Multi-agent based modeling of artificial stock markets by using the co-evolutionary genetic programming approach

This paper deals with multi-agent based modeling of artificial stock market by using the co-evolutionary Genetic Programming (GP) by considering social learning. Cognitive behaviors of agents are modeled by using the GP to introduce social learning as well as individual learning. Assuming five types of agents, in which rational agents prefer forecast models (equations) or production rules to support their decision making, and irrational agents select decisions at random like a speculator. Rational agents usually use their own knowledge base, but some of them utilize their public (common) knowledge base to improve trading decisions. By using the result of simulation studies on artificial market, it is shown that the time series for stock price resembles real stock price statistically. It is also shown that the lack of social learning leads the system to a very monotone market, and only a simple behavior of the market is realized. Moreover, we can see the effectiveness of classifier systems where we utilize a pool of decision rules in which not only prominent but also rules having potential rewards in fluctuating environment. It is also seen that the growth of wealth of irrational agent is almost always better than rational agents even though they analyze and behave on reasonable decision. The result provides us the way to analyze real market where traders usually use social learning and environment-dependent rules.