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Data Assimilation for Agent-Based Models: Applications to Civil Emergencies

Periodic Reporting for period 2 - DUST (Data Assimilation for Agent-Based Models: Applications to Civil Emergencies)

Reporting period: 2019-07-01 to 2020-12-31

When developing computer models of human systems, there are many situations where it is important to model the individual entities that drive the system, rather than using aggregate methods that combine individuals into homogeneous groups. In such circumstances, the technique of ‘agent-based modelling’ has been shown to be useful. In an agent-based model, individual, autonomous ‘agents’ are created. These agents can be given rules or objectives that determine how they will behave in a given situation. They can also interact with each other and with their environment. In this manner it is possible to simulate many of the interesting outcomes that emerge as social systems evolve which might be missed using aggregate methods that do not account for individuals, or their interactions, specifically.

There is a drawback with agent-based models though; they cannot incorporate real-time data into their simulations. This means that it is not possible to use agent-based modelling to simulate systems in real time. Agent-based modelling is an ideal tool for modelling systems such as traffic congestion, crowd dynamics, economic behaviour, disease spread, etc., but it is limited in its ability to make short term, real-world predictions or forecasts because it is not able to react to the most up to date data.

The aim of this project is to develop new methods that will allow real-time data to be fed in to agent-based models to bring them in to line with reality. It will leverage existing ‘data assimilation’ methods that have been established in fields such as meteorology and test how applicable those methods are when adapted for agent-based modelling. This has important implications for society because, if successful, these new methods will allow us to create real-time simulations for policy makers. This could help us to manage crowding in public transport hubs, disease spread through communities, traffic in city centres or evacuations in response to an evolving emergency.
The project has so far been focussing on methodological development. We have adapted a number of methods that are commonly used in other fields and have tested how well they are able to allow us to conduct data assimilation for use agent-based models. Specifically, we are looking at techniques called Particle Filters, Unscented and Ensemble Kalman Filters, and entirely innovate methods based on quantum field theory. We are also applying the methods to scenarios of increasing complexity. Initially we applied them to simple, hypothetical spaces before moving on to real environments; most recently the movements of pedestrians in Grand Central Terminal in New York City. Next we aim to apply the methods to situations with larger numbers of agents (from hundreds to thousands) in more complex spaces and where the data describing the phenomena are diverse.

The methods are showing promise but there are a number of challenges that must be overcome. Firstly, agent-based models are extremely computationally expensive so can take a long time to run. Most data assimilation methods also require a large number of models to be run simultaneously, which exacerbates the problem. To get around this, we are currently looking at emulator/surrogate methods that should allow the full agent-based model to run more quickly, albeit with lower accuracy. We have tested Random Forests and Gaussian Process Emulators; both of which hold promise. Secondly, data assimilation methods are typically designed for models with continuous variables (e.g. air pressure, wind speed, etc.) but agent-based models often have discrete variables (e.g. an agent’s age, or their destination). Hence we are also developing adaptations to the classic methods that will allow us to work with discrete variables.
The application of the Particle Filter, Unscented Kalman filter and Ensemble Kalman Filter (forthcoming) to the technique of agent-based modelling are novel and move beyond the state-of-the-art. As we refine these methods we will begin to better understand their advantages and disadvantages and will be able to apply them to iteratively more complex situations. As the project progresses we will move towards the simulation of larger systems, ideally whole city centres using publicly-available data that describe the urban dynamics.