  A population of committees of agents that learn by using neural networks is
implemented to simulate the stock market. Each committee of agents, which is
regarded as a player in a game, is optimised by continually adapting the
architecture of the agents using genetic algorithms. The committees of agents
buy and sell stocks by following this procedure: (1) obtain the current price
of stocks; (2) predict the future price of stocks; (3) and for a given price
trade until all the players are mutually satisfied. The trading of stocks is
conducted by following these rules: (1) if a player expects an increase in
price then it tries to buy the stock; (2) else if it expects a drop in the
price, it sells the stock; (3)and the order in which a player participates in
the game is random. The proposed procedure is implemented to simulate trading
of three stocks, namely, the Dow Jones, the Nasdaq and the S&P 500. A linear
relationship between the number of players and agents versus the computational
time to run the complete simulation is observed. It is also found that no
player has a monopolistic advantage.
