Computational Techniques for Modelling Learning in Economics

Computational Techniques for Modelling Learning in Economics

by Thomas Brenner
     
 

Computational Techniques for Modelling Learning in Economics offers a critical overview on the computational techniques that are frequently used for modelling learning in economics. It is a collection of papers, each of which focuses on a different way of modelling learning, including the techniques of evolutionary algorithms, genetic programming, neural networks,… See more details below

Overview

Computational Techniques for Modelling Learning in Economics offers a critical overview on the computational techniques that are frequently used for modelling learning in economics. It is a collection of papers, each of which focuses on a different way of modelling learning, including the techniques of evolutionary algorithms, genetic programming, neural networks, classifier systems, local interaction models, least squares learning, Bayesian learning, boundedly rational models and cognitive learning models. Each paper describes the technique it uses, gives an example of its applications, and discusses the advantages and disadvantages of the technique. Hence, the book offers some guiding in the field of modelling learning in computation economics.

Product Details

ISBN-13:
9781461372851
Publisher:
Springer US
Publication date:
04/30/2013
Series:
Advances in Computational Economics Series, #11
Edition description:
Softcover reprint of the original 1st ed. 1999
Pages:
391
Product dimensions:
6.14(w) x 9.21(h) x 0.84(d)

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