Zum Hauptinhalt springen Skip to page footer

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 Beharrlichkeit 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.

Access full article

References

  1. Anderson, A. (2013). Trading and Under-Diversification, Review of Finance, 17(5), 1699–1741. DOI: https://doi.org/10.1093/rof/rfs044.
  2. Anzanello, M. J. & Fogliatto, F. S. (2011). Learning curve models and applications: Literature review and research directions, International Journal of Industrial Ergonomics, 41(5), 573-583. DOI: https://doi.org/10.1016/j.ergon.2011.05.001.
  3. Barber, B. M. & Odean, T. (2001). Boys Will Be Boys: Gender, Overconfidence, And Common Stock Investment, Quarterly Journal of Economics, 116(1), 261-292. DOI: https://doi.org/10.1162/003355301556400.
  4. Barber, B. M. & Odean, T. (2000). Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors, The Journal of Finance, 55(2), 773-806. DOI: https://doi.org/10.1111/0022-1082.00226.
  5. Becker, O., Leitner, J. & Leopold-Wildburger, U. (2009). Expectation formation and regime switches, Experimental Economics, 12(3), 350-364. DOI: https://doi.org/10.1007/s10683-009-9213-0.
  6. Beketov, M., Lehmann, K. & Wittke, M. (2018). Robo Advisors: quantitative methods inside the robots, Journal of Asset Management, 19, 363–370. DOI: https://doi.org/10.1057/s41260-018-0092-9.
  7. Bhatia, A., Chandani, A. & Chhateja, J. (2020). Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context, Journal of Behavioral and Experimental Finance, 25. DOI: https://doi.org/10.1016/j.jbef.2020.100281.
  8. Castelo, N., Bos, M. W. & Lehmann, D. R. (2019). Task-dependent algorithm aversion, Journal of Marketing Research, 56(5), 809-825. DOI: https://doi.org/10.1177%2F0022243719851788.
  9. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed), Hillsdale, N.J., L. Erlbaum Associates.
  10. Cohen, J. (1992). A power primer, Psychological bulletin, 112(1), 155-159. DOI: https://doi.apa.org/doi/10.1037/0033-2909.112.1.155.
  11. D’Acunto, F., Prabhala, N. & Rossi, A. G. (2019). The Promises and Pitfalls of Robo-Advising, The Review of Financial Studies, 32(5), 1983–2020. DOI: https://doi.org/10.1093/rfs/hhz014.
  12. Dietvorst, B. J., Simmons, J. P. & Massey, C. (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them, Management Science, 64(3), 1155-1170. DOI: https://doi.org/10.1287/mnsc.2016.2643.
  13. Dietvorst, B. J., Simmons, J. P. & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err, Journal of Experimental Psychology: General, 144(1), 114-126. DOI: https://doi.apa.org/doi/10.1037/xge0000033.
  14. Dimmock, S. G., Kouwenberg, R., Mitchell, O. S. & Peijnenburg, K. (2016). Am-biguity Aversion and Household Portfolio Choice Puzzles: Empirical Evidence, Journal of Financial Economics, 119, 559-577. DOI: https://doi.org/10.1016/j.jfineco.2016.01.003.
  15. Efendić, E., Van de Calseyde, P. P. & Evans, A. M. (2020). Slow response times undermine trust in algorithmic (but not human) predictions, Organizational Behavior and Human Decision Processes, 157, 103-114. DOI: https://doi.org/10.1016/j.obhdp.2020.01.008.
  16. Erlei, A., Nekdem, F., Meub, L., Anand, A. & Gadiraju, U. (2020). Impact of Algorithmic Decision Making on Human Behavior: Evidence from Ultimatum Bargaining, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1), 43-52.
  17. Filiz, I., Nahmer, T. und Spiwoks, M. (2019). Herd behavior and mood: An experimental study on the forecasting of share prices, Journal of Behavioral and Experimental Finance, 24, 1-10. DOI: https://doi.org/10.1016/j.jbef.2019.07.004.
  18. Fischbacher, U. (2007). z-Tree: Zurich Toolbox for Ready-made Economic Experiments, Experimental Economics, 10(2), 171–178. DOI: https://doi.org/10.1007/s10683-006-9159-4.
