Effect of knowledge differentiation and state space partitioning on subjective probability estimation

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 x 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.

Automated summary

This study investigates the influence of state space partitioning and knowledge levels on subjective probability estimation, revealing the presence of partition dependence bias in probability estimation but an improvement in accuracy with increasing knowledge levels.