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AbstractOften research in judgment and decision making requires comparison of multiple competing models. Researchers invoke global measures such as the rate of correct predictions or the sum of squared (or absolute) deviations of the various models as part of this evaluation process. Reliance on such measures hides the (often very high) level of agreement between the predictions of the various models and does not highlight properly the relative performance of the competing models in thosedoi:10.1017/s1930297500004241 fatcat:ilcsvzltx5bdtm7qm4u4xasb3m