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Algorithm Aversion als Hindernis bei der Etablierung von Robo Advisors

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

sofia Diskussionsbeiträge 2022, No. 2 https://doi.org/10.46850/sofia.9783947850006

Wir untersuchen im Rahmen eines ökonomischen Laborexperimentes, wie hinderlich sich die Algorithm Aversion bei der Etablierung von Robo Advisors auswirkt. Die Probanden müssen Diversifikationsaufgaben bewältigen. Sie können dies selbst tun oder sie können einen Robo Advisor mit dieser Aufgabe betrauen. Der Robo Advisor wertet alle relevanten Daten aus und trifft stets die Entscheidung, die zum höchsten Erwartungswert der Vergütung für den Probanden führt. Obwohl die hohe Leistungsfähigkeit des Robo Advisors offensichtlich ist, vertrauen die Probanden nur in rund 40% aller Entscheidungen dem Robo Advisor. Damit reduzieren sie ihren Erfolg und ihre Vergütung. Viele Probanden orientieren sich an der 1/n-Heuristik, was zu ihren suboptimalen Entscheidungen beiträgt. Sofern die Probanden für andere entscheiden müssen, geben sie sich erkennbar mehr Mühe und sind auch erfolgreicher, als wenn sie für sich selbst entscheiden. Dies wirkt sich jedoch nicht auf die Akzeptanz des Robo Advisors aus. Auch bei Stellvertreter-Entscheidungen wird der Robo Advisor nur in rund 40% der Fälle in Anspruch genommen. Die Neigung der Wirtschaftssubjekte zur Algorithm Aversion steht einer breiten Etablierung von Robo Advisors im Weg.

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