TY - GEN ID - cogprints510 UR - http://cogprints.org/510/ A1 - Moss, Scott A1 - Edmonds, Bruce Y1 - 1994/11// N2 - 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. KW - learning KW - bounded rationality KW - modelling KW - logic KW - noise KW - complexity KW - specificity economics KW - simulation TI - Modelling Learning as Modelling SP - 5 AV - public EP - 37 ER -