Communicating Uncertainty Information in a Dynamic Decision Environment

Dados Bibliográficos

ID22331604
AutoresGala Gulacsik (0000-0001-7448-1597, University of Washington), Susan Joslyn (0000-0002-4452-0695, University of Washington), Susan L Joslyn (University of Washington), John Robinson (0000-0003-4559-5565, b Human Centered Design and Engineering, University of Washington, Seattle, Washington), John J Robinson (University of Washington), Chao Qin (0000-0003-0393-5330, University of Washington)
Ano2022
Volume14
Fascículo4
Páginas1201-1216
Data de publicação2022-10-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoWeather, Climate, and Society (JOURNAL)
Identificadores do periódicoISSN: 1948-8327 • E-ISSN: 1948-8335
EditoraAmerican Meteorological Society (PUBLISHER • US)
DOI10.1175/wcas-d-21-0186.1
OpenAlexW4308385946
IdiomaEN
Citações recebidas1
Referências citadas14

The likelihood of threatening events is often simplified for members of the public and presented as risk categories such as the “watches” and “warnings” currently issued by National Weather Service in the United States. However, research (e.g., Joslyn and LeClerc) suggests that explicit numeric uncertainty information—for example, 30%—improves people’s understanding as well as their decisions. Whether this benefit extends to dynamic situations in which users must process multiple forecast updates is as yet unknown. It may be that other likelihood expressions, such as color coding, are required under those circumstances. The experimental study reported here compared the effect of the categorical expressions “watches” and “warnings” with both color-coded and numeric percent chance expressions of the likelihood of a tornado in a situation with multiple updates. Participants decided whether and when to take shelter to protect themselves from a tornado on each of 40 trials, each with seven updated tornado forecasts. Understanding, decision quality, and trust were highest in conditions that provided percent chance information. Color-coded likelihood information inspired the least trust and led to the greatest overestimation of likelihood and confusion with severity information of all expressions

Actuarial science · Business · Categorical variable · Confusion · Econometrics · Geography · Machine learning · National weather service · Statistics · Tornado · Advanced Text Analysis Techniques · Computer Science · Educational Research and Analysis · Mathematics · Psychology · Safety Warnings and Signage