During the COVID crisis, computer modelling played a significant role in evaluating difficult policy options. In particular, a form of computer modelling known as empirical agent-based modelling, which explicitly represents people and businesses interacting with each other in a simulated geographical space, can be used to see how and whether proposed policy interventions yield their intended societal effect, while checking for disproportionate consequences in different regions, or by gender or ethnicity. These models are expensive to build and use, because they have many potential parameters to adjust, and can produce very different outcomes, even when given the same parameters and initial conditions. Experimentation with such models to explore possible interventions can therefore use months of computing time. Exascale computing means that this time can be reduced to a few seconds, creating the potential for scenario evaluation to be done as part of a creative, exploratory discussion.
Although exascale computing means that experimenting with an existing model can be done much more quickly, there is more fundamental uncertainty in the social sciences over the structure of such models in the first place – due to the diversity of ways in which social systems can be conceived and theorized. The dramatic shortening of computing time needed to experiment with a model at exascale also means that model development and data assembly and integration – which also take months of time – are now much more significant bottlenecks. Exascale computing therefore creates a context in which we need a deeper review of the computing environments in which agent-based modelling is done.
The ExAMPLER project will undertake a series of activities aimed at engaging with the diverse, interdisciplinary and international community of researchers working on empirical agent-based modelling, to discuss what exascale computing means for the community. We will undertake a structured literature review to assess the state-of-the-art in high-performance computing to support empirical agent-based modelling and use this as a basis to create Key Performance Indicators of ‘exascale readiness’ in a community, so that we can benchmark ourselves against other communities. We will co-construct a vision of the future of empirical agent-based modelling supported by exascale computing, and a roadmap of the software and hardware, institutions and training needed to bring that vision into effect. We will engage with other ExCALIBUR communities considering the potential of exascale computing and use existing conferences as a basis for meeting with our own community.
For more information about the project, see our website at https://exascale.hutton.ac.uk
