Computational Techniques for Modelling Learning in Economics

Computational Techniques for Modelling Learning in Economics

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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.
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Product details

  • Hardback | 391 pages
  • 162.6 x 241.3 x 27.9mm | 816.48g
  • Dordrecht, Netherlands
  • English
  • 1999 ed.
  • XIII, 391 p.
  • 0792385039
  • 9780792385035

Table of contents

Preface. List of Contributors. Part One: Simulating in Economics. Evolutionary Economics and Simulation; W. Kwasnicki. Simulation as a Tool to Model Stochastic Processes in Complex Systems; K.G. Troitzsch. Part Two: Evolutionary Approaches. Learning by Genetic Algorithms in Economics? F. Beckenbach. Can Learning-Agent Simulations Be Used for Computer Assisted Design in Economics? T.C. Price. On the Emergence of Attitudes towards Risk; S. Huck, et al. Interdependencies, Nearly-decomposability and Adaptation; K. Frenken, et al. Part Three: Neural Networks and Local Interaction. Neural Networks in Economics; R. Herbrich, et al. Genetic Algorithms and Neural Networks: A Comparison Based on the Repeated Prisoners Dilemma; R.E. Marks, H. Schnabl. Local Interaction as a Model of Social Interaction? D.K. Herreiner. Part Four: Boundedly Rational and Rational Models. Memory, Learning and the Selection of Equilibria in a Model with Non-Uniqueness; E. Barucci. A Behavioral Approach to a Strategic Market Game; M. Shubik, N.J. Vriend. Bayesian Learning in Optimal Growth Models under Uncertainty; S.M.N. Islam. Part Five: Cognitive Learning Models. Modelling Bounded Rationality in Agent-based Simulations Using the Evolution of Mental Models; B. Edmonds. Cognitive Learning in Prisoner's Dilemma Situations; T. Brenner. A Cognitively Rich Methodology for Modelling Emergent Socioeconomic Phenomena; S. Moss. Index.
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