Computational models are increasingly used to inform real-world policy decisions — from public health and climate planning to economics and infrastructure design. In practice, these models are not used as “prediction machines”, but as structured ways of exploring uncertainty, testing assumptions, and comparing scenarios when real-world experimentation is impossible.

In this talk, I’ll discuss what it actually means to build and use modelling systems in a policy context, drawing on experience developing epidemiological and other applied simulation models in Python. While epidemiology provides one concrete example, the focus of the talk is broader: how models are constructed, how they are used by decision-makers, and where misunderstandings often arise.

I will highlight common failure modes — such as over-interpreting model outputs, hidden assumptions in code, and miscommunication between technical builders and non-technical users — as well as what good practice looks like in terms of transparency, uncertainty handling, and reproducibility.

The goal of this talk is to give Python developers a clearer understanding of how modelling systems are used beyond academia, and why careful software design and communication matter when code has real-world consequences.