A common technique for eliciting subjective probabilities is to provide a set of exclusive and exhaustive events and ask the assessor to estimate the probabilities of such events. However, such subjective probabilities estimations are usually subjected to a bias known as the partition dependence bias. This study aims to investigate the effect of state space partitioning and the level of knowledge on subjective probability estimations. The state space is partitioned into full, collapsed, and pruned trees, while the knowledge is manipulated into low and high levels. A scenario called “Best Bank Award” was developed and a 2 × 3 experimental design was employed to explore the effect of the level of knowledge and the partitioning of the state space on the subjective probability. A total of 627 professionals participated in the study and 543 valid responses were used for analysis. The results of two-way ANOVA with the Tukey HSD test for post hoc analysis indicate a mean probability of 24.2% for the full tree, which is significantly lower than those of the collapsed (35.7%) as well as pruned (36.3%) trees. Moreover, there is significant difference in the mean probabilities between the low (38.1%) and high (24.9%) knowledge levels. The results support the hypotheses that the partitioning of the state space as well as the level of knowledge affects subjective probability estimation. The study demonstrates that regardless of the level of knowledge, the partition dependence bias is robust. However, the subjective probability accuracy improves with more knowledge.

得出主观概率的常用技术是提供一组排他且详尽的事件，并要求评估者估计此类事件的概率。然而，这种主观概率估计通常会受到称为分区依赖偏差的偏差的影响。本研究旨在研究状态空间划分和知识水平对主观概率估计的影响。状态空间被划分为完整树、折叠树和修剪树，而知识则被操纵为低层和高层。开发了一个名为“最佳银行奖”的场景，并采用2×3实验设计来探讨知识水平和状态空间划分对主观概率的影响。共有 627 名专业人士参与了这项研究，并使用了 543 份有效回复进行分析。用于事后分析的 Tukey HSD 检验的双向方差分析结果表明，整棵树的平均概率为 24.2%，明显低于倒塌树 (35.7%) 和修剪树 (36.3%) 的概率。此外，低知识水平（38.1%）和高知识水平（24.9%）之间的平均概率存在显着差异。结果支持状态空间的划分以及知识水平影响主观概率估计的假设。研究表明，无论知识水平如何，分区依赖偏差都是稳健的。然而，随着知识的增加，主观概率的准确性会提高。