Simplicity science

In recent years, massive amount of data have been made available on a variety of systems, from biology and neuroscience to economics and social sciences. This, and increasing computational power, has led to a surge of approaches based on large computational models, which are particularly suited in the absence of knowledge on the underlying “laws of motion” of such complex systems. I will argue that approaches aimed at extracting simple models or principles from complex systems or from large datasets are still possible. These rely on advances in our understanding of collective phenomena that provide a wealth of powerful methods to distill simple models from complex phenomena. Furthermore, information theory, considered as a universal language for describing complex systems, provides simple principles that can be used both in modelling and in inference from large datasets.