@misc{cogprints510,
volume = {29},
month = {November},
title = {Modelling Learning as Modelling},
author = {Scott Moss and Bruce Edmonds},
year = {1994},
pages = {5--37},
journal = {Cybernetics and Systems},
keywords = {learning, bounded rationality, modelling, logic, noise, complexity, specificity economics, simulation},
url = {http://cogprints.org/510/},
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.}
}