Each of us makes important decisions involving uncertainty in domains in which we are not experts, such as retirement planning, medical treatment, and precautions against severe weather. Often, reliable information about uncertainty is available to us, although how effectively we incorporate it into the decision process remains in question. Previous research suggests that people are error-prone when reasoning with probability. However, recent research in weather-related decision making is more encouraging. Unlike earlier work that compares people’s decisions with a rational standard, this research compares decisions made by people with and without uncertainty information. The results suggest that including specific numeric uncertainty estimates in weather forecasts increases trust and gives people a better idea of what to expect in terms of both the range of possible outcomes and the amount of uncertainty in the particular situation, all of which benefit precautionary decisions. However, the advantage for uncertainty estimates depends critically on how they are expressed. It is crucial that the expression is compatible with both the decision task and cognitive processes of the user.
AnckerJ. S.SenathirajahY.KukafkaR.StarrenJ. B.(2006). Design features of graphs in health risk communication. Journal of the American Medical Informatics Association, 13, 608–618.
2.
AndoM.WakaiK.SekiN.TamakoshiA.SuzukiK.ItoY.. . . JACC Study Group. (2003). Attributable and absolute risk of lung cancer death by smoking status: Findings from the Japan collaborative cohort study. International Journal of Cancer, 105, 249–254.
3.
BaddeleyA. (1992). Working memory. Science, 255, 556–559.
CosmidesL.ToobyJ. (1996). Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty. Cognition, 58, 1–73.
6.
CutterS. L.SmithM. M. (2009). Fleeing from the hurricane’s wrath: Evacuation and the two Americas. Environment, 51, 26–36.
7.
DowK.CutterS. L. (2000). Public orders and personal opinions: Household strategies for hurricane risk assessment. Environmental Hazards, 2, 143–155.
8.
FischerK.JungermannH. (1996). Rarely occurring headaches and rarely occurring blindness: Is rarely = rarely? The meaning of verbal frequentistic labels in specific medical contexts. Journal of Behavioral Decision Making, 9, 153–172.
9.
FischhoffB.BostromA.QuadrelM. J. (1993). Risk perception and communication. Annual Review of Public Health, 14, 183–203.
10.
GigerenzerG.EdwardsA. (2003). Simple tools for understanding risks: From innumeracy to insight. British Medical Journal, 327, 741–744.
11.
GigerenzerG.HoffrageU. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102, 684–704.
12.
GneitingT.RafteryA. E. (2005). Weather forecasting with ensemble methods. Science, 310, 248–249.
JoslynS. (2009). [Verbal protocol: Analysis of open-ended questions about understanding of the predictive interval]. Unpublished raw data.
16.
JoslynS.LeClercJ. (2012). Uncertainty forecasts improve weather-related decisions and attenuate the effects of forecast error. Journal of Experimental Psychology: Applied, 18, 126–140.
17.
JoslynS.Nadav-GreenbergL.NicholsR. M. (2009). Probability of precipitation: Assessment and enhancement of end-user understanding. Bulletin of the American Meteorological Society, 90, 185–193.
18.
JoslynS.Nadav-GreenbergL.TaingM. U.NicholsR. M. (2009). The effects of wording on the understanding and use of uncertainty information in a threshold forecasting decision. Applied Cognitive Psychology, 23, 55–72.
19.
JoslynS.PakK.JonesD.PylesJ.HuntE. (2007). The effect of probabilistic information on threshold forecasts. Weather and Forecasting, 22, 804–812.
20.
JoslynS.SavelliS. (2010). Communicating forecast uncertainty: Public perception of weather forecast uncertainty. Meteorological Applications, 17, 180–195.
21.
KahnemanD. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58, 697–720. doi:10.1037/0003-066X.58.9.697
22.
KahnemanD.TverskyA. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–292.
23.
LeClercJ.JoslynS. (2012). Odds ratio forecasts increase precautionary action for extreme weather events. Weather, Climate, and Society, 4, 263–270.
24.
MacEachrenA. M. (1992). Visualizing uncertain information. Cartographic Perspective, 13, 10–19.
25.
MorssR. E.DemuthJ. L.LazoJ. K. (2008). Communicating uncertainty in weather forecasts: A survey of the U.S. public. Weather and Forecasting, 23, 974–991.
MurphyA. H. (1991). Probabilities, odds, and forecasts of rare events. Weather and Forecasting, 6, 302–307.
28.
MurphyA. H.LichtensteinS.FischhoffB.WinklerR. L. (1980). Misinterpretations of precipitation probability forecasts. Bulletin of the American Meteorological Society, 61, 695–701.
29.
Nadav-GreenbergL.JoslynS. (2009). Uncertainty forecasts improve decision-making among non-experts. Journal of Cognitive Engineering and Decision Making, 2, 24– 47.
30.
PattA. G. (2001). Understanding uncertainty: Forecasting seasonal climate for farmers in Zimbabwe. Risk, Decision and Policy, 6, 105–119.
31.
RiadJ. K.NorrisF. H.RubackR. B. (1999). Predicting evacuation in two major disasters: Risk perception, social influence, and access to resources. Journal of Applied Social Psychology, 29, 918–934.
32.
RoulstonM. S.BoltonG. E.KleitA. N.Sears-CollinsA. L. (2006). A laboratory study of the benefits of including uncertainty information in weather forecasts. Weather and Forecasting, 21, 116–122.
33.
SavelliS.JoslynS. (2012). Boater safety: Communicating weather forecast information to high stakes end users. Weather, Climate, and Society, 4, 7–19.
34.
SavelliS.JoslynS. (2013). The advantages of 80% predictive interval forecasts for non-experts and the impact of visualizations. Applied Cognitive Psychology. doi:10.1002/acp.2932
35.
StanovichK. E.WestR. F. (2000). Individual differences in reasoning: Implications for the rationality debate?Behavioral & Brain Sciences, 23, 645–665.
36.
ThompsonJ. C. (1952). On the operational deficiencies in categorical weather forecasts. Bulletin of the American Meteorological Society, 33, 223–226.
37.
TufteE. R. (2006). Beautiful evidence. Cheshire, CT: Graphics Press.
38.
TverskyA.KahnemanD. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.
39.
WallstenT. S.BudescuD. V.RapoportA.ZwickR.ForsythB. (1986). Measuring the vague meanings of probability terms. Journal of Experimental Psychology: General, 115, 348–365.
40.
WindschitlP. D.WeberE. (1999). The interpretation of “likely” depends on the context, but “70%” is 70%—right? The influence of associative processes on perceived certainty. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 1514–1533.
