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Forecast Uncertainty Information Improves Multialternative Risky Decision-Making

Bibliographic Data

ID22332155
AuthorsJee Hoon Han (0000-0002-8105-526X, University of Washington), Susan Joslyn (0000-0002-4452-0695, University of Washington)
Year2025
Volume17
Issue2
Pages299-311
Publication date2025-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueWeather, Climate, and Society (JOURNAL)
Journal identifiersISSN: 1948-8327 • E-ISSN: 1948-8335
PublisherAmerican Meteorological Society (PUBLISHER • US)
DOI10.1175/wcas-d-24-0119.1
OpenAlexW4410314996
LanguageEN
References cited9

Research suggests that nonexperts make better decisions in binary decision tasks and have higher trust in forecasts that include numeric uncertainty estimates (e.g., 30% chance) compared to single-value forecasts (e.g., 6-in. snow accumulation). However, in real-world situations in which one must choose between protective action at a cost or risking a larger probabilistic loss, there is often an intermediate option. Two experiments, reported here, tested whether the previously shown advantages for numeric uncertainty forecasts generalize to decisions with three options. In a snow-day school closure paradigm, a 2-option condition (close, open) was compared to a 3-option condition that also included a cheaper alternative (delay) with less protection than closing, but more protection than staying open when snow occurred. The advantages of probabilistic forecasts persisted regardless of the number of options. In addition, the risk-seeking tendency, typical for decisions between losses, was attenuated in the 3-option condition in experiment 1 and reversed in experiment 2

Econometrics · Economics · Operations research · Computer Science · Engineering · Forecasting Techniques and Applications

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