Decision Analysis Working Paper Abstract
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WP040002
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Title: Assigning subjective probabilities to event trees: Partition
dependence and bias toward the ignorance prior
Author: Craig
R. Fox University of California Los Angeles and Robert
T. Clemen, Duke University
Date: July 2004
Status: Working Paper
Decision and risk analysts have considerable discretion in designing procedures
for eliciting subjective probabilities. One popular approach is to specify
a particular set of exclusive and exhaustive events for which the assessor
provides subjective probabilities. We show that assessed probabilities
are biased toward a uniform distribution over all events into which the
relevant sample space happens to be partitioned. We surmise that an assessor
begins with an “ignorance prior” probability distribution that assigns
equal probabilities to the specified events, then insufficiently adjusts
those probabilities to reflect his or her beliefs concerning how the likelihood
of the events differ. In five studies, we demonstrate partition dependence
for both discrete events and continuous variables (Studies 1 and 2), show
that the bias decreases (but may or may not disappear) with increased domain
knowledge (Studies 3 & 4), and that top experts in decision analysis
are susceptible to this bias (Study 5). We relate our work to previous
research on the “pruning bias” in fault-tree assessment (e.g., Fischhoff,
Slovic, & Lichtenstein, 1978) and show that previous explanations (enhanced
availability of specified events, ambiguity in interpreting event categories,
demand effects) cannot fully account for the effect. We conclude by discussing
implications for decision-analysis practice.
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