基于高阶Markov链的重大决策社会风险变权集对预测模型

为提高政府对重大决策社会风险的治理能力,基于高阶Markov链理论,借助变权方法和集对分析方法,构建重大决策社会风险预测模型.首先,将重大决策前后的社会风险指标状态集组成集对;然后,利用指标变权计算不同时刻的集对联系度和状态转移概率矩阵,以克服传统常权无法反映指标值次序重要性的问题;最后,利用更接近客观实际的高阶Markov链预测集对联系度,并进行社会风险态势分析,其中Markov链的高阶系数根据状态转移概率矩阵间的相似度计算.以某市PX项目决策为例进行方法验证和比较,结果表明所构建模型与传统模型相比,可以更有效、准确地对重大决策社会风险进行预测.另外,通过实例研究发现,专家的风险态度对短期的分析和预测影响较大,而对长期的分析和预测影响较小.

Set pair prediction model for social risk from major decision-making based on variable weight and higher-order Markov chain

In order to improve the government''s ability to manage social risk from major decision-making based on the higher-order Markov chain theory, the variable weight method and set pair analysis method are used to build the prediction model for social risk from major decision-making. Firstly, the state sets of social risk indexes before and after making major decisions are composed a set pair. Then the variable weight method is used to calculate the connection degree of the set pair and state transferring probability matrix in different time to overcome the problem that the constant weight can not reflect the order importance of the value of index. Finally, the higher-order Markov chain is proposed to predict the connection degree of the set pair and to analyze social risk situation. In addition, the higher-order coefficients of the Markov chain are calculated by the similarity between state transferring probability matrixes. The model presented is verified and compared by an illustrative example of PX project decisions. The results show that the proposed model is more accurate and efficient to predict social risk compared with the traditional model. Furthermore, the risk attitude of experts has great influence on short-term social risk analysis and prediction, while the long-term social risk analysis and prediction are less affected.