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. 2025 Jun 18;57(3):514-521.
doi: 10.19723/j.issn.1671-167X.2025.03.015.

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

[Article in Chinese]
Y Wang  1 , R Yuan  1 , S Li  1 , C Chang  1   2
Affiliations

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

[Article in Chinese]
Y Wang et al. Beijing Da Xue Xue Bao Yi Xue Ban. .

Abstract
in English, Chinese

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 (Iyy), regulatory costs (Czf), penalties (Fyy), and incentives (Pyy), 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., Iyy=100, 150, 200; Pyy=0, 20, 35; Fyy=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 Iqz(benefits of information acquisition) reduced Cqz (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 Fyy+ Pyy>Iyy, 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 (Fyy>20) and incentives (Pyy>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.

目的: 构建突发公共卫生事件中政府、公众与医药产业联盟的三方博弈模型, 揭示健康信息质量监管的动态机制, 探索通过奖惩策略优化信息传播环境的有效路径。

方法: 基于演化博弈理论, 构建三方演化博弈模型, 整合各主体的策略空间、支付函数及参数定义, 其中, 医药产业联盟策略包括发布高质量或低质量信息(α), 公众策略涵盖理性分析或被动响应(β), 政府策略涉及监管执行或不作为(γ)。量化关键参数(如经济收益Iyy、监管成本Czf、惩罚Fyy和激励Pyy)以反映现实情境。通过推导复制动态方程和Jacobian矩阵分析均衡点稳定性, 并利用MATLAB 2016a进行仿真验证, 模拟不同初始条件(如Iyy=100, 150, 200; Pyy=0, 20, 35; Fyy=0, 10, 20)下的演化过程。开展敏感性分析, 考察关键参数对系统演化的影响, 通过50次迭代模拟以观察收敛规律。

结果: 研究发现: (1)公众理性辨别能力(β)显著影响医药产业联盟的策略选择, 仿真表明, 当信息获取收益(Iqz)提高时, 公众认知成本(Cqz)降低, β从0.4增至0.8, 推动高质量信息概率(α)稳定至1;(2)政府监管强度(γ)与低质量信息的社会危害呈正相关, 当满足Fyy+ Pyy>Iyy时, 投机行为减少, 系统收敛至均衡(α=1);(3)系统存在双稳定均衡: 高质量均衡(α=1, β=1, γ=0)下监管成本降低, 低质量均衡(α=0, β=0, γ=1)则伴随社会风险提升, 相图揭示了路径依赖性, 若初始α < 0.5, 系统将趋向低质量均衡, 除非实施动态惩罚(Fyy>20)和激励(Pyy>30)。

结论: 研究提出“激励-约束”协同治理框架, 建议通过分类监管、人工智能技术赋能及动态惩戒制度优化监管效能。未来需引入情绪效用函数, 探讨非理性决策对系统演化的影响, 以完善健康信息传播监管体系。

Keywords: Health-related information; Quality supervision; Three-party evolution game.

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Conflict of interest statement

利益冲突 所有作者均声明不存在利益冲突。

Figures

图 1
图 1
三方主体博弈模型逻辑关系图 Logical relationship diagram of the three-party subject game model
图 2
图 2
医药产业收益的影响 Impact of pharmaceutical industry earnings
图 3
图 3
政府对医药产业奖励的影响 Impact of government incentives for the pharmaceutical industry
图 4
图 4
政府对医药产业处罚的影响 Impact of government penalties on the pharmaceutical industry
图 5
图 5
政府不作为状况行政处罚的影响 Impact of administrative penalties on the state of government inaction
图 6
图 6
第一组仿真数据演化50次结果 Results of evolving the first set of simulation data 50 times
图 7
图 7
第二组仿真数据演化50次结果 Results of evolving the second set of simulation data 50 times
See this image and copyright information in PMC

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