Learning in Economics : Analysis and Application of Genetic Algorithms

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.

The book is dedicated to the use of genetic algorithms in theoretical economic research. Genetic algorithms offer the chance of overcoming the limitations traditional mathematical tractability puts on economic research and thus open new horzions for economic theory. The book reveals close relationships between the theory of economic learning via genetic algorithms, dynamic game theory, and evolutionary economics.
Genetic algorithms are here introduced as metaphors for processes of social and individual learning in economics. The book gives a simple description of the basic structures of economic genetic algorithms, followed by an in-depth analysis of their working principles. Several well-known economic models are reconstructed to incorporate genetic algorithms. Genetic algorithms thus help to find genuinely new results of well-known economic problems.