DRAFT: 7/6/04 Comments welcome Assigning subjective probabilities to event trees: Partition dependence and bias toward the ignorance prior

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.