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Modelling Learning as Modelling

Moss, Scott and Edmonds, Bruce (1994) Modelling Learning as Modelling. [Journal (Paginated)]

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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.

Item Type:Journal (Paginated)
Keywords:learning, bounded rationality, modelling, logic, noise, complexity, specificity economics, simulation
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Learning
Philosophy > Philosophy of Science
Psychology > Social Psychology
ID Code:510
Deposited By:Edmonds, Dr Bruce
Deposited On:07 Aug 1998
Last Modified:11 Mar 2011 08:54

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