Skip to main content

Failing to learn from experience about catastrophes: The case of hurricane preparedness


This paper explores the question of whether there are inherent limits to our ability to learn from experience about the value of protection against low-probability, high-consequence, events. Findings are reported from two controlled experiments in which participants have a monetary incentive to learn from experience making investments to protect against hurricane risks. A central finding is that investments display a short-term forgetting effect consistent with the use of reinforcement learning rules, where a significant driver of investments in a given period is whether storm losses were incurred in the precious period. Given the relative rarity of such losses, this reinforcement process produces a mean investment level below that which would be optimal for most storm threats. Investments are also found to be insensitive to the censoring effect of protection itself, implying that the size of experienced losses—rather than losses that are avoided—is the primary driver of investment decisions.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. The slate was wiped clean after each year to allow participants to play three independent replicates of the basic decision task, with the only carry-over being expertise.

  2. This optimality pattern was a consequence of the storm damage function programmed in the simulation, where the potential home loss was a discrete step function of a storm’s strength and proximity.

  3. The sharp decrease in mitigation observed at the very end of the simulation shown in Fig. 5 might be attributed to an end-game effect, where the impending end of tenure in the home triggered a reluctance to make any fixed investments in protection—even though such a reduction would have no rational basis given the payoff structure of the game.

  4. We also estimated an ordered logit model that assumed decisions of both whether and how much to invest were driven by a single, homogeneous process. This single stage model, however, provided a poorer account of the data than one that partitioned the data into two stages.

  5. In exploratory analyses we also examined models recognizing higher-order lag effects (up to fifth order), however, due to the loss of efficiency in estimates of higher-order lags, we focus here on the first two.

  6. Mixed-model maximum-likelihood estimates derived under the assumption of more general (e.g., AR-1) error structures yield similar results.


  • Bajak, F. (2010). Chile was ready for quake, Haiti wasn’t.

  • Brinkley, D. (2006). The great deluge: Hurricane Katrina, New Orleans, and the Mississippi Gulf Coast. New York: Harper Collins.

    Google Scholar 

  • Browne, M. J., & Hoyt, R. E. (2000). The demand for flood insurance: Empirical evidence. Journal of Risk and Uncertainty, 20, 291–306.

    Article  Google Scholar 

  • Bush, R. R., & Mosteller, F. (1953). A stochastic model with applications to learning. Annals of Mathematical Statistics, 24(4), 559–585.

    Article  Google Scholar 

  • Camerer, C., Ho, T-H., & Chong, J. K. (2001). Behavioral game theory: Thinking, learning and teaching. Paper presented at the Nobel Prize Symposium, Dec. 2001.

  • Daniels, R. J., Kettl, D. F., & Kunreuther, H. (Eds.). (2006). On risk and disaster: Lessons from Hurricane Katrina. Philadelphia: University of Pennsylvania Press.

    Google Scholar 

  • Dillion, R. L., & Tinsley, C. H. (2008). How near-misses influence decision making under risk: A missed opportunity for learning. Management Science, 54(8), 1425–1440.

    Article  Google Scholar 

  • Erev, I., & Barron, A. (2005). On adaptation, maximization, and reinforcement learning among cognitive strategies. Psychological Review, 112, 912–931.

    Article  Google Scholar 

  • Goldstein, D. G., Johnson, E. J., Herrmann, A., & Heitmann, M. (2008). Nudge your customers toward better choices. Harvard Business Review, 86(12), 99–105.

    Google Scholar 

  • Fudenberg, D., & Levine, D. (2000). The theory of learning in games. Cambridge: MIT Press.

    Google Scholar 

  • Hussam, R. N., Porter, D., & Smith, V. L. (2008). Thar she blows: Can stock bubbles be rekindled with experienced subjects? American Economic Review, 98(3), 924–937.

    Article  Google Scholar 

  • Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80, 237–257.

    Article  Google Scholar 

  • Kalai, E., & Lehrer, E. (1993). Rational learning leads to Nash equilibrium. Econometrica, 61(5), 1019–1045.

