针对多信息来源、多数据结构的复杂评价问题,对传统评价模式进行拓展并提出了泛综合评价的方法。泛综合评价理论主要为复杂评价信息的整合及求解提供支撑,具体而言主要采用构建信息融合框架的方式对不同类别与结构的多源信息进行整合,并通过随机模拟仿真的方法对信息融合框架的求解算法进行了探讨。由于信息融合框架中包含信息的复杂性增加了框架的求解成本,因而进一步分析了信息集成框架的简化求解算法,并通过算例的方式对信息集成框架简化求解算法的有效性进行了验证。简化求解算法的研究提升了泛综合评价在实际应用中的可操作性。"区域发展绩效的参与式评价"算例的构建,说明泛综合评价的理论为不同利益主体之间的民主决策提供了可能。本文的研究可为大数据背景下群体智慧的挖掘、民主决策的结果分析等实际应用问题提供理论和技术支撑。

To problems with multi-sources and diverse information, the traditional evaluation model is extended, and a new method called generic comprehensive evaluation is provided. This method centers on the fusion of complex information and its associated algorithm. Particularly, a framework is built to integrate various types and structured information, and the stochastic simulation technology is used to discuss the algorithm of the framework. Because the algorithm cost increases as the complexity of information in the framework, further a type of simplified algorithm is proposed. The validity of this simplified algorithm is illustrated by numerical examples. And the theory of generic comprehensive evaluation can be handled easily by the using of simplified algorithm. The case of "performance evaluation of region's development by multi-participants" illustrates that generic evaluation makes democratic decision among different benefits become possible. The achievements of this paper can provide support for the questions such as discovery of collective wisdom under big-data background, analysis of democratic decision making results, and so on.