Computational Techniques for Modelling Learning in Economics / Edition 1

Computational Techniques for Modelling Learning in Economics / Edition 1

by Thomas Brenner
     
 

ISBN-10: 0792385039

ISBN-13: 9780792385035

Pub. Date: 05/31/1999

Publisher: Springer US

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, incl uding the techniques of evolutionary algorithms, genetic programming, neural networks

Overview

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, incl uding the techniques of evolutionary algorithms, genetic programming, neural networks, classifier systems, local interaction models, least s quares learning, Bayesian learning, boundedly rational models and cogn itive learning models. Each paper describes the technique it uses, giv es an example of its applications, and discusses the advantages and di sadvantages of the technique.

Product Details

ISBN-13:
9780792385035
Publisher:
Springer US
Publication date:
05/31/1999
Series:
Advances in Computational Economics Series, #11
Edition description:
1999
Pages:
391
Product dimensions:
6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

Preface. List of Contributors. Part One: Simulating in Economics. Evolutionary Economics and Simulation; W. Kwasnicki. Simulation as a Tool to Model Shastic 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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