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Details of Grant 

EPSRC Reference: EP/W002949/1
Title: Turing AI Fellowship: The LARGE AGENT COLLIDER: Robust agent-based modelling at scale
Principal Investigator: Wooldridge, Professor M
Other Investigators:
Calinescu, Dr A
Researcher Co-Investigators:
Project Partners:
Accenture plc (Global) JP Morgan Chase
Department: Computer Science
Organisation: University of Oxford
Scheme: EPSRC Fellowship - NHFP
Starts: 01 September 2021 Ends: 31 August 2026 Value (£): 3,625,312
EPSRC Research Topic Classifications:
Artificial Intelligence Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Financial Services
Related Grants:
Panel History:
Panel DatePanel NameOutcome
19 May 2021 Turing AI World-Leading Researcher Fellowship Interview Panel Announced
Summary on Grant Application Form
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 systems down at the level individual actors/decision-makers. Agent-based models allow us to capture aspects of systems (such as emergent properties, which arise 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 highlighted many difficulties currently associated with agent-based modelling. First, current agent-based modelling environments force us to embed 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, we need better ways of populating such models with realistic agent behaviours. Third, such models are limited in the extent to which we can rely on their predictions: we do not know how to calibrate such models (crudely: how can we be confident that $1 in a simulation corresponds to $1 in the real world?) Third, we have 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.

The overarching technical goal of this project is to effect a step change in our ability to develop and deploy robust large scale agent-based simulations. Using state of the art techniques in AI and machine learning, we will carry out the fundamental research to develop the scientific & engineering methodology necessary to transform our capability in each of the areas identified above: allowing us to develop, populate, calibrate, and validate agent-based models at scale.

Working with major industrial partners, we will test and refine our techniques on a range of real-world case studies. If successful, then this project will transform agent-based modelling from an ad hoc, trial and error process into a robust engineering discipline with a rigorous methodological foundation. It will establish Oxford as the world leader in the applications and analysis of multi-agent systems, and consolidate the UK's existing strengths in this area. Given our previous experience with agent-based financial modelling, we expect our results will be of considerable scientific interest and will have direct commercial value.

Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.ox.ac.uk