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Modelling Learning as Modelling 1

By Scott Moss, Bruce Edmonds and Centre For Policy Modelling

Abstract

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

Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.19.7608
Provided by: CiteSeerX
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