Partition Dependence and Carryover Biases in Subjective Probability Assessment Surveys for Continuous Variables: Model-Based Estimation and Correction

As probability elicitation becomes widely used, methods other than one-on-one interviews are being used to elicit expert probabilities. This paper considers biases that may arise when probabilities are elicited in an online or workbook setting. We develop a prescriptive model in which the elicited probability is a convex combination of the experts underlying probability with elements of partition dependence and two anchors arising from responses to previous questions ("carryover" bias). Our model, applied to two data sets, allows us to estimate the amount of the various biases in a set of elicited probabilities from experts. We find that both the format of the questionswhether they appear on the same or separate pages/screens and the ordering of the questions can affect the amount of bias. Our research addresses biases in the presence of multiple anchors and provides guidance on manipulating the availability of anchors. The results demonstrate the persistence of anchoring even with careful questionnaire design; thus, the proposed model-based methods are useful to suggest corrections for the resulting biases.