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  -

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