Bayesian inference and prediction in complex social systems

Complex behaviour in many systems arise from interactions between locally interacting agents, resulting in an emergent phenomenon. Complex systems are prevalent in human behaviour; two examples of practical interest are organised criminal networks and urban retail structure. Traditional analytical approaches are limited when trying to model such behaviour, and novel approaches are required to develop predictive models. This work is largely interested in the investigation of new computational approaches for inference and prediction in complex, typically multiscale, systems.

The current interest in machine learning and data science has generated the widespread impression that such methods are capable of solving most problems, without the need for scientific inquiry. Likewise, despite enormous advances in mathematical modelling and computational science, models of complex systems tend to have limited predictive capability. In this work we explore mechanistic and probabilistic mathematical models and look towards data assimilation techniques and inference in these models. We explore models of criminal behaviour and urban retail structure and estimate the parameter values using real-world data. Furthermore, we are interested in new methods that make Bayesian inference feasible in complex systems.