Advertisement

How to Collect Private Signals in Information Cascade: An Empirical Study

  • Kota TakedaEmail author
  • Masato Hisakado
  • Shintaro Mori
Conference paper
  • 60 Downloads
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

In the information cascade experiment, several subjects sequentially answer a two-choice question, after referring to previous subjects’ choices. Information cascade is defined as a tendency to follow the majority choice, even if, one’s private signal suggests the minority choice. When information cascade occurs, the private signal is lost, and the collective intelligence mechanism does not work. If the majority’s choice is wrong at the onset of the information cascade, it continues to be wrong forever. How can we find the correct choice even when the majority choice is wrong? In this study, we investigate a Bayesian Inference method, which collects private signals in the information cascade, based on the choice behavior of the subjects. Using the empirical data of an experiment, we estimate the probabilistic rule of the choice behavior. We demonstrate that the Bayesian algorithm works and one can know the correct choice even if the majority’s choice is wrong.

Keywords

Information cascade Bayesian modelling Empirical analysis 

References

  1. 1.
    Wang, T., Wang, D.: Why Amason’s ratings might mislead you. Big Data 2, 196 (2014)CrossRefGoogle Scholar
  2. 2.
    Rendell, L., Boyd, R., Cownden, D., Enquist, M., Eriksson, K., Feldman, M.W., Fogarty, L., Ghirlanda, S., Lillicrap, T., Laland, K.N.: Why copy others? Insights from the social learning strategies tournament. Science 328, 208 (2010)ADSMathSciNetCrossRefGoogle Scholar
  3. 3.
    Nakayama, K., Hisakado, M., Mori., S.: Nash equilibrium of social-learning agents in a restless multiarmed bandit game. Sci. Rep. 7 (2017). Article number: 1937Google Scholar
  4. 4.
    Bikhchandani, S., Hirshleifer, D., Welch, I.: A theory of fads, fashion, custom, and cultural change as information cascades. J. Polit. Econ. 100, 992–1026 (1992)CrossRefGoogle Scholar
  5. 5.
    Devenow, A., Welch, I.: Eur. Econ. Rev. 40, 603–615 (1996)CrossRefGoogle Scholar
  6. 6.
    Surowiecki, J.: The Wisdom of Crowds. Doubleday, New York (2004)Google Scholar
  7. 7.
    Page, S.E.: The Difference. Princeton University Press, Princeton (2007)zbMATHGoogle Scholar
  8. 8.
    Hino, M., Irie, Y., Hisakado, M., Takahashi, T., Mori, S.: Detection of phase transition in generalized Póla urn in information cascade experiment. J. Phys. Soc. Jpn. 85(3), 034002–034013 (2016)ADSCrossRefGoogle Scholar
  9. 9.
    Mori, S., Hisakado, M.: Information cascade experiment: Urn Quiz. In: Sato, A.H. (ed.) Applications of Data-Centric Science to Social Design. Agent-Based Social Systems, vol. 14, pp. 181–191. Springer, Singapore (2016)Google Scholar
  10. 10.
    Anderson, L., Holt, C.: Information cascades in the laboratory. Am. Econ. Rev. 87(5), 847–862 (1997)Google Scholar
  11. 11.
    Mori, S., Hisakado, M., Takahashi, T.: Phase transition to two-peaks phase in an information cascade voting experiment. Phys. Rev. E 86, 026109 (2012)ADSCrossRefGoogle Scholar
  12. 12.
    Goeree, J.K., Palfrey, T.R., Rogers, B.W., McKelvey, R.D.: Self-correcting information Cascades. Rev. Econ. Stud.74, 733–762 (2007)CrossRefGoogle Scholar
  13. 13.
    Eguíluz V.M., Masuda, N., Fernández-Gracia, J.: Bayesian decision making in human collectives with binary choices. PLoS One 10(4), e0121332 (2015).  https://doi.org/10.1371/journal.pone.0121332 CrossRefGoogle Scholar
  14. 14.
    Hisakado M., Mori S.: Information cascade and bayes formula. In: Sato, A.H. (ed.) Applications of Data-Centric Science to Social Design. Agent-Based Social Systems, Chapter 12, vol. 14, pp. 193–202. Springer, Singapore (2019)Google Scholar
  15. 15.
    Hill, B., Lane, D., Sudderth, W.: A strong law for some gener-alized urn processes. Ann. Prob. 8, 214–226 (1980)CrossRefGoogle Scholar
  16. 16.
    Mori, S., Hisakado, M.: Correlation function for generalized Polya urns: finite-size scaling analysis. Phys. Rev. E92, 052112 (2015)ADSGoogle Scholar
  17. 17.
    Mori, S., Hisakado, M.: Finite-size scaling analysis of binary stochastic processes and universality classes of information cascade phase transition. J. Phys. Soc. Jpn. 84, 054001 (2015)ADSCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Graduate School of Science and TechnologyHirosaki UniversityHirosaki, AomoriJapan
  2. 2.Nomura Holdings, Inc.Chiyoda-ku, TokyoJapan

Personalised recommendations