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Reduzierung der Algorithm Aversion durch Erfahrung

Ibrahim Filiz, Jan René Judek, Marco Lorenz, Markus Spiwoks

sofia Diskussionsbeiträge 2021, No. 1 https://doi.org/10.46850/sofia.9783941627864

Wir untersuchen experimentell die Persistenz der Algorithm Aversion im Hinblick auf Lernprozesse. Probanden sind aufgefordert in 40 Runden je eine Aktienkursprognose (steigend oder fallend) abzugeben. Es steht ein Prognosecomputer (Algorithmus) zur Verfügung, der eine Erfolgsquote von 70% aufweist. Intuitive Prognosen der Probanden führen in aller Regel zu einer deutlich schlechteren Erfolgsquote. Feedbacks nach jeder Prognoserunde und ein klarer ökonomischer Anreiz führen dazu, dass die Probanden ihre eigenen Prognosefähigkeiten besser einzuschätzen lernen. Dabei geht auch die Algorithm Aversion signifikant zurück.

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