How do different ways of measuring individual differences in zero-acquaintance personality judgment accuracy correlate with each other?

Objective: This research compares two different approaches that are commonly used to measure accuracy of personality judgment: the trait accuracy approach wherein participants discriminate among targets on a given trait, thus making intertarget comparisons, and the profile accuracy approach wherein participants discriminate between traits for a given target, thus making intratarget comparisons. We examined correlations between these methods as well as correlations among accuracies for judging specific traits. Method: The present ar ticle documents relations among these approaches based on meta-analysis of five studies of zero- acquaintance impressions of the Big Five traits. Results: Trait accuracies correlated only weakly with overall and normative profile accuracy. Substantial convergence between the trait and profile accuracy methods was only found when an aggregate of all five trait accuracies was correlated with distinctive profile accuracy. Importantly, however, correlations between the trait and profile accuracy approaches were reduced to negligibility when statistical overlap was corrected by removing the respective trait from the profile correlations. Moreover, correlations of the separate trait accuracies with each other were ver y weak. Conclusions: Different ways of measuring individual differences in personality judgment accuracy are not conceptually and empirically the same, but rather represent distinct abilities that rely on different judgment processes

Bounds for Multistage Stochastic Programs using Supervised Learning Strategies

Abstract. We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible decision policy, synthesized by a strategy relying on any scenario tree approximation from stochastic programming and on supervised learning techniques from machine learning.