%A Scott Moss
%A Bruce Edmonds
%J Cybernetics and Systems
%T Modelling Learning as Modelling
%X 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.
%K learning, bounded rationality, modelling, logic, noise, complexity, specificity economics, simulation
%P 5-37
%V 29
%D 1994
%L cogprints510

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