基于复杂网络的通用航空安全监管演化博弈研究

肖琴; 罗帆; 肖琴; 罗帆

doi:10.13306/j.1672-3813.2022.03.005

复杂系统与复杂性科学 >

2022 , Vol. 19 >Issue 3: 33 - 43

DOI: https://doi.org/10.13306/j.1672-3813.2022.03.005

基于复杂网络的通用航空安全监管演化博弈研究

肖琴 ,
罗帆 ,
肖琴 ,
罗帆

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1.武汉工程大学管理学院,武汉 430205;
2.武汉理工大学管理学院,武汉 430070

肖琴(1990-),女,湖北汉川人,博士,讲师,主要研究方向为航空安全、风险与应急管理、人力资源管理。

收稿日期: 2021-02-27

修回日期: 2021-05-24

网络出版日期: 2022-10-12

基金资助

教育部人文社会科学研究规划基金(18YJA630076);湖北省教育厅哲学社会科学项目(21Q098)

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Study on Evolutionary Game of General Aviation Safety Supervision Based on Complex Network

XIAO Qin ,
LUO Fan ,
XIAO Qin ,
LUO Fan

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1. School of Management, Wuhan Institute of Technology, Wuhan 430205, China;
2. School of Management, Wuhan University of Technology, Wuhan 430070, China

Received date: 2021-02-27

Revised date: 2021-05-24

Online published: 2022-10-12

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摘要

为探究通用航空企业安全监管合作行为的演化机制,基于复杂网络的视角,构建通用航空安全监管演化博弈模型;分析了网络规模、网络平均度、累计收益权重参数、成本收益比、监管机构的奖励及惩罚对合作行为的影响;提出通用航空安全监管的策略。研究结果表明:网络规模和网络平均度的增加均会促进合作者均衡密度的增加;累计收益权重参数的增加能够促进网络中合作行为的产生;成本收益比对网络中的博弈合作行为具有抑制作用;政府的奖励和惩罚均能促进合作行为的产生,且惩罚比奖励效果更明显;加大政府支持力度、构建网络沟通平台、降低企业运营成本、构建行业奖惩机制是通用航空安全的监管策略。

关键词： 通用航空; 安全监管; 演化博弈; 复杂网络; 模拟仿真

本文引用格式

肖琴 , 罗帆 , 肖琴 , 罗帆 . 基于复杂网络的通用航空安全监管演化博弈研究[J]. 复杂系统与复杂性科学, 2022 , 19(3) : 33 -43 . DOI: 10.13306/j.1672-3813.2022.03.005

Abstract

In order to explore the evolution mechanism of the cooperation behavior of general aviation enterprises on safety supervision, an evolution game model is proposed based on the perspective of complex network. With the model, this paper analyzes the influence of network size, network average degree, cumulative payoff weight, cost-income ratio, reward and punishment of regulators. The results show that the increase of network size and network average degree contributes to the increase of the equilibrium density of cooperators; the increase of cumulative payoff weight promotes the cooperation behavior in the network; however, the increase of cost-income ratio inhibits the game cooperation behavior in the network; government reward and punishment can promote the cooperation behavior, and the effect of punishment is more significant than reward. The regulatory strategies of general aviation safety can be concluded including increasing government support, building network communication platform, reducing enterprise operation cost, and building industry reward and punishment mechanism.

Key words： general aviation; safety supervision; evolutionary game; complex network; simulation

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