The effects of asymmetry among advisors on the aggregation of their opinions

We investigate the case of a Decision Maker (DM) who obtains probabilistic forecasts regarding the occurrence of a target event from J distinct, asymmetric advisors. In this context, asymmetry is induced by manipulating: (1) amount of information (number of diagnostic cues) available to each advisor and (2) quality (accuracy) of advisors’ previous forecasts. Empirical results from two experiments indicate that the DM’s final estimate can be described as a weighted average of advisor forecasts, where the weights are sensitive to both sources of asymmetry. This work extends the model derived by Budescu and Rantilla (2000) for the DMs confidence in the aggregate to accommodate advisor asymmetry. As in the symmetric case, the DM’s confidence in the weighted average of the forecasts is a function of the number of judges, the total number of cues, the (inferred) inter-judge correlation, and the level of inter-judge overlap in information. The extended model predicts that confidence increases as a function of asymmetry among judges. Empirical results support the main (ordinal) predictions of the model, including the predicted effect of inter-judge asymmetry.