  19. Fritz, C. O., Morris, P. E. & Richler, J. J. (2012). Effect Size Estimates: Current Use, Calculations, and Interpretation, Journal of Experimental Psychology: General, 141(1), 2–18. DOI: https://doi.apa.org/doi/10.1037/a0024338.
  20. Frydman, C. & Camerer, C. F. (2016). The Psychology and Neuroscience of Financial Decision Making, Trends in Cognitive Sciences, 20(9), 661-675. DOI: https://doi.org/10.1016/j.tics.2016.07.003.
  21. Gilovich, T., Vallone, R. & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences, Cognitive psychology, 17(3), 295-314. DOI: https://doi.org/10.1016/0010-0285(85)90010-6.
  22. Goetzmann, W. N. & Kumar, A. (2008). Equity Portfolio Diversification, Review of Finance, 12(3), 433-463. DOI: https://doi.org/10.1093/rof/rfn005.
  23. Hibbert, A. M., Lawrence, E. R. & Prakash, A. J. (2012). Can Diversification Be Learned? The Journal of Behavioral Finance, 13(1), 38-50. DOI: https://doi.org/10.1080/15427560.2012.654547.
  24. Jung, D., Dorner, V., Glaser, F. & Morana, S. (2018). Robo-Advisory - Digitalization and Automation of Financial Advisory, Business & Information Systems Engineering, 60(1), 81-86. DOI: https://doi.org/10.1007/s12599-018-0521-9.
  25. Köbis, N. & Mossink, L. D. (2020). Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry, Computers in Human Behavior, 114, 1-13. DOI: https://doi.org/10.1016/j.chb.2020.106553.
  26. Ku, C. Y. (2020). When AIs Say Yes and I Say No: On the Tension between AI’s Decision and Human’s Decision from the Epistemological Perspectives, Információs Társadalom, 19(4), 61-76.
  27. Kudryavtsev, A., Cohen, G. & Hon-Snir, S. (2013). “Rational” or “Intuitive”: Are Behavioral Biases Correlated Across Stock Market Investors? Contemporary Economics, 7(2), 31-53.
  28. Meub, L., Proeger, T., Bizer, K. & Spiwoks, M. (2015). Strategic coordination in forecasting - An experimental study, Finance Research Letters, 13(1), 155-162. DOI: https://doi.org/10.1016/j.frl.2015.02.001.
  29. Prahl, A. & Van Swol, L. (2017). Understanding algorithm aversion: When is advice from automation discounted? Journal of Forecasting, 36(6), 691-702. DOI: https://doi.org/10.1002/for.2464.
  30. Proeger, T. & Meub, L. (2014). Overconfidence as a Social Bias: Experimental Evidence, in: Economics Letters, 122(2), 203-207. DOI: https://doi.org/10.1016/j.econlet.2013.11.027.
  31. Roberts, H. V. (1959). Stock market ‘‘patterns’’ and financial analysis: Methodological suggestions, Journal of Finance, 1(14), 1–10. DOI: https://doi.org/10.1111/j.1540-6261.1959.tb00481.x.
  32. Rossi, A. G. & Utkus, S. P. (2020). Who Benefits from Robo-advising? Evidence from Machine Learning, SSRN Working Paper, https://ssrn.com/ab-stract=3552671. DOI: http://dx.doi.org/10.2139/ssrn.3552671.
  33. Rühr, A., Streich, D., Berger, B. & Hess, T. (2019). A Classification of Decision Automation and Delegation in Digital Investment Systems, in: Proceedings of the 52nd Hawaii International Conference on System Sciences, S. 1435-1444. DOI: https://doi.org/10.24251/HICSS.2019.174.
  34. Singh, I. & Kaur, N. (2017). Wealth Management Through Robo Advisory, International Journal of Research - Granthaalayah, 5(6), 33-43. DOI: https://doi.org/10.29121/granthaalayah.v5.i6.2017.1991.
  35. Uhl, M. W. & Rohner, P. (2018). Robo-advisors versus traditional investment advisors: An unequal game, The Journal of Wealth Management, 21(1), 44-50. DOI: https://doi.org/10.3905/jwm.2018.21.1.044.
  36. Wärneryd, K.-E. (2001). Stock-market psychology, Cheltenham: Edward Elgar.
  37. Wright, T. P. (1936). Factors affecting the cost of airplanes, Journal of the Aeronautical Sciences, 3(4), 122-128. DOI: https://doi.org/10.2514/8.155.
  38. Zielonka, P. (2004). Technical analysis as the representation of typical cognitive biases, International Review of Financial Analysis, 13, 217–225. DOI: https://doi.org/10.1016/j.irfa.2004.02.007.