Agent-Based Modeling for Complex Financial Systems

The continuing progression of globalization, coupled with the continuing adoption of new computing and network technology, means that today's global financial marketplace is best understood as a complex network of interacting market systems, in which events of world-wide significance unfold on timescales that are barely within the ability of humans to comprehend. Classic economic theories (such as, most famously, General Equilibrium Theory) have proven to be of limited value for understanding and modeling such networks of markets. Many researchers have concluded that the dynamics of networked market systems are better understood as complex adaptive systems, 1 in which independent software components interact without centralized control or oversight. 2 A key characteristic of complex adaptive systems is that, even if the individual micro behaviors of system components are all perfectly understood, it remains extremely difficult to predict the overall macro behaviors that the system might exhibit. One reason for this is that such systems exhibit emergent properties, in which a behavior is produced from the overall system which would not be produced by any individual components or subsets of components.