@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.} }