You could say he viewed the markets as a large system of agents perceiving each other mainly through the limited information source of price movement. As I start out on the path of organizing ways of looking at large systems of agents, I am beginning to realize that we are entering vast new realms of thinking about computing. The vast array of kinds of intelligent units and the multiplicity of ways they can interact and communicate is so wide open. Agents have the same potential to benefit and create disciplines of thought that we can't even begin to foresee. The examples that I give, such as market analysis, traffic routing and scheduling, and other decision support tools are only a taste of things to come.
Agents are smart things that perceive, make decisions, and then act.
This definition can include intelligent software objects, ants, people, business rules, and much more. Large systems of agents, even when each individual agent has limited information or intelligence, can show remarkable total intelligence, as with ant colonies, bee hives, or general market pricing, or seemingly highly maladaptive behavior, as with lemmings or unsustainable market bubbles. The answer is in large part dependent on the system dynamics resulting from the properties and interactions of the parts. Systems of agents can tell us a lot about market dynamics and how markets set prices. One key issue that differentiates these architectures is the nature of the communication between the agents.