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风险集聚类邻避冲突事件随机演化情景分析
引用本文:陈恒,卢巍,杜蕾.风险集聚类邻避冲突事件随机演化情景分析[J].中国管理科学,2020,28(4):131-141.
作者姓名:陈恒  卢巍  杜蕾
作者单位:哈尔滨工程大学经济管理学院, 黑龙江 哈尔滨 150001
基金项目:国家软科学研究计划项目(2013GXS5B190);黑龙江省攻关软科学计划项目(GC13D203)
摘    要:随着中国现代城市管理社区制的推行,由公共设施引发的社区冲突逐步凸显,其中,风险集聚类邻避设施成为引发社区冲突的主要诱因。邻避冲突事件演化过程的复杂性和随机扰动要求现有研究不应局限于确定环境下事件的分析,而应在更加真实的不确定环境下对冲突本身展开探讨。鉴于此,本文以演化博弈论为理论基础,引入高斯白噪声随机干扰项,构建风险集聚类邻避冲突事件中营建企业与周边民众两类群体的随机演化博弈模型,对比分析在无政府监管与政府监管情景下群体策略选择行为的随机演化过程,并利用Matlab进行数值仿真。研究发现:(1)无政府监管情景下,当营建企业采取强硬策略收益小于成本,且周边民众采取抗争策略成本大于收益时,(合作,妥协)是其演化均衡策略组合;营建企业与周边民众策略选择演化速度与初始策略选择概率密切相关。(2)政府监管情景下,当政府监管力度大于营建企业采取强硬策略收益与采取合作策略收益之差,且政府监管力度大于周边民众采取抗争策略收益与采取妥协策略收益之差时,(合作,妥协)是其唯一策略演化均衡点;政府监管力度对营建企业策略选择有显着影响,而对周边民众策略选择无显着影响。(3)随机因素对风险集聚类邻避冲突中营建企业与周边民众策略选择行为产生干扰,但随机模型演化趋势与确定性模型相一致。研究结果表明,政府完善监管机制,赋予公民参与权利,企业建立"柔性"冲突协调机制,民众合法表达利益诉求,推动风险集聚类邻避冲突治理框架的构建。

关 键 词:风险集聚类邻避冲突  随机演化博弈模型  情景分析
收稿时间:2018-07-02
修稿时间:2019-01-03

Stochastic Evolutionary Scenario Analysis of Risk Set Clustering NIMBY Conflicts Events
CHEN Heng,LU Wei,DU Lei.Stochastic Evolutionary Scenario Analysis of Risk Set Clustering NIMBY Conflicts Events[J].Chinese Journal of Management Science,2020,28(4):131-141.
Authors:CHEN Heng  LU Wei  DU Lei
Institution:School of Economics and Management, Harbin Engineering University, Harbin 150001, China
Abstract:With the implementation of the modern urban management community system in China, the community conflicts caused by public facilities are becoming increasingly prominent. Among them, risk set clustering NIMBY eventshave become the main causes of community conflicts. The complexity and random disturbance of the evolution process of NIMBY conflict events require that existing research should not be limited to the analysis of events in a deterministic environment, but should explore the conflict itself in a more real uncertain environment. In view of this, based on evolutionary game theory, Gauss white noise is instroduced to establish the stochastic evolutionary game model of risk set clustering NIMBY conflict events in uncertain environment, so as to compares and analyzes the evolution process of group strategy selection in deterministic system and stochastic system under the scenario of non-government regulation and government regulation by using MATLAB simulation. It is found that (1) in the government non-regulation situation, when the incomes of construction enterprises adopting tough strategy are less than their action costs, and the costs borne by the surrounding people taking protest strategy are more than their incomes, both sides tend to adopt (cooperation, compromise) strategy combination. In addition, in the government non-regulation situation, the speed at which the construction enterprise and the surrounding people's strategy evolved to equilibrium point closely related to its initial strategy selection probability.(2) In the government regulation situation, when the government regulation is greater than the difference of construction enterprises between the profit to adopt a strong strategy and cooperation strategy, and government regulation is greater than the difference between the resistance strategy and the compromise strategy of the surrounding people, (cooperation, compromise) is the only equilibrium point of evolution strategy. In addition, in the government regulation situation, government regulation has a significant impact on the strategic choice of construction enterprises, while has no significant impact on the surrounding people's strategic choice. (3) Random factors interfere with the strategic choice behavior of the construction enterprises and the surrounding people in the risk set clustering NIMBY conflict, but its evolution trend is consistent with deterministic evolution model. The research findings reveal that the legitimate expression of people's interests, the improvement of government regulation mechanism, and the establishment of "flexible" conflict compensation mechanism in enterprises can promote the construction of risk set clustering NIMBY conflicts events governance framework.
Keywords:risk set clustering NIMBY conflict  stochastic evolutionary game model  scenario analysis  
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