Proceedings of the Annual Meeting of the Cognitive Science Society

Configurative weighting and adding can be a surprisingly effective approximation of multiplicative functions. In thecontext of joint probability judgment, Nilsson et al. (2009) has shown that, when marginal probabilities are only approxi-mately known, the configurative weighted average (CWA) of two probabilities not only predicts a high level of conjunctionfallacies, as observed in data, but also correlates higher with the true joint probability than if the two probabilities are mul-tiplied. Here we show that [1] the surface representing the optimal Bayesian estimate of a joint probability can be closelyapproximated by two planes, [2] configurative weighting and adding, such as the CWA model, constitutes such a two-planeapproximation, and [3] a bias-variance tradeoff is not sufficient to explain the accuracy of the CWA. More generally, thissuggests that the efficiency of heuristics might be due to suitable weighting operations rather than less-is-more effects.