Download Adaptive Learning by Genetic Algorithms: Analytical Results by Herbert Dawid PDF

By Herbert Dawid

This publication considers the educational habit of Genetic Algorithms in financial structures with mutual interplay, like markets. Such platforms are characterised by means of a nation based health functionality and for the 1st time mathematical effects characterizing the longer term final result of genetic studying in such structures are supplied. numerous insights about the effect of using varied genetic operators, coding mechanisms and parameter constellations are received. The usefulness of the derived effects is illustrated via loads of simulations in evolutionary video games and monetary types.

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Obviously one of the most desirable feature of the simulations with artificially intelligent agents is the explicit representation of every individual in the population. Contrary to the econometric learning rules it is basically possible to build a heterogeneous population of agents who do not only differ in their strategies, but also in their learning behavior. An example for such a population is given for example in Beltrati and Margarita [13] and we will provide another one in chapter 5. We think that this feature is very important, for it is by no means clear why different individuals which are assumed to act differently as a member of the economic system have to use the same rule in order to build expectations or update their strategies.

As the rest of this work is concerned with the analysis of the behavior of genetic algorithms in economic systems, we will restrict the discussion to this kind of algorithm, but most of the arguments will hold also for the other CI techniques. Considering GAs we have to admit that the interpretation of the learning rules is quite difficult, if we consider socio economic learning processes rather than real evolutionary processes. We will discuss possible interpretations of the different genetic operators in an economic environment in the next chapter, but have to admit that these interpretations are in our opinion not completely satisfactory.

On the other hand, the network representing a naive agent consists only of one input and one output unit without any hidden unit inbetween. The naive agent bases his expectations only on the previous market price. Contrary to the other two types the naive expectations cause no costs. ions, where the actually paid price is the mean value of both price expectations. After every period the weights of the networks are updated with the help of observed data. Every T periods the agents may choose a new strategy.

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