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Conference Paper

Catastrophe, Compounding & Consistency in Choice

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Gagne,  C
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Dayan,  P
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Gagne, C., & Dayan, P. (2021). Catastrophe, Compounding & Consistency in Choice. In Workshop on Human and Machine Decisions @ NeurIPS 2021 (WHMD 2021).


Cite as: https://hdl.handle.net/21.11116/0000-0009-8142-A
Abstract
Conditional value-at-risk (CVaR) precisely characterizes the influence that rare,
catastrophic events can exert over decisions. Such characterizations are important
for both normal decision-making and for psychiatric conditions such as anxiety dis-
orders – especially for sequences of decisions that might ultimately lead to disaster.
CVaR, like other well-founded risk measures, compounds in complex ways over
such sequences – and we recently formalized three structurally different forms in
which risk either averages out or multiplies. Unfortunately, existing cognitive tasks
fail to discriminate these approaches well; here, we provide examples that highlight
their unique characteristics, and make formal links to temporal discounting for the
two of the approaches that are time consistent. These examples can ground future
experiments with the broader aim of characterizing risk attitudes, especially for
longer horizon problems and in psychopathological populations.