Toward an Integration of Social Learning and Individual Learning in Agent-Based Computational Stock Markets: The Approach Based on Population Genetic Programming∗

Artificial stock market is a growing field in the past few years. The essence of this issue is the interaction between many heterogeneous agents. In order to model this complex adaptive system, the techniques of evolutionary computation have been employed. Chen and Yeh (2000) proposed a new architecture to construct the artificial stock market. This framework is composed of a single-population genetic programming (SGP) based adaptive agent with a SA (Simulated Annealing) learning process and a business school. However, one of the drawbacks of SGP-based framework is that the traders can’t work out new ideas by themselves. The only way is to consult researchers in the business school. In order to make the traders more intelligent, we employ multi-population GP (MGP) based framework with the mechanism of school. This extension is not only reasonable, but also has the economic implications. How do the more intelligent agents influence the economy? Are the econometric properties of the simulation results based on MGP more like the phenomena found in the real stock market? In this paper, the comparison between SGP and MGP is studied from two sides. One is related to the micro-structure, traders’ behavior and believe. The other is macro-properties, the properties of time series. The line of research is helpful in understanding the foundation of economics and finance, and constructing more realistic economic models.