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A logarithmic market scoring rule agent-based model to evaluate prediction markets

Author

Listed:
  • Athos V. C. Carvalho

    (University of Brasilia)

  • Douglas Silveira

    (University of Alberta
    Territorial and Sectoral Analysis Laboratory (LATES))

  • Regis A. Ely

    (Federal University of Pelotas)

  • Daniel O. Cajueiro

    (University of Brasilia
    National Institute of Science and Technology for Complex Systems (INCT-SC)
    Machine Learning Laboratory in Finance and Organizations (LAMFO))

Abstract

Prediction Markets (PMs) are markets in which agents trade event contingent assets. Enterprises use PMs to forecast revenues and project deadlines. This paper presents an Agent-based model, called Logarithmic Market Scoring Rule-Automated Market Maker (LMSR-ASM), to evaluate Prediction Markets. Our model is capable of testing different types of Automated Market Makers (AMMs), which are mathematical functions or computational mechanisms needed to provide liquidity in Prediction Markets. The model offers insights into how to set parameters in a PM and how profits react to contrasting settings and AMMs. In addition, we simulate different probability processes, distinct AMMs, and agent behaviors. This paper also utilizes the LMSR-ASM to evaluate the impact of choosing initial prices in profits and revenue opportunities regarding AMM computational implementation. We show that we can use the LMSR-ASM to find optimal parameters for maximizing profits in PMs and how different AMMs affect market results under a variety of settings.

Suggested Citation

  • Athos V. C. Carvalho & Douglas Silveira & Regis A. Ely & Daniel O. Cajueiro, 2023. "A logarithmic market scoring rule agent-based model to evaluate prediction markets," Journal of Evolutionary Economics, Springer, vol. 33(4), pages 1303-1343, September.
  • Handle: RePEc:spr:joevec:v:33:y:2023:i:4:d:10.1007_s00191-023-00822-w
    DOI: 10.1007/s00191-023-00822-w
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    References listed on IDEAS

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    1. Daniele Giachini, 2021. "Rationality and asset prices under belief heterogeneity," Journal of Evolutionary Economics, Springer, vol. 31(1), pages 207-233, January.
    2. Wolfers, Justin & Zitzewitz, Eric, 2006. "Five Open Questions About Prediction Markets," CEPR Discussion Papers 5562, Centre for Economic Policy Research.
    3. Sanmay Das, 2005. "A learning market-maker in the Glosten-Milgrom model," Quantitative Finance, Taylor & Francis Journals, vol. 5(2), pages 169-180.
    4. 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.
    5. Blume, Lawrence & Easley, David, 2009. "The market organism: Long-run survival in markets with heterogeneous traders," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1023-1035, May.
    6. Scott C. Linn & Nicholas S. P. Tay, 2007. "Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning," Management Science, INFORMS, vol. 53(7), pages 1165-1180, July.
    7. Tay, Nicholas S. P. & Linn, Scott C., 2001. "Fuzzy inductive reasoning, expectation formation and the behavior of security prices," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 321-361, March.
    8. Ehrentreich, Norman, 2006. "Technical trading in the Santa Fe Institute Artificial Stock Market revisited," Journal of Economic Behavior & Organization, Elsevier, vol. 61(4), pages 599-616, December.
    9. Robin Hanson, 2007. "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 3-15, February.
    10. Bo Cowgill & Eric Zitzewitz, 2015. "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1309-1341.
    11. Chueh-Yung Tsao & Ya-Chi Huang, 2018. "Revisiting the issue of survivability and market efficiency with the Santa Fe Artificial Stock Market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 537-560, October.
    12. Volker Grimm & Steven F. Railsback & Christian E. Vincenot & Uta Berger & Cara Gallagher & Donald L. DeAngelis & Bruce Edmonds & Jiaqi Ge & Jarl Giske & Jürgen Groeneveld & Alice S.A. Johnston & Alex, 2020. "The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(2), pages 1-7.
    13. Bottazzi, Giulio & Giachini, Daniele, 2017. "Wealth and price distribution by diffusive approximation in a repeated prediction market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 473-479.
    14. Michael A. Smith & David Paton & Leighton Vaughan Williams, 2006. "Market Efficiency in Person‐to‐Person Betting," Economica, London School of Economics and Political Science, vol. 73(292), pages 673-689, November.
    15. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    16. Restocchi, Valerio & McGroarty, Frank & Gerding, Enrico, 2019. "The stylized facts of prediction markets: Analysis of price changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 159-170.
    17. 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.
    18. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    19. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-371, May.
    20. Ya-Chi Huang & Chueh-Yung Tsao, 2018. "Evolutionary Frequency and Forecasting Accuracy: Simulations Based on an Agent-Based Artificial Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 79-104, June.
    21. 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.
    22. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    23. Haijun Yang & Shuheng Chen, 2018. "A heterogeneous artificial stock market model can benefit people against another financial crisis," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-25, June.
    24. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    25. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    26. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    27. Frank M. A. Klingert & Matthias Meyer, 2018. "Comparing Prediction Market Mechanisms: An Experiment-Based and Micro Validated Multi-Agent Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-7.
    28. Marco Ottaviani & Peter Norman Sørensen, 2007. "Outcome Manipulation in Corporate Prediction Markets," Journal of the European Economic Association, MIT Press, vol. 5(2-3), pages 554-563, 04-05.
    29. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    30. Restocchi, Valerio & McGroarty, Frank & Gerding, Enrico, 2019. "Statistical properties of volume and calendar effects in prediction markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1150-1160.
    31. Haijun Yang & Harry Wang & Gui Sun & Li Wang, 2015. "A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 901-924, November.
    32. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    33. Dindo, Pietro & Massari, Filippo, 2020. "The wisdom of the crowd in dynamic economies," Theoretical Economics, Econometric Society, vol. 15(4), November.
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    Cited by:

    1. Przemys{l}aw Rola, 2025. "Boltzmann Price: Toward Understanding the Fair Price in High-Frequency Markets," Papers 2507.09734, arXiv.org.

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    • G1 - Financial Economics - - General Financial Markets
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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