Abstract
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.
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Notes
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.
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.
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.
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.
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.
Mixed-model maximum-likelihood estimates derived under the assumption of more general (e.g., AR-1) error structures yield similar results.
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The author thanks Howard Kunruether for comments on an earlier draft of this manuscript, and to Carol Heller for Editorial Assistance.
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Meyer, R.J. Failing to learn from experience about catastrophes: The case of hurricane preparedness. J Risk Uncertain 45, 25–50 (2012). https://doi.org/10.1007/s11166-012-9146-4
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DOI: https://doi.org/10.1007/s11166-012-9146-4