Research on advice utilization often operationalizes the construct via Judge Advisor Systems (JAS), where a judge’s belief is elicited, they are provided advice, and given an opportunity to revise their belief. Belief change, or weight of advice (WOA), is measured as the shift in the judge’s belief proportional to the difference between their original belief and the advice. Several JAS studies have found WOA typically takes on a trimodal distribution, with inflation at the boundary values of 0 (indicating a judge declined advice) and 1 (adoption of advice). A dual hurdle beta model is proposed to account for these inflations. In addition to being an innovative computational model to address this methodological challenge, it also serves as a descriptive theoretical model which posits that the decision process happens in two stages: an initial discrete “choosing” stage, where the judge opts to either decline, adopt, or compromise with advice; and a subsequent continuous “averaging” stage, which occurs only if the judge opts to compromise. The approach was assessed via reanalysis of three recent JAS studies reflective of popular topics in the literature, such as algorithmic advice utilization, egocentric discounting effects, and judgmental forecasting. In each case new results were uncovered about how different correlates of advice utilization influence the decision process at either or both of the discrete and continuous stages, often in quite different ways, providing support for the descriptive theoretical model. A Bayesian graphical analysis framework is provided that can be applied to future research on advice utilization.