大群体决策呈现出决策参与人不再局限于某领域专家、决策信息来自异时异地等新特点，是一种更适应于互联网+时代下的决策方式。本项目分析已有研究的局限性，提炼出大群体偏好信息下的信息融合与集结、不完全信息处理及共识一致性等关键问题。（1）提出大群体环境下基于犹豫信息的信息融合理论。发展可能性分布理论，为包含犹豫信息的偏好信息融合提供新的工具。（2）提出基于统计分析的不完全信息估计方法。设计缺失信息的可估计判别准则，利用个体信息、子群体信息、其他子群体信息，给出估计缺失信息的新方法。（3）建立大群体环境下的共识决策模型。考虑共识过程中的约束条件（如成本、时间），设计交互式或自动化共识模型；分析个体或子群体的策略操纵行为，探讨控制策略。（4）拓展大群体决策理论到具有广泛商业应用前景的群体推荐系领域，改进其推荐有效性。研究结果旨在为解决大群体偏好信息下的决策问题提供借鉴，推动现有理论研究的深化。

Large group decision making refers to the selection of the best option from a set of alternatives according to the preferences of a large number of people. Compared to small group decision making, the participators in a large group decision making problem are no longer limited to some special experts and the decision information is collected from different places in different time. It is a more appropriate way for the Internet + environment. In this project, based on the limitations of the current literature, we summarized some critical problems that need to be further studied in the field of large group decision making: information fusion and aggregation, incomplete information processing, and consensus reaching process. (1) The theory of information fusion is presented considering heterogeneous information including hesitate information. Based on the development of probability distribution, we provide a new tool to deal with the hesitate information. (2) An approach utilizing the statistical analysis is proposed to manage the incomplete information. In this approach, some estimate criteria are deigned to identify if the missing information can be estimated or not. Then by use of the individual preferences, subgroups’ information, and other groups’ information, a new method to estimate the missing information is given. (3) Some consensus models are proposed to assist the consensus reaching processes under a large group decision-making environment. Taking into account some constraint conditions (e.g., cost, time), interactive or automatic consensus models can be developed. Some manipulation behaviors between the individuals or the sub-groups are analyzed, and correspondingly some control strategies are designed and discussed. (4) The developed theory of large group decision making is applied to improve the effectiveness of some group recommendation systems which become increasing popular in business applications. The results of this study aim to provide new insights for solving the problems under a large group decision making setting.