Communicating Weather Uncertainty: An Individual Differences Approach

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Grounds, Margaret

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Previous research suggests that people make better decisions in weather-related decision tasks when they are given probabilistic uncertainty estimates about the outcomes (Joslyn & Leclerc, 2012; Joslyn & Leclerc, 2013). However, it is unclear whether all users can take advantage of probabilistic forecasts to the same extent. The research reported here assessed various cognitive and demographic factors to explore the extent to which these factors were associated with users’ ability to take advantage of probabilistic forecasts. In three studies, participants made decisions about whether to spend limited resources to salt roads to prevent icy conditions. Several expression formats were tested, including numerical uncertainty and explicit advice. Results suggested that increased numeracy was associated with better weather-related decisions when forecasts included numerical uncertainty estimates in all three studies, although no user groups were substantially impaired when given numerical uncertainty. There were also demographic factors such as age and education that explained decision quality, however, their effects were inconsistent or even contradictory between experiments. However, when all other predictors were removed, individuals with higher numeracy made better decisions regardless of the uncertainty communication method, suggesting that the advantage of numeracy may extend beyond understanding the forecast to larger decision strategy issues. This research adds to a growing body of evidence that numerical uncertainty estimates are an effective way to communicate weather danger. It demonstrates that the advantages of numerical expressions are not seriously limited by individual differences and allow users to better differentiate situations that do and do not require precautionary action and increase trust. Moreover, these results might generalize to other domains in which lay people must make important decisions when faced with uncertainty.

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Thesis (Ph.D.)--University of Washington, 2016-12

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