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Über dieses Buch

It took me over five years to write this book. Finishing my research project and thus finishing this book would not have been possible without the help of many friends of mine. Thus, the first thing to do is to say 'Thanks a lot' . This means at first place the Evangelisches Studienwerk Haus Villigst. They gave me a grant for my work, thus laying the important financial grounds of everything I've done. There is such a large number of friends I worked and lived with over the last few years that I cannot possibly mention them all by name, but I'll try, anyway: So, thanks Christiane, Gilbert, Maik, Karl, and everybody else feeling that his or her name should appear in this list. And, of course, thanks Franz Haslinger, for letting me do whatever I wanted to - and for even encouraging me to stick with it. One more thing I'd like to mention: Although this work is based on very heavy use of computer power, it is my special pride to say that not a single penny (i.e. Deutschmark) had to be spent for software in order to do this work. Instead, all that has been done has been done by free software. Thus, I would like to mention some of my most heavily used software tools in order to let you, the reader, know that nowadays you don't depend on big commercial software packages any more.

Inhaltsverzeichnis

Frontmatter

Introduction

Frontmatter

1. Introduction

Abstract
Man is not perfect. This — admittedly trivial — insight has up to now been applied to economics in a very limited extend. A large amount of work in theoretical economics concentrates on ‘perfectly rational’ subjects, who are sometimes even given the gracious gift of ‘perfect foresight’. The main actors of theoretical economic models are often omniscient and omnipotent. Even more, subjects are assumed to be identical with respect to their preferences and aversions, their endowments and decisions and many things more.
Thomas Riechmann

2. The Core Topics: Learning and Computational Economics

Abstract
Learning in economics is one of the central topics of this book. Thus, it is necessary to explain what is meant by the term ‘learning’ in economics and in this book.
Thomas Riechmann

3. An Exemplary Introduction to Structure and Application of Genetic Algorithms in Economic Research

Abstract
The goal of this chapter is to provide a simple introduction into the kind of genetic algorithms applied in economic theory. The main focus will be on technical aspects, the economic example merely serves as an illustration. This means that in this chapter new economic results will hardly be found. The model shown here is very simple and, more than this, it lacks one of the most important characteristics of the models to be presented in the further course of this work: It does not include state dependency of individual economic success1. The large number of simplifications make the model look a little stylized. Nevertheless, the simplifications are intentionally introduced in favor of clarity of the main technical goal, the introduction into the basic techniques of modeling economic problems in form of a genetic algorithm.
Thomas Riechmann

General Analysis of Genetic Algorithms

Frontmatter

4. Methods for the General Analysis of Genetic Algorithms as Economic Learning Techniques

Abstract
Economic models examining social learning with the help of GAs always come in the form of simulations. For a precisely specified economic problem, a computer program is constructed. The most important part of this program is the formulation of artificial economic agents, which are often constructed as objects. The most important part of the agents, in turn, is their decision system which, in this work, consists of a genetic algorithm or at least of parts of a GA. The core analysis of the behavior of the agents and by that the behavior of the economic model consists of running the simulation program, recording the resulting data and analyzing and interpreting the data. All the results won from this work are inductive by nature. Even if the simulation is run very many times: The results do not allow for deductive conclusions, they are never true in general, but only in a set of special circumstances.
Thomas Riechmann

5. Statistical Aspects of the Analysis of Economic Genetic Algorithms

Abstract
Simulation based on economic genetic algorithms generate a large amount of data informing about the performance of the agents and the results of the model. Every analysis and interpretation of GA models is thus based on statistical examinations of these data.
Thomas Riechmann

Economic Applications and Technical Variations

Frontmatter

6. Modifications: Election and Meta-Learning

Abstract
Up to this point, the main focus of the book was on the behavior of the canonical GA (Goldberg, 1989) used as a metaphor for learning in economic models. Originally, the canonical GA has been developed as a tool for optimization of non economic, static problems. The canonical GA is not a genuine tool for economic, agent based modeling. If a genetic algorithm is to be used as a true metaphor for economic learning, modifications to the canonical form of the algorithm are needed.
Thomas Riechmann

7. Extensions: Variable Time Horizon of Selection

Abstract
The model of regional monopolies discussed up to now, is too simple in at least one point: It is no satisfactory model of economic learning via GAs, because there is no state dependency. It has been mentioned above that the state dependency of individual economic success is an important characteristic of almost every economic problem. Even more, it is a major characteristic of models of economic GA learning. For example, the profit of a supplier in a market (except of a monopolistic market) does not only depend on the quantity of the good the supplier offers, but also on the market price. The market price, in turn, depends on the aggregate supply on the market, i.e. on the quantities all suppliers offer. This means that the profit of every single supplier is inseparably connected to each action of every member of the market and by that, to the state of the whole economy1.
Thomas Riechmann

8. Algorithms with Real Valued Coding

Abstract
This chapter is dedicated to an economic problem with a much more complex structure than all the problems focused before. It is the problem of consumer choice of private households. In the model, a population of households has to divide their income between the consumptions of three different consumption goods. The final goal of their decision is to achieve optimal utility.
Thomas Riechmann

9. A Multi Population Algorithm

Abstract
Like all the chapters in the third part of this book, this one, too, has an economic and a technical topic.
Thomas Riechmann

10. Final Remarks

Abstract
The main goal of this book is to show how genetic algorithms can be used in economic modeling. It should have been proved that the question ‘Can genetic algorithms be sensibly used in order to model and analyze economic problems?’ must be answered ‘Yes’.
Thomas Riechmann

Backmatter

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