Skip to main content

Computational Techniques for Modelling Learning in Economics

  • Book
  • © 1999

Overview

Part of the book series: Advances in Computational Economics (AICE, volume 11)

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (15 chapters)

  1. Simulating in Economics

  2. Evolutionary Approaches

  3. Neural Networks and Local Interaction

  4. Boundedly Rational and Rational Models

Keywords

About this book

Computational Techniques for Modelling Learning in Economics offers a critical overview of 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 guidance in the field of modelling learning in computation economics. In addition, the material contains state-of-the-art applications of the learning models in economic contexts such as the learning of preference, the study of bidding behaviour, the development of expectations, the analysis of economic growth, the learning in the repeated prisoner's dilemma, and the changes of cognitive models during economic transition. The work even includes innovative ways of modelling learning that are not common in the literature, for example the study of the decomposition of task or the modelling of cognitive learning.

Editors and Affiliations

  • Max-Planck-Institute for Research into Economic Systems, Germany

    Thomas Brenner

Bibliographic Information

  • Book Title: Computational Techniques for Modelling Learning in Economics

  • Editors: Thomas Brenner

  • Series Title: Advances in Computational Economics

  • DOI: https://doi.org/10.1007/978-1-4615-5029-7

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1999

  • Hardcover ISBN: 978-0-7923-8503-5Published: 31 May 1999

  • Softcover ISBN: 978-1-4613-7285-1Published: 17 October 2012

  • eBook ISBN: 978-1-4615-5029-7Published: 06 December 2012

  • Series ISSN: 0929-130X

  • Edition Number: 1

  • Number of Pages: XIII, 391

  • Topics: Economic Theory/Quantitative Economics/Mathematical Methods

Publish with us