Literature DB >> 33735197

On measuring agreement with numerically bounded linguistic probability schemes: A re-analysis of data from Wintle, Fraser, Wills, Nicholson, and Fidler (2019).

David R Mandel1, Daniel Irwin2.   

Abstract

Across a wide range of domains, experts make probabilistic judgments under conditions of uncertainty to support decision-making. These judgments are often conveyed using linguistic expressions (e.g., x is likely). Seeking to foster shared understanding of these expressions between senders and receivers, the US intelligence community implemented a communication standard that prescribes a set of probability terms and assigns each term an equivalent numerical probability range. In an earlier PLOS ONE article, [1] tested whether access to the standard improves shared understanding and also explored the efficacy of various enhanced presentation formats. Notably, they found that embedding numeric equivalents in text (e.g., x is likely [55-80%]) substantially outperformed the status-quo approach in terms of the percentage overlap between participants' interpretations of linguistic probabilities (defined in terms of the numeric range equivalents they provided for each term) and the numeric ranges in the standard. These results have important prescriptive implications, yet Wintle et al.'s percentage overlap measure of agreement may be viewed as unfairly punitive because it penalizes individuals for being more precise than the stipulated guidelines even when the individuals' interpretations fall perfectly within the stipulated ranges. Arguably, subjects' within-range precision is a positive attribute and should not be penalized in scoring interpretive agreement. Accordingly, in the present article, we reanalyzed Wintle et al.'s data using an alternative measure of percentage overlap that does not penalize in-range precision. Using the alternative measure, we find that percentage overlap is substantially elevated across conditions. More importantly, however, the effects of presentation format and probability level are highly consistent with the original study. By removing the ambiguity caused by Wintle et al.'s unduly punitive measure of agreement, these findings buttress Wintle et al.'s original claim that the methods currently used by intelligence organizations are ineffective at coordinating the meaning of uncertainty expressions between intelligence producers and intelligence consumers. Future studies examining agreement between senders and receivers are also encouraged to reflect carefully on the most appropriate measures of agreement to employ in their experiments and to explicate the bases for their methodological choices.

Entities:  

Year:  2021        PMID: 33735197      PMCID: PMC7971511          DOI: 10.1371/journal.pone.0248424

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  9 in total

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2.  Improving communication of uncertainty in the reports of the intergovernmental panel on climate change.

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3.  Accuracy of forecasts in strategic intelligence.

Authors:  David R Mandel; Alan Barnes
Journal:  Proc Natl Acad Sci U S A       Date:  2014-07-14       Impact factor: 11.205

4.  Words or numbers? Communicating probability in intelligence analysis.

Authors:  Mandeep K Dhami; David R Mandel
Journal:  Am Psychol       Date:  2020-07-23

Review 5.  Describing treatment effects to patients.

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Journal:  J Gen Intern Med       Date:  2003-11       Impact factor: 5.128

6.  Expert status and performance.

Authors:  Mark A Burgman; Marissa McBride; Raquel Ashton; Andrew Speirs-Bridge; Louisa Flander; Bonnie Wintle; Fiona Fidler; Libby Rumpff; Charles Twardy
Journal:  PLoS One       Date:  2011-07-29       Impact factor: 3.240

7.  People's Understanding of Verbal Risk Descriptors in Patient Information Leaflets: A Cross-Sectional National Survey of 18- to 65-Year-Olds in England.

Authors:  Rebecca K Webster; John Weinman; G James Rubin
Journal:  Drug Saf       Date:  2017-08       Impact factor: 5.606

8.  Verbal probabilities: Very likely to be somewhat more confusing than numbers.

Authors:  Bonnie C Wintle; Hannah Fraser; Ben C Wills; Ann E Nicholson; Fiona Fidler
Journal:  PLoS One       Date:  2019-04-17       Impact factor: 3.240

9.  The Role of Type and Source of Uncertainty on the Processing of Climate Models Projections.

Authors:  Daniel M Benjamin; David V Budescu
Journal:  Front Psychol       Date:  2018-03-27
  9 in total

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