Decision Makers seek advice from multiple experts in order to increase diagnostic ability and improve the quality of their decisions. The effects of such aggregations have been well studied (Ariely et al. 2001; Clemen & Winkler, 1985; Hogarth, 1978; Johnson, Budescu & Wallsten, 2001; Wallsten & Diederich, 2001). Several measures of quality of performance (precision, discrimination, and validity) increase with each additional judge, but at a diminishing rate that is a function of inter-judge correlation. These findings stress the importance of inter-judge dependence.Using a set of reasonable assumptions we derive a model to predict the magnitude of inter-judge correlation as a function of 5 underlying factors. The first two factors are cue similarity, ρc, and the number of cues, N, which describe assumptions about nature. The final three factors ρw, σ2w, and δ describe assumptions about the judges (similarity in training, cue use, and accuracy respectively). We found that the factors ρc, N, and ρw, increase inter-judge correlation, while σ2w and δ decrease it. This model allows us to study the relative importance and interrelation of these five factors with respect to inter-judge correlation. Interrelation between factors impedes complete dominance but generally we found ρc has more influence than ρw and σ2w. Using our model in conjunction with existing models we can also address a variety of practical questions. For example, our results indicate that additional judges increase efficacy at a greater rate than additional cues (cues can sometimes slightly decrease efficacy). |