Between Confidence and Clarity: Objective and Subjective Expertise Differentially Predict Use of Alignable and Nonalignable Product Attributes

Samuel H. Borislow & Daniel M. Bartels

Journal of Behavioral Decision Making2026https://doi.org/10.1002/bdm.70073article
AJG 3ABDC A
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0.50

Abstract

Expertise is multifaceted. This project builds on the difference between subjective expertise (the confidence one holds in understanding a particular topic) and objective expertise (one's actual understanding of that topic), which can vary independently. We examine how subjective and objective expertise differentially relate to making better choices and seeking additional choice‐relevant information. We hypothesized that some participants would be poorly calibrated in their ability to assess the value of more difficult‐to‐evaluate nonalignable attributes (in our setting, categorical attributes that are present or absent, such as torque‐vectoring) compared with easier‐to‐evaluate alignable attributes (e.g., miles per gallon). We measure and manipulate subjective and objective expertise and find that participants with greater objective expertise (either naturally or through provided information) tend to base their choices on the values of nonalignable attributes and that this pattern is generally not predicted by measures of subjective expertise. High subjective expertise, instead, predicts a reduced willingness to seek additional information or take advice, as well as a greater willingness to pay for customized items, independent of objective expertise. These studies point to important differences between these facets of expertise, underscoring the importance of incorporating measures of each when examining the role of expertise in decision‐making.

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https://doi.org/https://doi.org/10.1002/bdm.70073

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@article{samuel2026,
  title        = {{Between Confidence and Clarity: Objective and Subjective Expertise Differentially Predict Use of Alignable and Nonalignable Product Attributes}},
  author       = {Samuel H. Borislow & Daniel M. Bartels},
  journal      = {Journal of Behavioral Decision Making},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/bdm.70073},
}

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R · text relevance †0.50 × 0.4 = 0.20

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