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Evolutionary Dynamics in Data Trading with Risk Compensation

Author

Listed:
  • Jun Qian

    (School of Computer Science, Beijing University of Technology, Beijing 100124, China)

  • Xiao Sun

    (Department of Automation, Tsinghua University, Beijing 100124, China)

  • Yueting Chai

    (Department of Automation, Tsinghua University, Beijing 100124, China)

  • Xiao Xue

    (Division of Intelligence and Computing, Tianjin University, Tianjin 300350, China)

Abstract

The fact that data can be privately possessed yet contain the attributes of public goods leads to the “Arrow’s Information Paradox” in data trading. If left unchecked, supply-side deception about data quality and demand-side data leakage can seriously undermine the trust between suppliers and demanders. Inspired by the ideas of reputation and punishment, this paper combines a risk compensation mechanism, which is widely available in the financial sector, with data trading. Specifically, we propose a data trading model with risk compensation and study the evolutionary dynamics of the population using evolutionary game theory. We define λ as the demander’s compensation share that the supplier has to bear for data quality cheating and η as the supplier’s compensation share that the demander has to bear for data leakage. Through numerical solution and simulation, we analyze the evolutionary stable states of the population and find that the risk compensation mechanism in some data trading scenarios can limit the supply side from cheating on data quality or the demand side from leaking data. The results show that λ and η act asymmetrically, with λ being able to affect both supply-side and demand-side strategies, while η affects only the demand-side strategy. This work reveals chaos and asymmetry in data trading with risk compensation, and the proposed model and replication dynamic equations may have implications for future research.

Suggested Citation

  • Jun Qian & Xiao Sun & Yueting Chai & Xiao Xue, 2025. "Evolutionary Dynamics in Data Trading with Risk Compensation," Mathematics, MDPI, vol. 13(5), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:730-:d:1598491
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    References listed on IDEAS

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    1. Liu, Jiaqi & Zhang, Qianwei & Tang, Rui, 2024. "Fostering cooperative evolution through probabilistic punishment and environmental feedback in public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    2. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    3. Laura Veldkamp & Cindy Chung, 2024. "Data and the Aggregate Economy," Journal of Economic Literature, American Economic Association, vol. 62(2), pages 458-484, June.
    4. Herring, Richard J & Vankudre, Prashant, 1987. "Growth Opportunities and Risk-Taking by Financial Intermediaries," Journal of Finance, American Finance Association, vol. 42(3), pages 583-599, July.
    5. Manfred Milinski & Dirk Semmann & Hans-Jürgen Krambeck, 2002. "Reputation helps solve the ‘tragedy of the commons’," Nature, Nature, vol. 415(6870), pages 424-426, January.
    6. Zou, Kuan & Huang, Changwei, 2024. "Incorporating reputation into reinforcement learning can promote cooperation on hypergraphs," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    7. Hal Varian, 2018. "Artificial Intelligence, Economics, and Industrial Organization," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 399-419, National Bureau of Economic Research, Inc.
    8. Ma, Jinlong & Zhao, Hongfei, 2024. "Cooperation dynamics of reputation-based manhattan distance social circle in spatial prisoner’s dilemma game in evolutionary game theory," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    9. Iachan, Felipe S. & Silva, Dejanir & Zi, Chao, 2022. "Under-diversification and idiosyncratic risk externalities," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1227-1250.
    10. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    11. Luo, Luping & He, Wen & Hu, Hao, 2024. "Government subsidy policies for guarantee financing: Risk compensation vs. fee reduction," European Journal of Operational Research, Elsevier, vol. 314(2), pages 747-759.
    12. Ernst Fehr & Simon Gächter, 2002. "Altruistic punishment in humans," Nature, Nature, vol. 415(6868), pages 137-140, January.
    13. John, Kose, 1984. "Market Resolution and Valuation in Incomplete Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 19(1), pages 29-44, March.
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