Decision-making in healthcare and energy systems is becoming more challenging. Markets, technologies, and human behaviour can change quickly, while decisions often need to account for uncertainty, regional differences, and interactions between many actors. Traditional mathematical models are valuable for planning, but they often use simplified assumptions about how people and organisations behave. This can limit their ability to represent real-world behaviour and unexpected changes.
The ABM-POLICY project develops and applies agent-based modelling (ABM) to support better planning and policy decisions in healthcare and energy systems. In ABM, individuals, organisations, producers, consumers, and other relevant actors are represented as agents with different characteristics, goals, and behaviours. Their interactions can create complex system-level outcomes. This approach helps assess how different policies affect disease transmission, healthcare demand, energy markets, and the transition to new energy technologies.
The project uses two application areas. In healthcare, the work builds on SINTEF's previous ABM2HC project, where an agent-based model was developed to simulate COVID-19 transmission and optimise measures such as social distancing and mask use. ABM-POLICY extends this work by including regional policies and behavioural differences. In energy systems, ABM is used to study interactions between producers, consumers, manufacturers, and other market actors, for example in emerging hydrogen or ammonia markets.
SINTEFs contribution in the project
SINTEF contributes expertise in mathematical modelling, simulation, optimisation, machine learning, and decision support. These methods are combined to develop practical tools that help decision-makers compare policies, understand system responses, and identify robust strategies for complex and changing environments.