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Effect of knowledge differentiation and state space partitioning on subjective probability estimation
1KFUPM Business School, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Science Progress
|April 16, 2021
Summary
Subjective probability estimates are biased by how information is presented. This study shows that both the way probabilities are partitioned and the level of knowledge significantly impact these estimates, with more knowledge improving accuracy.
Area of Science:
- Cognitive Psychology
- Decision Science
- Behavioral Economics
Background:
- Subjective probability estimation is crucial in decision-making.
- Estimates are often biased, notably by partition dependence.
- Understanding factors influencing these biases is essential.
Purpose of the Study:
- To investigate the impact of state space partitioning on subjective probability estimation.
- To examine how the level of knowledge influences subjective probability estimates.
- To explore the interaction between partitioning and knowledge on probability biases.
Main Methods:
- A 2x3 experimental design was used with varying state space partitions (full, collapsed, pruned) and knowledge levels (low, high).
- A "Best Bank Award" scenario was developed for eliciting probabilities.
- Two-way ANOVA and Tukey HSD tests were employed for analysis with 543 valid responses from 627 professionals.
Main Results:
- The full tree partition (24.2%) yielded significantly lower probabilities than collapsed (35.7%) and pruned (36.3%) trees.
- Low knowledge levels (38.1%) resulted in significantly higher probabilities than high knowledge levels (24.9%).
- Partition dependence bias was found to be robust across knowledge levels.
Conclusions:
- Both state space partitioning and the level of knowledge significantly affect subjective probability estimations.
- Partition dependence bias persists irrespective of the assessor's knowledge level.
- Increased knowledge demonstrably enhances the accuracy of subjective probability assessments.
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