Advertisement

Journal of Risk and Uncertainty

, Volume 30, Issue 3, pp 195–209 | Cite as

The Gambler’s Fallacy and the Hot Hand: Empirical Data from Casinos

  • Rachel Croson
  • James Sundali
Article

Abstract

Research on decision making under uncertainty demonstrates that intuitive ideas of randomness depart systematically from the laws of chance. Two such departures involving random sequences of events have been documented in the laboratory, the gambler’s fallacy and the hot hand. This study presents results from the field, using videotapes of patrons gambling in a casino, to examine the existence and extent of these biases in naturalistic settings. We find small but significant biases in our population, consistent with those observed in the lab.

Keywords

perceptions of randomness uncertainty field study 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brown, William and Raymond Sauer. (1993). “Does the Basketball Market Believe in the Hot Hand? Comment,” American Economic Review 83, 1377–1386.Google Scholar
  2. Cahart, Mark. (1997). “On Persistence in Mutual Fund Performance,” Journal of Finance 52, 57–82.Google Scholar
  3. Camerer, Colin. (1989). “Does the Basketball Market Believe in the ‘Hot Hand’?” American Economic Review 79, 1257–1261.Google Scholar
  4. Camerer, Colin. (1998). “Can Asset Markets be Manipulated? A Field Experiment with Racetrack Betting,” Journal of Political Economy 106, 457–482.CrossRefGoogle Scholar
  5. Chau, Albert and James Phillips. (1995). “Effects of Perceived Control Upon Wagering and Attributions in Computer Blackjack,” The Journal of General Psychology 122, 253–269.Google Scholar
  6. Clotfelter, Charles and Phil Cook. (1989). Selling Hope: State Lotteries in America. Cambridge: Harvard University Press.Google Scholar
  7. Clotfelter, Charles and Phil Cook. (1991). “Lotteries in the Real World,” Journal of Risk and Uncertainty 4, 227–232.CrossRefGoogle Scholar
  8. Clotfelter, Charles and Phil Cook. (1993). “The ‘Gambler’s Fallacy’ in Lottery Play,” Management Science 39, 1521–1525.Google Scholar
  9. Estes, William. (1964). “Probability Learning.” In A.W. Melton (ed.), Categories of Human Learning. New York: Academic Press.Google Scholar
  10. Ethier, Stuart. (1982). “Testing for Favorable Numbers on a Roulette Wheel,” Journal of the American Statistical Association 77, 660–665.Google Scholar
  11. Gilovich, Thomas, Robert Vallone and Amos Tversky. (1985). “The Hot Hand in Basketball: On the Misperception of Random Sequences,” Cognitive Psychology 17, 295–314.CrossRefGoogle Scholar
  12. Golec, Joseph and Murray Tamarkin. (1998). “Bettors Love Skewness, Not Risk, at the Horse Track.” Journal of Political Economy 106, 205–225.CrossRefGoogle Scholar
  13. Langer, Ellen. (1975). “The Illusion of Control,” Journal of Personality and Social Psychology 32, 311–328.CrossRefGoogle Scholar
  14. Laplace, Pierre. (1820: 1951) Philosophical Essays on Probabilities, translated by F. W. Truscott and F. L. Emory. New York: Dover.Google Scholar
  15. Metzger, Mary. (1984). “Biases in Betting: An Application of Laboratory Findings,” Psychological Reports 56, 883–888.Google Scholar
  16. Mullainathan, Sendhil. (2002). “Thinking Through Categories,” Working Paper, Department of Economics, Massachusetts Institute of Technology.Google Scholar
  17. Odean, Terrence. (1998). “Are Investors Reluctant to Realize their Losses?” Journal of Finance 53, 1775–1789.CrossRefGoogle Scholar
  18. Quandt, Richard. (1986). “Betting and Equilibrium,” Quarterly Journal of Economics 101, 201–207.Google Scholar
  19. Rabin, Matthew. (2002). “Inference by Believers in the Law of Small Numbers,” Quarterly Journal of Economics 157, 775–816.CrossRefGoogle Scholar
  20. Ritov, Ilana and Jonathan Baron. (1992). “Status Quo and Omission Bias,” Journal of Risk and Uncertainty 5, 49–61.CrossRefGoogle Scholar
  21. Samuelson, William and Richard Zeckhauser. (1988). “Status Quo Bias in Decision Making,” Journal of Risk and Uncertainty 1, 7–59.CrossRefGoogle Scholar
  22. Shefrin, Hersh and Meir Statman. (1985). “The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence,” Journal of Finance 40, 777–790.Google Scholar
  23. Sirri, Erik and Peter Tufano. (1998). “Costly Search and Mutual Fund Flows,” Journal of Finance 53, 1589-1622.CrossRefGoogle Scholar
  24. Sundali, James and Rachel Croson. (2004). “Biases in Casino Betting: The Hot Hand and the Gambler’s Fallacy,” Working Paper, The Wharton School, University of Pennsylvania.Google Scholar
  25. Terrell, Dek. (1994). “A Test of the Gambler’s Fallacy: Evidence from Pari-Mutuel Games,” Journal of Risk and Uncertainty 8, 309–317.CrossRefGoogle Scholar
  26. Terrell, Dek. (1998). “Biases in Assessments of Probabilities: New Evidence from Greyhound Races,” Journal of Risk and Uncertainty 17, 151–166.CrossRefGoogle Scholar
  27. Terrell, Dek and Amy Farmer. (1996). “Optimal Betting and Efficiency in Parimutuel Betting Markets with Information Costs,” The Economic Journal 106, 846–868.Google Scholar
  28. Tversky, Amos and Daniel Kahneman. (1971). “Belief in the Law of Small Numbers,” Psychological Bulletin 76, 105–110.Google Scholar
  29. Wagenaar, Wilhelm. (1988). Paradoxes of Gambling Behavior. London: Lawrence Erlbaum.Google Scholar
  30. Walker, Mark and John Wooders. (2001). “Minimax Play at Wimbledon,” American Economic Review 91, 1521–1538.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.The Wharton SchoolUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Managerial Sciences/028University of NevadaReno, Reno

Personalised recommendations