Literature DB >> 35185399

Precise or Imprecise Probabilities? Evidence from Survey Response Related to Late-Onset Dementia.

Pamela Giustinelli1, Charles F Manski2, Francesca Molinari3.   

Abstract

We elicit numerical expectations for late-onset dementia and long-term-care (LTC) outcomes in the US Health and Retirement Study. We provide the first empirical evidence on dementia-risk perceptions among dementia-free older Americans and establish important patterns regarding imprecision of subjective probabilities. Our elicitation distinguishes between precise and imprecise probabilities, while accounting for rounding of reports. Imprecise-probability respondents quantify imprecision using probability intervals. Nearly half of respondents hold imprecise dementia and LTC probabilities, while almost a third of precise-probability respondents round their reports. These proportions decrease substantially when LTC expectations are conditioned on hypothetical knowledge of the dementia state. Among rounding and imprecise-probability respondents, our elicitation yields two measures: an initial rounded or approximated response and a post-probe response, which we interpret as the respondent's true point or interval probability. We study the mapping between the two measures and find that respondents initially tend to over-report small probabilities and under-report large probabilities. Using a specific framework for study of LTC insurance choice with uncertain dementia state, we illustrate the dangers of ignoring imprecise or rounded probabilities for modeling and prediction of insurance demand.
© The Author(s) 2021. Published by Oxford University Press on behalf of European Economic Association.

Entities:  

Year:  2021        PMID: 35185399      PMCID: PMC8848333          DOI: 10.1093/jeea/jvab023

Source DB:  PubMed          Journal:  J Eur Econ Assoc        ISSN: 1542-4766


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Journal:  Lancet Neurol       Date:  2016-04       Impact factor: 44.182

4.  Rounding Probabilistic Expectations in Surveys.

Authors:  Charles F Manski; Francesca Molinari
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Authors:  Péter Hudomiet; Michael D Hurd; Susann Rohwedder
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Authors:  S Woloshin; L M Schwartz; S Byram; B Fischhoff; H G Welch
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Authors:  B L Plassman; K M Langa; G G Fisher; S G Heeringa; D R Weir; M B Ofstedal; J R Burke; M D Hurd; G G Potter; W L Rodgers; D C Steffens; R J Willis; R B Wallace
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  2 in total

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2.  Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).

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