Multiple-Bounded Uncertainty Choice Data as Probabilistic Intentions

Mary F. Evans, Nicholas E. Flores and Kevin J. Boyle


The multiple-bounded uncertainty choice (MBUC) value elicitation method allows respondents to indicate qualitative levels of uncertainty, as opposed to a simple yes or no, across a range of prices. We argue that MBUC responses convey subjective probabilities. We examine the decision process of the researcher faced with estimating population parameters from MBUC sample responses. We develop her optimal decision rule based on a specified loss function. The resulting estimator accommodates uncertainty on the part of the respondent and the researcher. We illustrate the proposed estimation method using MBUC responses from the first field application of this elicitation format. The resulting framework produces stable estimates and nests alternative methods of modeling MBUC responses. (JEL Q26)