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Tripartite evolutionary game and simulation of solvency supervision under C‐ROSS II based on prospect theory

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  • Shilong Li
  • Zhijie Tong

Abstract

This paper focuses on the implementation of “C‐ROSS II” and utilizes an evolutionary game model to investigate regulatory issues. Based on prospect theory, a three‐party evolutionary game model is constructed among regulatory agencies, insurance companies, and consumers with incomplete rationality, examining evolutionary stability strategies. Meanwhile, considering the different attitudes of policyholders in the face of loss and return, the heterogeneous risk preference is analyzed by changing the prospect parameters. The results show that increases in penalty amounts, positive incentives, and consumer sensitivity to losses will promote the evolution of the system to the optimal stable equilibrium point. However, rises in brand incomes and rectification costs, as well as decreases in capital costs, will decrease the probability of regulatory authorities enforcing strict supervision.

Suggested Citation

  • Shilong Li & Zhijie Tong, 2025. "Tripartite evolutionary game and simulation of solvency supervision under C‐ROSS II based on prospect theory," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(1), pages 515-528, January.
    • Handle: RePEc:wly:mgtdec:v:46:y:2025:i:1:p:515-528
    DOI: 10.1002/mde.4388
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    References listed on IDEAS

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    1. King, Michael & Singh, Anuj Pratap, 2020. "Understanding farmers’ valuation of agricultural insurance: Evidence from Vietnam," Food Policy, Elsevier, vol. 94(C).
    2. Daniel Friedman, 1998. "On economic applications of evolutionary game theory," Journal of Evolutionary Economics, Springer, vol. 8(1), pages 15-43.
    3. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    4. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    5. Xiaoyuan Qi & Ying Han & Baoling Jin, 2023. "Evolutionary game equilibrium in the downstream carbon market: Evidence from the household size in China," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(5), pages 2814-2834, July.
    6. Aoqing Li & Zaiwu Gong & Jeffrey Yi-Lin Forrest, 2023. "Financial fund allocation in China's catastrophe insurance market: a game-theoretic analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 3181-3202, July.
    7. Jiali Wang & Xue Peng & Yunan Du & Fulin Wang, 2022. "A tripartite evolutionary game research on information sharing of the subjects of agricultural product supply chain with a farmer cooperative as the core enterprise," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(1), pages 159-177, January.
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