Academic Journal
Decision Threshold Setting in Binary Classification Problems—A Behavioral Lens.
| Τίτλος: | Decision Threshold Setting in Binary Classification Problems—A Behavioral Lens. |
|---|---|
| Συγγραφείς: | Moder, Patrick1 (AUTHOR), Hoberg, Kai1 (AUTHOR) kai.hoberg@the‐klu.org, Papier, Felix2 (AUTHOR) |
| Πηγή: | Journal of Operations Management. Jun2026, Vol. 72 Issue 4, p536-555. 20p. |
| Θεματικοί όροι: | *Decision making, False positive error, Classification, Parameterization, False alarms |
| Περίληψη: | When binary classification models are wrong, managers face misclassification costs. Although false positive outcomes imply unnecessary mitigation efforts, false negative outcomes imply overlooking the class of interest. Humans calibrate these ai models supporting operational systems by adjusting the decision threshold that translates prediction probability into either class. Results of our controlled laboratory experiment show that, despite all relevant information being available, decision makers systematically deviate from the optimal cost‐efficient threshold. We observe a significant interaction effect of class and cost imbalance on this deviation, which increases in high‐stakes settings where more extreme thresholds are optimal. When unit costs are different, we find that participants anchor on the threshold where expected misclassification costs for false alarms and missed hits are equal, whereas mean anchoring cannot explain the pull‐to‐center behavior sufficiently. Surprisingly, we confirm that this impulse balance equilibrium also serves as attractive anchor in our setting, where decisions are made ex ante without loss aversion. To debias decision makers, simulated responses with behavior‐aware costs show that subjects are nudged to make choices closer to the optimum. Managers should be aware of this boundedly rational behavior and complementary debiasing techniques, as sub‐optimal threshold setting results in 53% higher misclassification costs, on average. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Operations Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Decision Threshold Setting in Binary Classification Problems—A Behavioral Lens. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Moder%2C+Patrick%22">Moder, Patrick</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hoberg%2C+Kai%22">Hoberg, Kai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kai.hoberg@the‐klu.org</i><br /><searchLink fieldCode="AR" term="%22Papier%2C+Felix%22">Papier, Felix</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Operations+Management%22">Journal of Operations Management</searchLink>. Jun2026, Vol. 72 Issue 4, p536-555. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22False+positive+error%22">False positive error</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Parameterization%22">Parameterization</searchLink><br /><searchLink fieldCode="DE" term="%22False+alarms%22">False alarms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: When binary classification models are wrong, managers face misclassification costs. Although false positive outcomes imply unnecessary mitigation efforts, false negative outcomes imply overlooking the class of interest. Humans calibrate these ai models supporting operational systems by adjusting the decision threshold that translates prediction probability into either class. Results of our controlled laboratory experiment show that, despite all relevant information being available, decision makers systematically deviate from the optimal cost‐efficient threshold. We observe a significant interaction effect of class and cost imbalance on this deviation, which increases in high‐stakes settings where more extreme thresholds are optimal. When unit costs are different, we find that participants anchor on the threshold where expected misclassification costs for false alarms and missed hits are equal, whereas mean anchoring cannot explain the pull‐to‐center behavior sufficiently. Surprisingly, we confirm that this impulse balance equilibrium also serves as attractive anchor in our setting, where decisions are made ex ante without loss aversion. To debias decision makers, simulated responses with behavior‐aware costs show that subjects are nudged to make choices closer to the optimum. Managers should be aware of this boundedly rational behavior and complementary debiasing techniques, as sub‐optimal threshold setting results in 53% higher misclassification costs, on average. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Operations Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/joom.70040 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 536 Subjects: – SubjectFull: Decision making Type: general – SubjectFull: False positive error Type: general – SubjectFull: Classification Type: general – SubjectFull: Parameterization Type: general – SubjectFull: False alarms Type: general Titles: – TitleFull: Decision Threshold Setting in Binary Classification Problems—A Behavioral Lens. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Moder, Patrick – PersonEntity: Name: NameFull: Hoberg, Kai – PersonEntity: Name: NameFull: Papier, Felix IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02726963 Numbering: – Type: volume Value: 72 – Type: issue Value: 4 Titles: – TitleFull: Journal of Operations Management Type: main |
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