Aided and unaided decisions with imprecise probabilities in the domain of losses

David V. Budescu, Stephen B. Broomell, Robert J. Lempert, Klaus Keller

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

We report results of a series of experiments on decision-making in the presence of irreducibly imprecise probabilities of negative and undesirable outcomes. Subjects faced decisions among actions where the payoffs depend on the probability of drawing balls from an urn whose composition was not fully known. Consistent with the vagueness avoidance hypothesis, Decision Makers (DMs) displayed systematic preferences for safe actions even at a high premium. This tendency grew with increased vagueness, characterized by the width of the interval of plausible probabilities. We compared two decision aids that portray these imprecise probabilities in different ways: one aid calculates the expected value of alternative actions contingent on probability distributions provided by the DMs, and the other displays graphically the distribution of the conditional expected value of the actions over the entire range of plausible probabilities. Access to either decision aid reduced vagueness avoidance and the type of aid used systematically influenced the way DMs approached the problem. We compared the DMs’ choices with predictions of decision models for decision under ignorance and under risk. We found support for the conservative maxi–min criterion, but a subjective expected value model with probabilities inferred from the partial information available also performed well, especially for low levels of vagueness and in the presence of decision aids. These findings suggest some initial implications for the debate over how to best characterize imprecise probabilistic information for policy-makers when decisions involve irreducible uncertainties, such as climate change.

Original languageEnglish (US)
Pages (from-to)31-62
Number of pages32
JournalEURO Journal on Decision Processes
Volume2
Issue number1-2
DOIs
StatePublished - Jun 1 2014

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All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Statistics and Probability
  • Business, Management and Accounting (miscellaneous)
  • Computational Mathematics
  • Applied Mathematics

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