基于判断聚合模型对集体判断不一致的化解

Solutions for Inconsistent Collective Judgment Set Based on Judgment Aggregation Model

阿罗定理证明了将完全理性个体偏好聚合为完全理性集体偏好的不可能性。这个悲观的结论对实际的集体决策过程没有帮助。如何化解聚合产生的集体意见不一致,以保证集体决策公平公正地实现集体最大利益,即实现集体决策的理性化,成为许多学科研究的热点。判断聚合模型将聚合问题放在逻辑框架之下,将集体理性聚合研究提升到一个更高的抽象水平。本文运用判断聚合模型,从分析判断困境入手,对基于前提和结论的判断聚合方法,基于多数判断集的判断聚合方法和基于权重最大的判断聚合方法进行探讨,分析了各种方法在化解集体判断不一致上的优势及局限。这些方法尽管有一定局限,但能够求得一致的集体判断集,并且逻辑形式刻画技术也使这些方法能够运用于计算机和人工智能领域中的推荐系统、多主体决策系统以及搜索引擎技术之中,推动了相关理论和应用技术的发展。

Arrow's Impossibility Theorem has been proved that it is impossible to aggregate rational individual preference order to a rational collective preference order,but the pessimistic conclusion has nothing to do with the practical collective decision.The recent research focuses on how to find solutions to the inconsistent judgment set in order to guarantee the justice in the group decision process to achieve the maximum of group benefit.Judgment aggregation model puts the collective aggregation problem to a general logic framework,and improves the collective rational aggregation research to a higher and more abstract level.This paper discusses the aggregation problems based on judgment aggregation model.It begins with the discursive dilemma,and applies the premise-based and conclusion-based methods,methods based on the majoritarian judgment set,and methods based on the weighted majoritarian judgment set,to solve the inconsistent collective judgment set,and analyze the advantages and disadvantages of these different methods.Although these methods have some limits,they can be used to get the consistent collective judgment set,and the logical characterization technique can be applied to recommender systems,multi-agent decision system and search engines in computer science and artificial intelligence area.The research may improve the development of the relative theories and applied technologies.