Alternative distributed models for the comparative study of stock market phenomena

In this paper we present a methodology of studying the complex phenomena emerging in stock markets. This methodology is based on the use of distributed multi-agent models with minimal knowledge representation and reasoning capabilities that have proven to be a powerful modeling tool for complex biological systems. Unlike neural and ''neoconnectionist'' models, our models allow a comparative and incremental evaluation of their validity and relevance to the observed phenomena. The possibility of their application to the modeling and study of stock market phenomena is demonstrated on a simple example of a central agency that regulates the behavior of the investors: we show how a ''blind'' or myopic behavioral model reproduces results found in the literature and how the mutation of the model according to the parameters' values or the adaptation structures gives rise to a series of complex phenomena comparable to those observed in reality. (C) Elsevier Science Inc. 1997.