Adaptive Learning by Genetic Algorithms (2^nd Ed., 2nd ed. 1999. Softcover reprint of the original 2nd ed. 1999) Analytical Results and Applications to Economic Models

The fact that I have the opportunity to present a second edition of this monograph is an indicator for the growing size of the community concerned with agent-based computational economics. The rapid developments in this field make it very difficult to keep a volume like this, which is partly devoted to surveying the literature, up to date. I have done my best to incorporate the relevant new developments in this revised edition but it is in the nature of such a work that the selection of material covered is biased by the authors personal interest and his informational constraints. My apologies go to all researchers in this field whose work is not or not adequately represented in this book. Besides the correction of some errors and typos several additions have been made. In the literature survey sections 2.4 (which was also reorganized) and 3.5 new material was added. I have also added a new section in chapter 3 which deals with the question how well empirically observed phenomena can be explained by GA simulations. A new section in chapter 6 presents a rather extensive analysis of the behavior of a two population GA in the framework of a sealed bid double auction market. Further minor additions and changes were made throughout the text.

1. Introduction.- 2. Bounded Rationality and Artificial Intelligence.- 2.1 Bounded Rationality in Economics.- 2.2 Artificially Intelligent Agents in Economic Systems.- 2.3 Learning Techniques of Artificially Intelligent Agents.- 2.4 Some Applications of CI Methods in Economic Systems.- 2.5 Potentiality and Problems of CI Techniques in Economics.- 3. Genetic Algorithms.- 3.1 What are Genetic Algorithms?.- 3.2 The Structure of Genetic Algorithms.- 3.3 Genetic Operators.- 3.4 Genetic Algorithms with a Non-Standard Structure.- 3.5 Some Analytical Approaches to Model Genetic Algorithms.- 3.6 Do Genetic Algorithms Describe Adaptive Learning?.- 4. Genetic Algorithms with a State Dependent Fitness Function.- 4.1 State Dependency in Economic Systems.- 4.2 A Markov Model for Systems with a State Dependent Fitness Function.- 4.3 The Difference Equations Describing the GA.- 4.4 Deviation from the Markov Process.- 4.5 A Numerical Example.- 4.6 Stability of the Uniform States.- 4.7 Two-Population Models.- 5. Genetic Learning in Evolutionary Games.- 5.1 Equilibria and Evolutionary Stability.- 5.2 Learning in Evolutionary Games.- 5.3 Learning by a Simple Genetic Algorithm.- 5.4 Two-Population Contests.- 6. Simulations with Genetic Algorithms in Economic Systems.- 6.1 A Model of a Competitive Market.- 6.2 An Overlapping Generations Model with Fiat Money.- 6.3 A Sealed Bid Double Auction Market.- 7. Stability and Encoding.- 7.1 The Cobweb Example Revisited.- 7.2 Impact of a Change in Encoding and Scaling.- 7.3 A Method for Finding Economic Equilibria.- 8. Conclusions.- A. Basic Definitions and Results Used.- A.1 Time Homogeneous Markov Chains.- A.2 Nonlinear Difference Equations and Stability.- B. Calculation of the Equilibria of the Evolutionary Games in Chapter 5.- B.1 Rock-Scissor-Paper Games.- B.2 The GA Deceptive Game GAD.- B.3 The Games G1 and G2.- C. Proof of Proposition 6.3.1.- References.- List of Figures.- List of Tables.

Introduction to the field of agent-based computational economics and genetic algorithms Analytical results and examples facilitate the understanding of the learning behavior of genetic algorithms in economic models