Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment

Tiffany Tsz Kwan Tse, Nobuyuki Hanaki and Bolin Mao

ISER Discussion Paper from Institute of Social and Economic Research, The University of Osaka

Abstract: We experimentally investigated the relationship between participants' reliance on algorithms, their familiarity with the task, and the performance level of the algorithm. We found that when participants could freely decide on their final forecast after observing the one produced by the algorithm (a condition found to mitigate algorithm aversion), the average degree of reliance on high and low performing algorithms did not significantly differ for participants with little experience in the task. Experienced participants relied less on the algorithm than inexperienced participants, regardless of its performance level. The reliance on the low performing algorithm was positive even when participants could infer that they outperformed the algorithm. Indeed, participants would have done better without relying on the low performing algorithm at all. Our results suggest that, at least in some domains, excessive reliance on algorithms, rather than algorithm aversion, should be a concern.

Date: 2022-10, Revised 2024-03
New Economics Papers: this item is included in nep-exp
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Journal Article: Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment (2024)
Working Paper: Beware the Performance of an Algorithm Before Relying on it: Evidence from a Stock Price Forecasting Experiment (2022)
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