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Journal of Risk and Uncertainty

, Volume 57, Issue 3, pp 225–252 | Cite as

Decision irrationalities involving deadly risks

  • W. Kip ViscusiEmail author
  • Scott DeAngelis
Article
  • 98 Downloads

Abstract

This article provides an experimental analysis of two-armed bandit problems that have a different structure in which the first unsuccessful outcome leads to termination of the game. It differs from a conventional two-armed bandit problem in that there is no opportunity to alter behavior after an unsuccessful outcome. Introducing the risk of death into a sequential decision problem alters the structure of the problem. Even though play ends after an unsuccessful outcome, Bayesian learning after successful outcomes has a potential function in this class of two-armed bandit problems. Increasing uncertainty boosts the chance of long-term survival since ambiguous probabilities of survival are increased more after each successful outcome. In the independent choice experiments, a slim majority of participants displayed a preference for greater risk ambiguity. Particularly in the interdependent choice experiments, participants were overly deterred by ambiguity. For both independent and interdependent choices, there were several dimensions on which participants displayed within session rationality. However, participants failed to learn and improve their strategy over a series of rounds, which is consistent with evidence of bounded rationality in other challenging games.

Keywords

Two-armed bandit Risk Uncertainty Bayesian learning Ambiguity aversion Belief updating 

JEL Classifications

D80 D91 C91 

Notes

Acknowledgements

The authors are indebted to Gary Charness for superb suggestions that greatly improved the manuscript.

References

  1. Anderson, C. M. (2012). Ambiguity aversion in multi-armed bandit problems. Theory and Decision, 72(1), 15–33.CrossRefGoogle Scholar
  2. Bellemare, C., Kröger, S., & Sossou, K. M. (2018). Reporting probabilistic expectations with dynamic uncertainty about possible distributions. Journal of Risk and Uncertainty, 57(2), 153–176.CrossRefGoogle Scholar
  3. Berry, D. A. (1972). A Bernoulli two-armed bandit. The Annals of Mathematical Statistics, 43(3), 871–897.CrossRefGoogle Scholar
  4. Berry, D. A., & Fristedt, B. (1985). Bandit problems: Sequential allocation of experiments. London: Chapman and Hall.CrossRefGoogle Scholar
  5. Berry, D. A., & Viscusi, W. K. (1981). Bernoulli two-armed bandits with geometric termination. Stochastic Processes and their Applications, 11(1), 35–45.CrossRefGoogle Scholar
  6. Bradt, R. N., Johnson, S. M., & Karlin, S. (1956). On sequential designs for maximizing the sum of n observations. The Annals of Mathematical Statistics, 2(4), 1060–1074.CrossRefGoogle Scholar
  7. Camerer, C., & Weber, M. (1992). Recent developments in modeling preferences: Uncertainty and ambiguity. Journal of Risk and Uncertainty, 5(4), 325–370.CrossRefGoogle Scholar
  8. Charness, G., & Levin, D. (2005). When optimal choices feel wrong: A laboratory study of Bayesian updating, complexity, and affect. American Economic Review, 95(4), 1300–1309.CrossRefGoogle Scholar
  9. Charness, G., & Levin, D. (2009). The origin of the winner’s curse: A laboratory study. American Economic Journal: Microeconomics, 1(1), 207–236.Google Scholar
  10. Charness, G., Karni, E., & Levin, D. (2007). Individual and group decision making under risk: An experimental study of Bayesian updating and violations of first-order stochastic dominance. Journal of Risk and Uncertainty, 35(2), 129–148.CrossRefGoogle Scholar
  11. Ellsberg, D. (1961). Risk, ambiguity, and the Savage axioms. Quarterly Journal of Economics, 75(4), 643–669.CrossRefGoogle Scholar
  12. Engle-Warnick, J., & Laszlo, S. (2017). Learning-by-doing in an ambiguous environment. Journal of Risk and Uncertainty, 55(1), 71–94.CrossRefGoogle Scholar
  13. Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American Economic Review, 92(5), 1644–1655.CrossRefGoogle Scholar
  14. Knight, F. H. (1921). Risk, uncertainty, and profit. Chicago: University of Chicago Press.Google Scholar
  15. Liu, H.-H., & Colman, A. M. (2009). Ambiguity aversion in the long run: Repeated decisions under risk and uncertainty. Journal of Economic Psychology, 30(3), 277–284.CrossRefGoogle Scholar
  16. Machina, M. J., & Siniscalchi, M. (2014). Ambiguity and ambiguity aversion. In M. J. Machina & W. K. Viscusi (Eds.), Handbook of the economics of risk and uncertainty (Vol. 1, pp. 729–807). Amsterdam: Elsevier B.V.Google Scholar
  17. Moreno, O. M., & Rosokha, Y. (2016). Learning under compound risk vs. learning under ambiguity—An experiment. Journal of Risk and Uncertainty, 53(2–3), 137–162.CrossRefGoogle Scholar
  18. Park, H. M. (2009). Comparing group means: T-tests and one-way ANOVA using STATA, SAS, R, and SPSS. Working Paper. The University Information Technology Services (UITS) Center for Statistical and Mathematical Computing, Indiana University.Google Scholar
  19. Trautmann, S. T., & Zeckhauser, R. J. (2013). Shunning uncertainty: The neglect of learning opportunities. Games and Economic Behavior, 79(1), 44–55.CrossRefGoogle Scholar
  20. Viscusi, W. K. (1979). Employment hazards: An investigation of market performance. Cambridge: Harvard University Press.Google Scholar
  21. Yakowitz, S. J. (1969). Mathematics of adaptive control processes. New York: American Elsevier.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Vanderbilt Law SchoolNashvilleUSA
  2. 2.Robinson, Bradshaw, & HinsonCharlotteUSA

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