Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/315417 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Quantitative Marketing and Economics [ISSN:] 1573-711X [Volume:] 22 [Issue:] 4 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 445-483
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
Artificial Intelligence has the potential to improve human decisions in complex environments, but its effectiveness can remain limited if humans hold context-specific private information. Using the empirical example of antibiotic prescribing for urinary tract infections, we show that full automation of prescribing fails to improve on physician decisions. Instead, optimally delegating a share of decisions to physicians, where they possess private diagnostic information, effectively utilizes the complementarity between algorithmic and human decisions. Combining physician and algorithmic decisions can achieve a reduction in inefficient overprescribing of antibiotics by 20.3 percent.
Schlagwörter: 
Human-machine complementarity
Machine learning
Antibiotic resistance
Antibiotic prescribing
JEL: 
C53
D83
I18
I19
L2
M15
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
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Dokumentart: 
Article
Dokumentversion: 
Published Version

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