TY - GEN
ID - cogprints510
UR - http://cogprints.org/510/
A1 - Moss, Scott
A1 - Edmonds, Bruce
Y1 - 1994/11//
N2 - 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.
KW - learning
KW - bounded rationality
KW - modelling
KW - logic
KW - noise
KW - complexity
KW - specificity economics
KW - simulation
TI - Modelling Learning as Modelling
SP - 5
AV - public
EP - 37
ER -