The proposed architecture provides a testbed that a variety of experiments can be carried out. The market model is initially populated with a number of different types of agents. At each iteration, each agent forecasts next period's price and makes a trade by contacting another agent and offering a bid. They then proceed to negotiate on the price and the quantity of currency to be exchanged. Within this architecture, each agent can be either a human trader or a machine which can respond based on certain predictive modeling methods.
Agents make judgments based on each individual's predictions of market price directions.
The behavior of each agent depends on the cues each agent recognizes and the actions each agent takes. Although the central goal of each agent is similar, each individual agent is characterized with different internal representation which may lead to the different problem solving behavior. Once the trade is complete and the market clears, the accuracy of each agent can be calculated and the price of the next period will be revealed. Amongst the numerous learning algorithms that have been proposed for predicting futures, we have identified the following ones as our intelligent agents. The idea of development of neural networks is inspired by the computational aspects of a human brain.