Patterns of Preference for Numerical and Verbal Probabilities

✍ Scribed by MICHAEL J. OLSON; DAVID V. BUDESCU

• Publisher: John Wiley and Sons
• Year: 1997
• Tongue: English
• Weight: 240 KB
• Volume: 10
• Category: Article
• ISSN: 0894-3257
• DOI: 10.1002/(sici)1099-0771(199706)10:2<117::aid-bdm251>3.0.co;2-7

✦ Synopsis

We report results of an experiment designed to test a principle formulated by Budescu and Wallsten (1995), that, when communicating uncertainty information, mode choices are sensitive to sources and degrees of vagueness. In addition, we examined subjects' ecacy in using such uncertainty information as a function of communication mode, source, and vagueness. In phase one of the experiment, subjects in a dyad used precise (numerical) or imprecise (verbal) expressions to communicate to a remote partner precise or vague uncertainty about the likelihoods of events. Spinner outcomes were used to generate precise uncertainty while answers to almanac questions were used to elicit vague uncertainty. In phase two, subjects saw the events paired with their partners' estimates of similar events, and were asked to gamble on one event from each pair. Communication mode preferences were measured as the relative frequency that subjects chose the numerical mode to either express or receive uncertainty information regarding the events. Ecacy was measured as the relative frequency that subjects choose from the pair the event associated with the objectively more probable uncertainty expression. Underlying uncertainty interacted with direction of communication to aect preferences for modes of expression of the probabilities. Subjects preferred precise (numerical) information, especially for precise events (spinners). For vague events (questions), their preference for precise (numerical) information was stronger when receiving than when communicating information. Similar preferences were re¯ected in the eciency of subsequent gamble decisions based on the probability estimates. Speci®cally, decisions were more ecacious (i.e. consistent with Expected Utility) when degrees of precision in events and estimates matched.