title: Modelling Learning as Modelling creator: Moss, Scott creator: Edmonds, Bruce subject: Artificial Intelligence subject: Machine Learning subject: Philosophy of Science subject: Social Psychology description: Economists tend to represent learning as a procedure for estimating the parameters of the "correct" econometric model. We extend this approach by assuming that agents specify as well as estimate models. Learning thus takes the form of a dynamic process of developing models using an internal language of representation where expectations are formed by forecasting with the best current model. This introduces a distinction between the form and content of the internal models which is particularly relevant for boundedly rational agents. We propose a framework for such model development which use a combination of measures: the error with respect to past data, the complexity of the model, the cost of finding the model and a measure of the model's specificity The agent has to make various trade-offs between them. A utility learning agent is given as an example. date: 1994-11 type: Journal (Paginated) type: PeerReviewed format: application/postscript identifier: http://cogprints.org/510/1/learningA4.ps format: application/pdf identifier: http://cogprints.org/510/5/learning.pdf identifier: Moss, Scott and Edmonds, Bruce (1994) Modelling Learning as Modelling. [Journal (Paginated)] relation: http://cogprints.org/510/