About the predictability and complexity of complex systems

Abstract

Understanding and prediction are the ultimate goals in Modeling and Simulation. In this endeavor, we are faced with ever-increasing complexity. This works twofold with ever more complex models and modeling techniques mapping to the increasingly complex phenomena that we are trying to simulate. With steadily raising computing capability, we are able to incorporate more complexity into our models and simulations. In return, this has had an impact on our thinking paradigm and capability for abstraction and modeling techniques. So, when are things complex? Our thorough analysis has revealed that complexity can mean very different things to different people, contexts and environments. In many cases, it refers to an aspect or part of the system under study, in others the magnitude and variety of the system itself. To some "complexity" rather means the unknown: that what cannot be easily understood, modeled or predicted with current knowledge. In this keynote address, I will present a unifying and systematic approach to complexity, to bring some clarity into the unknown, and a step further towards predictability. We will be looking at different types of complexity, ranging from simple structural and functional complexity, to those highly complex systems that change (but not necessarily evolve) through their own dynamics. With this in mind, we will be able to identify what can be predicted and what cannot. By knowing it, we will be in a better position to find the unknown, which is the most important step towards the prediction of complex systems.