On measuring agreement with numerically bounded linguistic probability schemes: A re-analysis of data from Wintle, Fraser, Wills, Nicholson, and Fidler (2019)
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. Wintle et al. (2019) 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 since it penalizes individualsfor being more precise than the stipulated guidelines even when the individuals’ interpretations fall perfectly within the stipulated ranges. Arguably, within-range precision is a positive attribute. Accordingly, we reanalyzed Wintle et al.’s data using an alternative measure of percentage overlap that does not penalize in-range precision. Predictably, we find that percentage overlap is elevated across conditions. More importantly, the effects of presentation format and probability level are highly consistent with the original study. By removing further ambiguity, these findings buttress the claim that the methods currently used by intelligence organizations are ineffective at coordinating the meaning of uncertainty expressions between intelligence producers and intelligence consumers.