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From disclosure to compliance: Modeling the evolutionary dynamics of tripartite privacy regulation

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Listed:
  • Qu, Qixing
  • Wei, Chengxi
  • Wang, Mingxian
  • Wang, Yaxin

Abstract

In the context of increasing concerns over digital privacy, this research develops a three-party evolutionary game framework that involves users, application service providers (ASPs), and the government to examine how information disclosure and regulatory mechanisms emerge and evolve. The model incorporates key behavioral and institutional parameters, including the degree of ASP violations, reputational loss, government-imposed fines, and economic compensation to users. Through replicator dynamics and numerical simulations, the study identifies four evolutionarily stable scenarios that reflect different stages of regulatory maturity from no regulation to market-driven self-regulation. The results reveal that user disclosure decisions are primarily influenced by perceived service utility and rights awareness, while ASP compliance is more sensitive to reputational risks than to monetary penalties or subsidies. Government regulatory intensity is found to be contingent on the observed degree of market violations. Based on these findings, the study proposes a staged framework for regulatory design and offers policy recommendations that integrate user empowerment, dynamic enforcement, and reputational mechanisms to promote sustainable and credible privacy governance.

Suggested Citation

  • Qu, Qixing & Wei, Chengxi & Wang, Mingxian & Wang, Yaxin, 2026. "From disclosure to compliance: Modeling the evolutionary dynamics of tripartite privacy regulation," Applied Mathematics and Computation, Elsevier, vol. 512(C).
    • Handle: RePEc:eee:apmaco:v:512:y:2026:i:c:s0096300325004989
    DOI: 10.1016/j.amc.2025.129773
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    References listed on IDEAS

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