The Large Agent Collider

The aim of this project is to effect a step change in the ability to develop and deploy valid and robust, large-scale, agent-based models.  

Agent-based models are increasingly used throughout industry and academia, in areas ranging from financial modelling to logistics and supply chain management, where they are used to model complex socio-technical systems down at the level of individual actors. 

Agent-based models allow the capturing of aspects of systems (such as emergent properties, which arise in unpredictable ways from the interaction of many agents) that conventional modelling does not permit. Agent-based modelling came to international prominence when an agent-based epidemiological model of COVID-19 was revealed as one of the key drivers behind the UK government's decision to enter a lockdown in March 2020.  

Although they are widely used, as an engineering discipline agent-based modelling remains in its infancy, and subsequent criticisms of the COVID-19 model highlight common difficulties in agent-based models. First, current agent-based modelling environments force the embedding of key assumptions directly within code, thereby obfuscating such assumptions and making it hard to understand them (clearly essential for situations such as the COVID model). Second, there needs to be better ways of populating such models with realistic agent behaviours. Third, such models are limited in the extent to which their predictions can be relied upon: knowledge is lacking as to how to calibrate such models. Fourth, there is no available methodology for validating such models: existing techniques (e.g., model checking, used for formally verifying that systems satisfy their requirements) are unsuitable in their present form for agent-based models. 

Using state-of-the-art techniques in AI and machine learning, this project will see fundamental research in the development of the scientific and engineering methodology necessary to transform capability with respect to modelling, populating, calibrating, and validating agent-based models at scale. Working with industrial partners, and building on extensive previous in-house models, techniques will be tested and refined on a range of case studies. If successful, agent-based modelling will be transformed from an ad hoc, trial and error process into a robust engineering discipline with a rigorous methodological foundation. The project will establish Oxford as a world leader in the applications and analysis of multi-agent systems in general, and agent-based modelling specifically, and will greatly strengthen the UK’s capabilities in this important and rapidly expanding area.