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Convergence within binary market scoring rules

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  • Razvan Tarnaud

    (Université Paris 1 Panthéon-Sorbonne)

Abstract

Prediction markets are run to extract information from its participants through financial incentive. The market scoring rule mechanism represents a way of organizing markets in order to foster agents to make sincere predictions. Market scoring rules are usually presented in a context of asset trading, but they also boil down to a sequential probability report process analyzed here. If the future state space is binary (i.e., composed of only two possible states) and only two agents participate alternatively in the market, it is proven that for strictly proper market scoring rules, the report sequences of each agent converge toward limit probability reports which are closer to each other than the subjective probabilities of the agents.

Suggested Citation

  • Razvan Tarnaud, 2019. "Convergence within binary market scoring rules," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(4), pages 1017-1050, November.
    • Handle: RePEc:spr:joecth:v:68:y:2019:i:4:d:10.1007_s00199-018-1155-3
    DOI: 10.1007/s00199-018-1155-3
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    1. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    2. Berg, Joyce & Forsythe, Robert & Nelson, Forrest & Rietz, Thomas, 2008. "Results from a Dozen Years of Election Futures Markets Research," Handbook of Experimental Economics Results, in: Charles R. Plott & Vernon L. Smith (ed.), Handbook of Experimental Economics Results, edition 1, volume 1, chapter 80, pages 742-751, Elsevier.
    3. He, Xue-Zhong & Treich, Nicolas, 2017. "Prediction market prices under risk aversion and heterogeneous beliefs," Journal of Mathematical Economics, Elsevier, vol. 70(C), pages 105-114.
    4. Jeffrey Lange & Nicholas Economides, 2005. "A Parimutuel Market Microstructure for Contingent Claims," European Financial Management, European Financial Management Association, vol. 11(1), pages 25-49, January.
    5. Michael Ostrovsky, 2012. "Information Aggregation in Dynamic Markets With Strategic Traders," Econometrica, Econometric Society, vol. 80(6), pages 2595-2647, November.
    6. Armantier, Olivier & Treich, Nicolas, 2013. "Eliciting beliefs: Proper scoring rules, incentives, stakes and hedging," European Economic Review, Elsevier, vol. 62(C), pages 17-40.
    7. Theo Offerman & Joep Sonnemans & Gijs Van De Kuilen & Peter P. Wakker, 2009. "A Truth Serum for Non-Bayesians: Correcting Proper Scoring Rules for Risk Attitudes ," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(4), pages 1461-1489.
    8. Yiling Chen & David M Pennock, 2012. "A Utility Framework for Bounded-Loss Market Makers," Papers 1206.5252, arXiv.org.
    9. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    10. Martin Weitzman, 2008. "Utility Analysis And Group Behavior An Empirical Study," World Scientific Book Chapters, in: Donald B Hausch & Victor SY Lo & William T Ziemba (ed.), Efficiency Of Racetrack Betting Markets, chapter 9, pages 47-55, World Scientific Publishing Co. Pte. Ltd..
    11. Eric Budish & Peter Cramton & John Shim, 2015. "Editor's Choice The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1547-1621.
    12. Shipra Agrawal & Erick Delage & Mark Peters & Zizhuo Wang & Yinyu Ye, 2011. "A Unified Framework for Dynamic Prediction Market Design," Operations Research, INFORMS, vol. 59(3), pages 550-568, June.
    13. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    14. Friend, Irwin & Blume, Marshall E, 1975. "The Demand for Risky Assets," American Economic Review, American Economic Association, vol. 65(5), pages 900-922, December.
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    Cited by:

    1. Rabah Amir & Sergei Belkov & Igor V. Evstigneev & Thorsten Hens, 2022. "An evolutionary finance model with short selling and endogenous asset supply," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 73(2), pages 655-677, April.
    2. Felipe R. Durazzo & David Turchick, 2023. "Welfare-improving misreported polls," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 75(2), pages 523-565, February.
    3. Dian Yu & Jianjun Gao & Weiping Wu & Zizhuo Wang, 2022. "Price Interpretability of Prediction Markets: A Convergence Analysis," Papers 2205.08913, arXiv.org, revised Nov 2023.

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    More about this item

    Keywords

    Prediction market; Risk aversion; Fixed point; Favorite-longshot bias; Equilibrium;
    All these keywords.

    JEL classification:

    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • D79 - Microeconomics - - Analysis of Collective Decision-Making - - - Other
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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