Adaptive Learning By Genetic Algorithms

Adaptive Learning by Genetic Algorithms

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. TOC:Introduction.- Bounded Rationality and Artificial Intelligence.- Genetic Algorithms.- Genetic Algorithms with a State Dependent Fitness Function.- Genetic Learning in Evolutionary Games.- Simulations with Genetic Algorithms in Economic Systems.- Stability and Encoding.- Conclusions.- Basic Definitions and Results Used.- Calculation of the Equilibria of the Evolutionary Games in Chapter 5.- Proof of Proposition 6.3.1.The complete table of contents can be found on the Internet: http://www.springer.de

Adaptive Learning by Genetic Algorithms

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.

Adaptive Learning by Genetic Algorithms