Information Systems Frontiers

, Volume 5, Issue 1, pp 47–61 | Cite as

Predicting the Future

  • Kay-Yut Chen
  • Leslie R. Fine
  • Bernardo A. Huberman

Abstract

We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncertain event and compare it to both the objective probability of its occurrence and the performance of the market as a whole. Experiments show that this nonlinear aggregation mechanism vastly outperforms both the imperfect market and the best of the participants. We then extend the mechanism to prove robust in the presence of public information.

information aggregation information markets public information experimental economics mechanism design 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson L., Holt C. Information cascades in the laboratory. American Economic Review 1997;87:847-862.Google Scholar
  2. Camerer C., Weigelt K. Information mirages in experimental asset Markets. Journal of Business 1991;64:463-493.Google Scholar
  3. Forsythe R., Lundholm R. Information aggregation in an experimental market. Econometrica 1990;58:309-347.Google Scholar
  4. Forsythe R., Palfrey T., Plott C. Asset valuation in an experimental market. Econometrica, 1982;50:537-567.Google Scholar
  5. Hayek F. The use of knowledge in society. American Economic Review, 1945;35(4):519-530.Google Scholar
  6. Iowa Electronic Markets. http.//www.biz.uiowa.edu/iem.Google Scholar
  7. Kullback S., Leibler R.A. On information and sufficiency. Annals of Mathematical Statistics, 1952;22:79-86.Google Scholar
  8. Markowitz H. Portfolio Selection, NY: Wiley, 1959.Google Scholar
  9. Nöth M., Camerer C., Plott C., Weber M. Information traps in experimental asset markets, Submitted to Review of Financial Studies, 1999.Google Scholar
  10. Nöth M., Weber M. Information aggregation with random ordering: Cascades and overconfidence. University of Mannheim, Discussion Paper, presented at the Summer 1998 ESA Meetings, 1998.Google Scholar
  11. O'Brien J., Srivastava S. Dynamic stock markets with multiple assets. Journal of Finance 1991;46:1811-1838.Google Scholar
  12. Pennock D., Lawrence S., Giles C., Nielsen F. The power of play: Efficient and forecast accuracy in web market games. NEC Research Institute Technical Report 2000-168, 2000.Google Scholar
  13. Plott C., Sunder S. Efficiency of experimental security markets with insider information: An application of rational expectations models. Journal of Political Economy 1982;90:663-698.Google Scholar
  14. Plott C., Sunder S. Rational expectations and the aggregation of diverse information in laboratory security markets. Econometrica 1988;56:1085-1118.Google Scholar
  15. Plott C., Wit J., Yang W. Pari-mutuel betting markets as information aggregation devices: Experimental results. Social Science Working Paper 986, California Institute of Technology, 1997.Google Scholar
  16. Scharfstein D., Stein J. Herd behavior and investment. American Economic Review 1990;80:465-479.Google Scholar
  17. Sunder S. Markets for information: Experimental evidence. Econometrica 1992;60:667-695.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Kay-Yut Chen
    • 1
  • Leslie R. Fine
    • 1
  • Bernardo A. Huberman
    • 1
  1. 1.HP LaboratoriesPalo AltoUSA

Personalised recommendations