 ConclusionsHerbert Dawid
Chapter 8 in Adaptive Learning by Genetic Algorithms, 1999, pp 169-170 from Springer Abstract: Abstract These notes are intended to contribute to the growing field of research dealing with the use of AI and CI techniques in economics. I have shortly summarized some of the most important contributions of the recent years and have afterwards concentrated on a thorough analysis of a special CI technique, namely genetic algorithms. It was argued that GAs, due to their decentralized structure which very naturally resembles to a group of economic agents and their interactions, are especially well-suited to simulate the behavior of an economic system. Further an interpretation in a behavioral sense of the single operators contained in a genetic algorithm was suggested, but also the problems with the economic interpretation of certain aspects of the algorithm were pointed out. The fact that in economic setups the fitness of a single string depends on the current state of the whole population has led to the conclusion that the analytical models which describe the behavior of a genetic algorithm used to solve an optimization problem can not be applied in most economic systems. Perhaps the main results are several propositions describing the limit behavior of a genetic algorithm in systems where the fitness function is state dependent. These theoretical results, but of course also the learning ability of a GA as such, have been illustrated afterwards with several examples from the field of game theory and economics. The simulations showed that mathematical reasoning enables us not only to understand but also to predict the behavior of GAs in economic systems. However these examples made also clear that a local analysis like the one done in chapter 4 is in many cases not able to explain the complete behavior of the system. In several cases heuristic explanations for a certain kind of behavior had to be given instead of a rigorous mathematical analysis. We have to face the fact that the current knowledge about high-dimensional non-linear difference equations does not enable us to describe the global behavior of such a system. Nevertheless, I am confident that the application of special theories like the graph theoretical approach taken here in proposition 4.2.1 or the theory of nonlinear dynamical systems will permit further mathematical insights into the behavior of GAs in SDF systems. Keywords: Genetic Algorithm; Economic System; Nonlinear Dynamical System; Economic Agent; Global Behavior (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-18142-9_8 Ordering information: This item can be ordered from DOI: 10.1007/978-3-642-18142-9_8 Access Statistics for this chapter More chapters in Springer Books from Springer |
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