Quantifying complexity and measuring structure within complex systems

大多数有趣的系统都很复杂。该项目中感兴趣的复杂系统的特征是每个单独部分的简单动力学行为;以及许多不同部分之间复杂的交互网络。该项目将重点关注大脑中相互作用的神经元的大规模系统，以及通过人际接触传播流感。该项目将提供一个更好的互动网络模型;以及根据数据统计验证该模型的新方法。现有的复杂网络模型在统计上是有偏差的，因此，通过采用稳健的统计方法，这个问题将得到纠正，并提供一种随机选择代表性复杂系统的方法。

Most interesting systems are complex. The complex systems of interest in this project are characterised by a simple dynamical behaviour on each individual part; and a complicated web of interaction between the many distinct parts. The project will focus on the massive system of interacting neurones in the brain, and transmission of influenza via interpersonal contacts. This project will provide a better model of that web of interactions; and new methods for statistically validating this model against data. Existing models of complex networks are statistically biased so, by employing robust statistical methodologies, this problem will be rectified and provide a method for randomly choosing representative complex systems.