Natural language of uncertainty: numeric hedge words

Ferson, Scott; O'Rawe, Jason; Antonenko, Andrei; Siegrist, Jack; Mickley, James; Luhmann, Christian C.; Sentz, Kari; Finkel, Adam M.

doi:10.1016/j.ijar.2014.11.003

Cited by 34 publications

(44 citation statements)

References 30 publications

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“…Two models from the literature, estimating the range of denoted values, are then presented: a scale-based model [8,13] and a regression model [4]. Finally, the fuzzy set approach to vagueness is discussed.…”

Section: Related Workmentioning

confidence: 99%

“…Ferson et al [4] propose an empirical approach using real data to test the relevance of predictors of the interval width. Semantically contextualised ANEs (e.g., "Roughly 25% of Canadians are Protestant.")…”

Section: Regression Model (Regm)mentioning

confidence: 99%

“…This questionnaire meets the criteria proposed by [1] to elicit membership functions in a random set perspective. This method is also similar to the one used by [4]. 146 participants have been recruited through an announcement diffused on mailing-lists: 102 women and 44 men, aged 20 to 70 (M = 38.6; σ = 14.2).…”

Section: Data Collectionmentioning

confidence: 99%

“…As the models from the literature [4,13] are not fuzzy, they can be used to estimate either the support, the kernel or the 0.5-cut. We propose to use them to predict the 0.5-cut as it is a central indicator of the boundary distributions.…”

Section: Evaluation Of the 05-cut Estimationmentioning

confidence: 99%

“…. }), and the regression model REGM [4]. The latter only provides the size of the intervals and no information about their location or symmetry around the ANE.…”

Section: Evaluation Of the 05-cut Estimationmentioning

confidence: 99%

See 4 more Smart Citations

How Much Is “About”? Fuzzy Interpretation of Approximate Numerical Expressions

Lefort

Lesot

Zibetti

et al. 2016

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Abstract. Approximate Numerical Expressions (ANEs) are linguistic expressions involving numbers and referring to imprecise ranges of values, such as "about 100 ". This paper proposes to interpret ANEs as fuzzy numbers. A model, taking into account the cognitive salience of numbers and based on critical points from Pareto frontiers, is proposed to characterise the support, the kernel and the 0.5-cut of the corresponding membership functions. An experimental study, based on real data, is performed to assess the quality of these estimated parameters.

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Section: Related Workmentioning

confidence: 99%

Section: Regression Model (Regm)mentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Evaluation Of the 05-cut Estimationmentioning

confidence: 99%

“…. }), and the regression model REGM [4]. The latter only provides the size of the intervals and no information about their location or symmetry around the ANE.…”

Section: Evaluation Of the 05-cut Estimationmentioning

confidence: 99%

See 3 more Smart Citations

How Much Is “About”? Fuzzy Interpretation of Approximate Numerical Expressions

Lefort

Lesot

Zibetti

et al. 2016

Information Processing and Management of Uncertainty in Knowledge-Based Systems

View full text Add to dashboard Cite

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More than 50% or Less than 70% Chance: Pragmatic Implications of Single‐Bound Probability Estimates

Hohle

Teigen

2017

Behavioral Decision Making

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Probability estimates can be given as ranges or uncertainty intervals, where often only one of the interval bounds (lower or upper) is specified. For instance, a climate forecast can describe La Niña as having "more than 70% chance" or "less than 90% chance" of occurring. In three experiments, we studied how research participants perceived climate-related forecasts expressed with lower-bound ("over X% chance") or upper-bound ("under Y% chance") probability statements. Results indicate that such single-bound statements give pragmatic information in addition to the numeric probabilities they convey. First, the studies show that these statements are directional, leading the listeners' attention in opposite directions. "Over" statements guide attention towards the possible occurrence of the event and are explained by reasons for why it might happen, while "under" statements direct attention to its possible non-occurrence and are more often explained by reasons for why the target event might not appear, corresponding to positive (it is possible) versus negative (it is uncertain) verbal probabilities. Second, boundaries were found to reveal the forecaster's beliefs and could be perceived as indicative of an increasing or a decreasing trend. Single-bound probability estimates are therefore not neutral communications of probability level but might "leak" information about the speaker's expectations and about past and future developments of the forecast.

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Framing experts' (dis)agreements about uncertain environmental events

Løhre

Sobków

Hohle

et al. 2019

Behavioral Decision Making

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Agreements and disagreements between expert statements influence lay people's beliefs. But few studies have examined what is perceived as a disagreement. We report six experiments where people rated agreement between pairs of probabilistic statements about environmental events, attributed to two different experts or to the same expert at two different points in time. The statements differed in frame, by focusing on complementary outcomes (45% probability that smog will have negative health effects vs. 55% probability that it will not have such effects), in probability level (45% vs. 55% probability of negative effects), or in both respects. Opposite frames strengthened disagreement when combined with different probability levels. Approximate probabilities can be “framed” in yet another way by indicating reference values they are “over” or “under”. Statements that use different directional verbal terms (over vs. under 50%) indicated greater disagreement than statements with the same directional term but different probability levels (over 50% vs. over 70%). Framing and directional terms similarly affected consistency judgments when both statements were issued by the same expert at different occasions. The effect of framing on perceived agreement was significant for medium (10 and 20 percentage points) differences between probabilities, whereas the effect of directional term was stable for numerical differences up to 40 percentage points. To emphasize agreement between different estimates, they should be framed in the same way. To accentuate disagreements or changes of opinion, opposite framings should be used.

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Natural language of uncertainty: numeric hedge words

Cited by 34 publications

References 30 publications

How Much Is “About”? Fuzzy Interpretation of Approximate Numerical Expressions

How Much Is “About”? Fuzzy Interpretation of Approximate Numerical Expressions

More than 50% or Less than 70% Chance: Pragmatic Implications of Single‐Bound Probability Estimates

Framing experts' (dis)agreements about uncertain environmental events

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