Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models / Edition 2

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Overview

This book deals with the learning behavior of boundedly rational agents in economic systems. In particular, the modeling of learning populations by genetic algorithms is studied in detail. After an extensive review and discussion of the existing literature in the first part, a mathematical analysis of the dynamic properties of genetic algorithm learning in the general framework of systems with a state dependent fitness function is provided. It is shown that co-evolutionary economic models typically fall into this class and the usefulness of the analytical results derived is illustrated in several game theoretic and microeconomic models. The mathematical analysis is complemented by extensive simulation analyses. The last part of the book demonstrates how the obtained theory may be used to design the algorithm such that the learning of equilibria of the economic system is facilitated.

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Product Details

  • ISBN-13: 9783540656074
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 5/28/1999
  • Edition description: 2nd, rev. a. enlarged ed. 1999
  • Edition number: 2
  • Pages: 200
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.56 (d)

Table of Contents

1 Introduction 1
2 Bounded Rationality and Artificial Intelligence 7
3 Genetic Algorithms 41
4 Genetic Algorithms with a State Dependent Fitness Function 71
5 Genetic Learning in Evolutionary Games 97
6 Simulations with Genetic Algorithms in Economic Systems 121
7 Stability and Encoding 161
8 Conclusions 169
A Basic Definitions and Results Used 171
B Calculation of the Equilibria of the Evolutionary Games in Chapter 5 175
C Proof of Proposition 6.3.1 181
References 183
List of Figures 193
List of Tables 197
Index 199
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