Computational Techniques for Modelling Learning in Economics (Advances in Computational Economics) - Info and Reading Options
By Thomas Brenner

"Computational Techniques for Modelling Learning in Economics (Advances in Computational Economics)" is published by Springer in May 31, 1999, it has 408 pages and the language of the book is English.
“Computational Techniques for Modelling Learning in Economics (Advances in Computational Economics)” Metadata:
- Title: ➤ Computational Techniques for Modelling Learning in Economics (Advances in Computational Economics)
- Author: Thomas Brenner
- Language: English
- Number of Pages: 408
- Publisher: Springer
- Publish Date: May 31, 1999
“Computational Techniques for Modelling Learning in Economics (Advances in Computational Economics)” Subjects and Themes:
- Subjects: Mathematical Economics - Study and teaching - Econometrics - Computational learning theory - Economics, mathematical
Edition Specifications:
- Format: Hardcover
- Weight: 1.8 pounds
- Dimensions: 9.5 x 6.4 x 1.1 inches
Edition Identifiers:
- The Open Library ID: OL7810259M - OL8362674W
- Online Computer Library Center (OCLC) ID: 40990315
- Library of Congress Control Number (LCCN): 99025823
- ISBN-13: 9780792385035
- ISBN-10: 0792385039
- All ISBNs: 0792385039 - 9780792385035
AI-generated Review of “Computational Techniques for Modelling Learning in Economics (Advances in Computational Economics)”:
"Computational Techniques for Modelling Learning in Economics (Advances in Computational Economics)" Description:
The Open Library:
"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."--BOOK JACKET.
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