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Reducing algorithm aversion through experience

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

Abstract

In the context of an experiment, we examine the persistence of aversion towards algorithms in relation to learning processes. The subjects of the experiment are asked to make one share price forecast (rising or falling) in each of 40 rounds. A forecasting computer (algorithm) is available to them which has a success rate of 70%. Intuitive forecasts made by the subjects usually lead to a significantly poorer success rate. Feedback provided after each round of forecasts and a clear financial incentive lead to the subjects becoming better able to estimate their own forecasting abilities. At the same time, their aversion to algorithms also decreases significantly.

Suggested Citation

  • Filiz, Ibrahim & Judek, Jan René & Lorenz, Marco & Spiwoks, Markus, 2021. "Reducing algorithm aversion through experience," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
    • Handle: RePEc:eee:beexfi:v:31:y:2021:i:c:s221463502100068x
    DOI: 10.1016/j.jbef.2021.100524
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Ivanova-Stenzel, Radosveta & Tolksdorf, Michel, 2023. "Measuring Preferences for Algorithms - Are people really algorithm averse after seeing the algorithm perform?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277692, Verein für Socialpolitik / German Economic Association.
    2. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    3. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    4. Jan René Judek, 2024. "Willingness to Use Algorithms Varies with Social Information on Weak vs. Strong Adoption: An Experimental Study on Algorithm Aversion," FinTech, MDPI, vol. 3(1), pages 1-11, January.
    5. Alexia GAUDEUL & Caterina GIANNETTI, 2023. "Trade-offs in the design of financial algorithms," Discussion Papers 2023/288, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    6. Zulia Gubaydullina & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2022. "Comparing Different Kinds of Influence on an Algorithm in Its Forecasting Process and Their Impact on Algorithm Aversion," Businesses, MDPI, vol. 2(4), pages 1-23, October.
    7. Sutton, Steve G. & Arnold, Vicky & Holt, Matthew, 2023. "An extension of the theory of technology dominance: Capturing the underlying causal complexity," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).

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    More about this item

    Keywords

    Algorithm aversion; Overconfidence; Operating experience; Stock market forecasting; Behavioral finance; Experiments;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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