Published in

American Meteorological Society, Bulletin of the American Meteorological Society, 10(103), p. E2176-E2187, 2022

DOI: 10.1175/bams-d-21-0258.1

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Can Decision Theory Help End-Users Take the Appropriate Action in an Emergency?

Journal article published in 2022 by Natalie J. Harvey, Luke M. Western ORCID, Helen F. Dacre, Antonio Capponi
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Abstract Making decisions about the appropriate action to take when presented with uncertain information is difficult, particularly in an emergency response situation. Decision-makers can be influenced by factors such as how information is framed, their risk sensitivity, and the impact of false alarms. Uncertainty arising from limited knowledge of the current state or future outcome of an event is unavoidable when making decisions. However, there is no universally agreed method on the design and presentation of uncertainty information. The aim of this article is to demonstrate that decision theory can be applied to an ensemble of plausible realizations of a situation to build a transparent framework that can then be used to determine the optimal action by assigning losses to different decision outcomes. The optimal action is then visualized, enabling the uncertainty information to be presented in a condensed manner suitable for decision-makers. The losses are adaptable depending on the hazard and the individual operational model of the decision-maker. To illustrate this approach, decision theory will be applied to an ensemble of volcanic ash simulations used for the purpose of airline flight planning, focusing on the 2019 eruption of Russian volcano Raikoke. Three idealized scenarios are constructed to show the impact of different loss models on the optimal action. In all cases, applying decision theory can significantly alter the regions, and therefore potential flight tracks, identified as potentially hazardous. Thus, we show that different end-users would and should make different decisions when presented with the same probabilistic information based on their individual user requirements.