The use of soft computing in finance and economics has become increasingly important. These methods have been proven to be a powerful tool in domains were analytic solutions are not a good alternative. Real-world problems involve complexity, noisy environments, uncertainty and vagueness, hence the popularity of this approach among both researchers and practitioners. So far it has been successfully used in financial engineering, risk management, portfolio optimization, industrial organization, auctions, searching equilibriums, studying the emergence of the representative agent and rational expectations, designing public policy, financial forecasting, market simulation or agent-based computational economics, among many other areas.
