. On the other hand, the SCPM model of Peters et al. is based on ideas from a call auction setting using a convex optimization model. In this work, we develop a unified framework that bridges these seemingly unrelated models for centrally organizing contingent claim markets. The framework, developed as a generalization of the SCPM, will support many desirable properties such as proper scoring, truthful bidding (in a myopic sense), efficient computation, and guarantees on worst case loss. In fact, our unified framework will allow us to express various proper scoring rules, existing or new, from classical utility functions in a convex optimization problem representing the market organizer. Additionally, we utilize concepts from duality to show that the market model is equivalent to a risk minimization problem where a convex risk measure is employed. This will allow us to more clearly understand the differences in the risk attitudes adopted by various mechanisms, and particularly deepen our intuition about popular mechanisms like Hanson's market-maker. In aggregate, we believe this work advances our understanding of the objectives that the market organizer is optimizing in popular pari-mutuel mechanisms by recasting them into one unified framework.

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  • Shipra Agrawal & Erick Delage & Mark Peters & Zizhuo Wang & Yinyu Ye, 2009. "A Unified Framework for Dynamic Pari-Mutuel Information Market Design," Papers 0902.2429,
  • Handle: RePEc:arx:papers:0902.2429

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    References listed on IDEAS

    1. Berg, Joyce & Forsythe, Robert & Nelson, Forrest & Rietz, Thomas, 2008. "Results from a Dozen Years of Election Futures Markets Research," Handbook of Experimental Economics Results, Elsevier.
    2. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    3. Bossaerts, Peter & Fine, Leslie & Ledyard, John, 2002. "Inducing liquidity in thin financial markets through combined-value trading mechanisms," European Economic Review, Elsevier, vol. 46(9), pages 1671-1695, October.
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