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Artificial neural networks are a class of input-output models developed by cognitive scientists interested in understanding how computation is performed by the brain. These networks are capable of learning through a process of trial and error. In econometric terms, artificial neural network models constitute a particular class of nonlinear parametric models." Learning" corresponds to statistical estimation of model parameters. Although inspired by certain aspects of the way information is processed in the brain, these network models and their associated learning paradigms are still far from anything close to a realistic description of how brains actually work. They nevertheless provide a rich, powerful and interesting modeling framework with proven and potential application across the sciences. To mention just a handful of such applications, artificial neural networks have been successfully used to translate printed …