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.
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Conjectures and Refutations: The Growth of Scientific Knowledge.Mary Hesse - 1965 - Philosophical Quarterly 15 (61):372-374.
On the Thresholds of Knowledge.Douglas B. Lenat & Edward A. Feigenbaum - 1991 - Artificial Intelligence 47 (1-3):185-250.

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