When a decision-maker prefers to compare different alternatives in pairs to handle real situations, there are many different expression styles that can be used. Two representative expression styles are the probabilistic linguistic preference relation (PLPR), which originates from the fuzzy linguistic approach and the distributed preference relation (DPR), which originates from the evidential reasoning approach. Although these two expression styles look quite similar, their meanings, operations, and relevant decision making processes are significantly different. This presents the decision-maker with the challenge of selecting either PLPRs or DPRs in different real cases. To address this issue, this paper provides a detailed analysis of the similarities and differences between PLPRs and DPRs. The analysis is conducted from five perspectives, including modeling of decision making problems, handling of uncertainty, consistency between preference relations, information aggregation, and elicitation process. An engineer selection problem for an automobile manufacturing enterprise is investigated to demonstrate how to appropriately select PLPRs or DPRs to model and analyze decision making problems in real situations with consideration for the preferences of decision-makers.

当决策者喜欢成对比较不同的选择方案以处理实际情况时，可以使用许多不同的表达方式。两种代表性的表达方式是概率语言偏好关系（PLPR），它起源于模糊语言方法；而分布偏好关系（DPR），它来自证据推理方法。尽管这两种表达方式看起来很相似，但它们的含义，操作和相关的决策过程却有很大不同。这给决策者带来了在不同实际情况下选择PLPR或DPR的挑战。为了解决这个问题，本文详细分析了PLPR和DPR之间的异同。分析是从五个角度进行的，包括决策问题建模，不确定性处理，偏好关系之间的一致性，信息聚合和启发过程。研究了汽车制造企业的工程师选择问题，以演示如何在考虑到决策者的偏好的情况下，如何正确选择PLPR或DPR来对实际情况中的决策问题进行建模和分析。