政府动态奖惩机制下重大疫情防控演化博弈研究

doi:10.16381/j.cnki.issn1003-207x.2020.0587

摘要/Abstract

摘要： 重大疫情的爆发对于人类健康和社会经济都具有极大的负面影响，当前控制疫情扩散的主要手段是做好隔离防控。在隔离防控的过程中，政府部门和社会公众存在着一种博弈关系。文章分别在静态和三种动态奖惩机制下构建了政府部门和社会公众的博弈矩阵，重点分析了防控成本、奖励上限值和惩罚上限值对博弈系统演化稳定策略的影响，最后进行了算例仿真分析。研究结果表明：政府部门采取静态奖惩机制时，不存在演化稳定策略，采用动态奖惩机制可以有效地弥补静态奖惩机制的不足，实现演化稳定状态；动态奖励与静态惩罚机制在疫情防控中优于其他动态奖惩机制；社会公众采取自愿隔离的概率与防控成本、奖励上限值呈负相关，与惩罚上限值呈正相关。

关键词: 演化博弈；重大疫情防控；动态奖惩机制；演化稳定策略

Abstract: The COVID-19 that broke out at the end of 2019 is highly contagious, the world is caught in panic of the epidemic, and the cumulative number of confirmed cases is still on the rise. Since the outbreak of covid-19 in China, the government has taken decisive measures to control the epidemic. In the process of isolation prevention and control, there is a game relationship between government departments and the public. It is assumed that both the government and the public are bounded rational groups and the evolutionary game model is used, a game matrix is constructed for government departments and the public under static and three dynamic reward and punishment mechanisms,analyzing the impact of prevention and control costs, the upper limit of reward and the upper limit of punishment on the evolutionary and stable strategy of the game system.Finally, a simulation analysis is performed.The results show that:there is no evolutionary stable strategy under static reward and punishment mechanisms,the use of dynamic reward and punishment mechanisms can effectively make up for the shortcomings of static reward and punishment mechanisms,achieving the evolution and stability of government departments’ active prevention and control and the public’s voluntary isolation strategy;dynamic reward and static punishment is better than other mechanisms; The probability of voluntary segregation by the public is negatively correlated with the cost of prevention and control and the upper limit of reward,positively correlated with the upper limit of punishment.

Key words: evolutionary game; major epidemic prevention and control; the mechanism of dynamic reward and punishment; evolutionary stable strategy

中图分类号:

R181.8
D63

梁喜, 陈清卿. 政府动态奖惩机制下重大疫情防控演化博弈研究[J]. 中国管理科学, 2023, 31(3): 277-286.

LIANG Xi, CHEN Qing-qing. Study on the Major Epidemic Prevention and Control under the Mechanism of Government Dynamic Reward and Punishment[J]. Chinese Journal of Management Science, 2023, 31(3): 277-286.

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