[Three-party game and simulation analysis of health-related information quality regulation in public health emergencies].

Abstract

Objective: To construct a tripartite game model involving the government, the public, and the pharmaceutical industry alliance during public health emergencies, revealing the dynamic mechanisms of health-related information quality regulation and exploring effective strategies to optimize the information dissemination environment through reward-punishment mechanisms.

Methods: Based on evolutionary game theory, a tripartite evolutionary game model was established, integrating strategy spaces, payoff functions, and parameter definitions for each stakeholder. The pharmaceutical industry alliance ' s strategies included publishing high- or low-quality information (α), the public ' s strategies encompassed rational analysis or passive response (β), and the government's strategies involved regulatory enforcement or inaction (γ). Key parameters, such as economic benefits (I_yy), regulatory costs (C_zf), penalties (F_yy), and incentives (P_yy), were quantified to reflect real-world scenarios. Replicator dynamic equations and Jacobian matrices were derived to analyze the stability of equilibrium points, while MATLAB 2016a simulations were conducted to validate the model under varying initial conditions (e.g., I_yy=100, 150, 200; P_yy=0, 20, 35; F_yy=0, 10, 20). Sensitivity analyses examined the impact of critical parameters on system evolution, by 50 iterative simulations to observe convergence patterns.

Results: The study revealed three key findings: (1) Public rational discernment (β) significantly influenced the pharmaceutical industry ' s strategy. Simulations demonstrated that increasing I_qz(benefits of information acquisition) reduced C_qz (cognitive costs), elevating β from 0.4 to 0.8 and driving α (high-quality information probability) to stabilize at 1. (2) Government regulatory intensity (γ) correlated positively with the social hazards of low-quality information. When F_yy+ P_yy>I_yy, speculative behaviors decreased, achieving equilibrium at α=1. (3) Dual stable equilibria emerged: a high-quality equilibrium (α=1, β=1, γ=0) with lower regulatory costs and a low-quality equilibrium (α=0, β=0, γ=1) associated with higher social risks. Phase diagrams illustrated path dependency, where initial α < 0.5 led to the low-quality equilibrium unless dynamic penalties (F_yy>20) and incentives (P_yy>30) were enforced.

Conclusion: A "carrot-stick" collaborative governance framework is proposed, emphasizing categorized regulation, AI-enabled auditing, and dynamic penalty systems. Future research should integrate emotional utility functions to address irrational decision-making impacts, thereby enhancing the adaptability of health information regulatory systems.