    Article  Google Scholar 

  • Kunreuther, H., Meyer, R. J., & Michel-Kerjan, E. (2012). Overcoming decision biases to reduce losses from natural disasters. In E. Shafir (Ed.), Behavioral Foundations of Policy. Princeton University Press.

  • Kunreuther, H., Sanderson, W., & Vetschera, R. (1985). A behavioral model of the adoption of protective activities. Journal of Economic Behavior and Organization, 6, 1–15.

    Article  Google Scholar 

  • Lerner, J. S., Gonzales, R. M., Small, D. A., & Fischhoff, B. (2003). Emotion and perceived risks of terrorism: A national field experiment. Psychological Science, 14(2), 144–150.

    Article  Google Scholar 

  • Loewenstein, G., & Prelec, D. D. (1992). Anomalies in intertemporal choice: Evidence and an interpretation. Quarterly Journal of Economics, 107, 573–597.

    Article  Google Scholar 

  • March, J. G. (1996). Learning to be risk averse. Psychological Review, 103, 309–313.

    Article  Google Scholar 

  • Meyer, R. (2006). Why we under-prepare for hazards. In R. J. Daniels, D. F. Kettl, & H. Kunreuther (Eds.), On risk and disaster: Lessons from Hurricane Katrina. Philadelphia: University of Pennsylvania Press.

    Google Scholar 

  • Meyer, R. J., & Hutchinson, J. W. (2001). Bumbling geniuses: The power of everyday reasoning on multi-state decision making. In S. Hoch & H. Kunreuther (Eds.), Wharton on making decisions. New York: Wiley.

    Google Scholar 

  • Michel-Kerjan, E. (2010). Catastrophe economics: The national flood insurance program. Journal of Economic Perspectives, 24(4), 165–186.

    Article  Google Scholar 

  • Michel-Kerjan, E., Lemoyne de Forges, S., & Kunreuther, H. (2012). Policy tenure under the federal flood insurance program. Risk Analysis, in press.

  • Raghubir, P., & Menon, G. (1998). AIDS and me, never the twain shall meet: The effects of information accessibility on judgments of risk and advertising effectiveness. The Journal of Consumer Research, 25, 52–63.

    Article  Google Scholar 

  • Shafran, A. P. (2011). Self protection against repeated low probability risks. Journal of Risk and Uncertainty, 42, 263–285.

    Article  Google Scholar 

  • Smith, V. K., Carbone, J. C., Pope, J. E., Hallstrom, D. G., & Darden, M. E. (2006). Adjusting to natural disasters. Journal of Risk and Uncertainty, 33(1/2), 37–54.

    Article  Google Scholar 

  • Thaler, R. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior and Organization, 1, 39–60.

    Article  Google Scholar 

  • Viscusi, W. K. (1979). Insurance and individual incentives in adaptive contexts. Econometrica, 47, 1195–1208.

    Article  Google Scholar 

  • Weber, E., Shafir, S., & Blais, A. R. (2004). Predicting risk sensitivity in humans and lower animals: Risk as variance or coefficient of variation. Psychological Review, 111, 430–445.

    Article  Google Scholar 

  • Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal of Personality and Social Psychology, 39, 806–820.

    Article  Google Scholar 

  • Wilkinson, C. (2008). The California earthquake authority. Briefing, Insurance Information Institute,

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Robert J. Meyer.

Additional information

The author thanks Howard Kunruether for comments on an earlier draft of this manuscript, and to Carol Heller for Editorial Assistance.

Appendix: Screen shots from the hurricane simulation, study 1

Appendix: Screen shots from the hurricane simulation, study 1

Figures 8, 9, 10

Fig. 8
figure 8

Basic Interface and Information gathering

Fig. 9
figure 9

Storm motion and mitigation decisions

Fig. 10
figure 10

Damage feedback and debriefing

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Meyer, R.J. Failing to learn from experience about catastrophes: The case of hurricane preparedness. J Risk Uncertain 45, 25–50 (2012).

Download citation

  • Published:

  • Issue Date:

  • DOI:


  • Decision making under uncertainty
  • Learning from experience
  • Natural disasters

JEL Classification

  • D8
  • D9
  • Q5