Political systems can be understood through a set of mathematical and mechanistic ideas that help make sense of how they behave over time. Concepts such as homeostasis, autopoiesis, growth, equilibrium, feedback, and phase change give us ways of describing how political processes stabilise, amplify, or transform. Rather than treating politics as purely contingent or narrative-driven, these tools allow us to see recurring structural patterns: how actions generate responses, how those responses feed back into the system, and how certain configurations persist while others break down or reorganise.

Institutions can be understood in similar terms. Some behave in a homeostatic way, actively counteracting disturbances in order to maintain a stable state. Others exhibit growth dynamics, expanding their reach, resources, or influence until they encounter constraints or diminishing returns. In some cases, organisations take on autopoietic characteristics, reproducing the internal rules, roles, and cultures that sustain them, even when their original purpose has weakened or disappeared. These are not just abstract or playful analogies: they point to the fact that political systems have strong quantitative foundations, where votes, resources, and participation are all measurable. Thinking in these terms helps build a more precise understanding of why political systems sometimes appear stable, sometimes become unstable, and sometimes shift abruptly into new forms. In this topic, we will use guided reading and writing to explore how these mechanistic and systems-based ideas can be applied to better understand real political systems.

I have spent much of my career building mathematical and computational models of biological and social systems. We can learn the fundamentals of systems thinking by mapping political systems and experimenting with simple models of their behaviour. The aim is to develop a practical vocabulary for recognising feedback, stability and structural change—and a set of tools that can be applied to political questions.

Core reading

• Donella H. Meadows, Thinking in Systems: A Primer (Chelsea Green, 2008).
• Mitchel Resnick, Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds (MIT Press, 1994).