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.

近年来，从生物学和神经科学到经济学和社会科学，各种系统上都提供了大量数据。这以及不断增加的计算能力导致了基于大型计算模型的方法的激增，这些方法特别适合缺乏对此类复杂系统的基本“运动定律”知识的情况。我认为，旨在从复杂系统或大型数据集中提取简单模型或原理的方法仍然是可能的。这些依赖于我们对集体现象理解的进步，这些进步提供了丰富的强大方法来从复杂现象中提取简单模型。此外，信息论被认为是描述复杂系统的通用语言，它提供了可用于建模和大型数据集推理的简单原理。