Quantifying Probabilistic Expressions

Mosteller, Frederick; Youtz, Cleo

doi:10.1214/ss/1177012242

Cited by 142 publications

(116 citation statements)

References 29 publications

Supporting

Mentioning

110

Contrasting

Order By: Relevance

“…The CMAH uses the terms 'unlikely,' 'possible,' 'likely,' 'very likely' and 'almost certain' on an ordinal scale to express the likelihood of avalanche(s). Although many studies of quantified verbal probability expressions have identified consistent probability ranges for these terms (e.g., Kent 1964;Reagan et al 1989;Mosteller and Youtz 1990;Mastrandea et al 2010), the scale dependence of probability values and the scale independence of this likelihood terminology rule out associating probability values for this multi-scale approach. When a single slope that is possible to trigger is treated in isolation, it might be considered an unacceptably high risk.…”

Section: Likelihood Of Avalanche(s)mentioning

confidence: 99%

A conceptual model of avalanche hazard

Statham

Haegeli

Greene³

et al. 2017

Nat Hazards

114

112

View full text Add to dashboard Cite

This conceptual model of avalanche hazard identifies the key components of avalanche hazard and structures them into a systematic, consistent workflow for hazard and risk assessments. The method is applicable to all types of avalanche forecasting operations, and the underlying principles can be applied at any scale in space or time. The concept of an avalanche problem is introduced, describing how different types of avalanche problems directly influence the assessment and management of the risk. Four sequential questions are shown to structure the assessment of avalanche hazard, namely: (1) What type of avalanche problem(s) exists? (2) Where are these problems located in the terrain? (3) How likely is it that an avalanche will occur? and (4) How big will the avalanche be? Our objective was to develop an underpinning for qualitative hazard and risk assessments and address this knowledge gap in the avalanche forecasting literature. We used judgmental decomposition to elicit the avalanche forecasting process from forecasters and then described it within a risk-based framework that is consistent with other natural hazards disciplines.

show abstract

Section: Likelihood Of Avalanche(s)mentioning

confidence: 99%

A conceptual model of avalanche hazard

Statham

Haegeli

Greene³

et al. 2017

Nat Hazards

114

112

View full text Add to dashboard Cite

show abstract

“…A weatherman forecasts that "rain is likely" A doctor assures her or his patient that "there is a good chance that the prescribed medication will have no side-effects. " Wallsten, Budescu, Rapoport, Zwick, and Forsyth (1986) and Mosteller and Youtz (1990) studied people's perceptions of the numerical probability implied by such verbal expressions. Each expression was found to be interpreted as expressing a range of plausible numerical probabilities, but to varying degrees.…”

Section: Interpretations Of Verbal Probabilitiesmentioning

confidence: 99%

From subjective probabilities to decision weights: The effect of asymmetric loss functions on the evaluation of uncertain outcomes and events.

Weber

1994

Psychological Bulletin

204

149

View full text Add to dashboard Cite

Much of decision aiding uses a divide-and-conquer strategy to help people with risky decisions. Assessing the utility of outcomes and one's degree of belief in their likelihood are assumed to be separable tasks, the results of which can then be combined to determine the preferred alternative. Evidence from different areas of psychology now provides a growing consensus that this assumption is too simplistic. Observed dependencies in the evaluation of uncertain outcomes and the likelihood of the events giving rise to them are frequent and systematic. Dependencies seem to derive from general strategic processes that take into consideration asymmetric costs of over-vs. underestimates of uncertain quantities. This asymmetric-loss-function interpretation provides a psychological explanation for observed judgments and decisions under uncertainty and links them to other judgment tasks. The decision weights estimated when applying dependent-utility models to choices are not simply reflections of perceived subjective probability but a response to several constraints, all of which modify the weight of risky or uncertain outcomes.Perhaps more than any other social science, psychology maintains an ongoing debate about its status as a coherent field of scholarship (cf. Fowler, 1990;Koch, 1969;Simon, 1992), often expressing genuine concern about the paucity of established and cumulative results and theories. Thus, note the emergence of a consensus on an important behavioral fact from different areas of psychology as well as economics. This article brings together some commonalities in results and in the mechanisms designed to explain them. These should be of theoretical as well as practical interest to anybody interested in human judgments and decisions. With this interpretative review, I attempt to present these often technical results and theories in an integrative and more accessible way. I argue that people's behavior in the judgment and decision situations discussed can be seen as responsive to self-or outwardly imposed constraints in their environment rather than as the result of perceptual or cognitive errors. In particular, I suggest that in situations in which an uncertain quantity needs to be assessed, for example, the probability with which some event will occur or the value of Parts of this article were presented as an invited address at the 23rd Annual Meeting of the Society of Mathematical Psychology. This article was completed while I was a Fellow at the Center for Advanced Study in the Behavioral Sciences, Stanford, California, with financial support from National Science Foundation Grant SES-9022192 and the Graduate School of Business, University of Chicago.I am grateful to Michael Birnbaum, David Budescu, Ward Edwards, Ido Erev, Bill Estes, Claudia Gonzalez, Danny Kahneman, Lola Lopes, Duncan Luce, Barbara Mellers, Robin Hogarth, David Sears, Zur Shapira, Abe Tesser, Tom Wallsten, Martin Weber, and two anonymous reviewers for many helpful comments and suggestions.Correspondence concerning this article should ...

show abstract

“…Past theorists have proposed that experts or forecasters should avoid using linguistic probabilities altogether (e.g., von Winterfeldt & Edwards, 1986) or should adopt a standardized quantification of probability phrases (e.g., Hamm, 1991;Mosteller & Youtz, 1990). However, neither proposal seems workable.…”

mentioning

confidence: 99%