Preface
Complex systems are defined as systems with many interdependent parts that give rise to nonlinear and emergent properties determining their high-level functioning and behavior. Due to the interdependence of their constituent elements and other characteristics of complex systems, it is difficult to predict system behavior based on the “sum of their parts” alone. Examples of complex systems include human economies and societies, nervous systems, molecular interaction networks, cells and other living things, such as bees and their hives, and ecosystems, as well as modern energy and telecommunication infrastructures. Arguably, one of the most striking properties of complex systems is that conventional experimental and engineering approaches are inadequate to capture and predict the behavior of such systems. A relatively recent and more holistic approach employs computational techniques to model and simulate complex natural phenomena and complex man-made artifacts. Complex system simulations typically require considerable computing and storage resources (processing units and primary and secondary memory) as well as high-speed communication links. Supercomputers are the technology of choice to satisfy these requirements. Because supercomputers are expensive to acquire and maintain, there has been a trend to exploit distributed computing and other large-scale computing technologies to facilitate complex system simulations. Grid computing, service-oriented architectures, programmable ...
