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Statistical Analysis and Data Mining The ASA Data Science Journal
Article . 2016 . Peer-reviewed
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Use and communication of probabilistic forecasts

Authors: Adrian E. Raftery;

Use and communication of probabilistic forecasts

Abstract

Probabilistic forecasts are becoming more and more available. How should they be used and communicated? What are the obstacles to their use in practice? We review experience with five problems where probabilistic forecasting played an important role. This leads us to identify five types of potential users: low stakes users, who do not need probabilistic forecasts; general assessors, who need an overall idea of the uncertainty in the forecast; change assessors, who need to know if a change is out of line with expectations; risk avoiders, who wish to limit the risk of an adverse outcome; and decision theorists, who quantify their loss function and perform the decision‐theoretic calculations. This suggests that it is important to interact with users and consider their goals. Cognitive research tells us that calibration is important for trust in probability forecasts and that it is important to match the verbal expression with the task. The cognitive load should be minimized, reducing the probabilistic forecast to a single percentile if appropriate. Probabilities of adverse events and percentiles of the predictive distribution of quantities of interest often seem to be the best way to summarize probabilistic forecasts. Formal decision theory has an important role but in a limited range of applications. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016

Keywords

FOS: Computer and information sciences, cognitive research, Statistics, Applications (stat.AP), decision theory, calibration, uncertainty, Computer science, Statistics - Applications, risk

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
46
Top 10%
Top 10%
Top 10%
Green
bronze