Subjective recalibration of advisors' probability estimates.

Are decision makers sensitive to the statistical properties (i.e., calibration) of probability estimates that they receive from advisors? After specifying the ideal use of such estimates, we derive the roughly ideal forecast consumer (RIFC) and generalize it to account for how humans might use the estimates. We report an experiment in which participants first experienced various advisors by seeing their probability estimates and the associated outcomes and then provided confidence judgments in the presence of the advisors' estimates. The generalized model described the data well and showed that the participants were appropriately sensitive to the statistical properties of the advisors. Models of the individuals were better calibrated than the participants themselves, but still inferior to the RIFC. A detailed description of our model-testing procedure can be found in an appendix to the article.