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Computational Techniques for Modelling Learning in Economics

  • Thomas Brenner

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

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Simulating in Economics

    1. Front Matter
      Pages 1-1
    2. Witold Kwaśnicki
      Pages 3-44
  3. Evolutionary Approaches

    1. Front Matter
      Pages 71-71
    2. Frank Beckenbach
      Pages 73-100
    3. Steffen Huck, Wieland Müller, Martin Strobel
      Pages 123-143
    4. Koen Frenken, Luigi Marengo, Marco Valente
      Pages 145-165
  4. Neural Networks and Local Interaction

    1. Front Matter
      Pages 167-167
    2. Ralf Herbrich, Max Keilbach, Thore Graepel, Peter Bollmann-Sdorra, Klaus Obermayer
      Pages 169-196
    3. Dorothea K. Herreiner
      Pages 221-239
  5. Boundedly Rational and Rational Models

    1. Front Matter
      Pages 241-241
    2. Martin Shubik, Nicolaas J. Vriend
      Pages 261-282
  6. Cognitive Learning Models

  7. Back Matter
    Pages 387-391

About this book

Introduction

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.

Keywords

agents algorithms complex systems economic growth economics evolutionary economics genetic programming modeling simulation simulations

Editors and affiliations

  • Thomas Brenner
    • 1
  1. 1.Max-Planck-Institute for Research into Economic SystemsGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-5029-7
  • Copyright Information Kluwer Academic Publishers 1999
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7285-1
  • Online ISBN 978-1-4615-5029-7
  • Series Print ISSN 0929-130X
  • Buy this book on publisher's site