Artificial Financial Markets with Adaptive Trading Agents

Motivation: In the last decade there has been a surge of interest within the finance community in describing equity markets through computational agent models [7]. At the same time, financial markets are an important application area within artificial intelligence for the fields of agent-based modeling and machine learning, since agent objectives and interactions tend to be more clearly defined, both practically and mathematically, in these markets than in other areas. Computational modeling of markets allows for the opportunity to push beyond the restrictions of traditional theoretical models of markets through the use of computational power while also allowing a fine-grained level of experimental control that is not available in real markets. Thus, data obtained from artificial market experiments can be compared to the predictions of theoretical models and to data from real-world markets, and the level of control allows one to examine precisely which settings and conditions lead to the deviations from theoretical predictions usually seen in the behavior of real markets. This project is also an application area for the general problems of distributed intelligence such as collective learning, coordination and competition. We are interested in studying how software agents endowed with learning abilities might interact, co-evolve, and cooperate in societies of learning agents.