This topic builds on Systems Thinking: Understanding Political Dynamics.

Simulation models give us a way to explore how politics systems behave, and what might happen when we change or interfere with them. Examples are how opinions spread through a population, how protests or political movements grow, how inequality accumulates, how institutions respond to pressure, or how different interventions might change the course of a system.

A simulation is a simplified representation of a system. We define the things in it, how they behave, and how they interact, then see what larger patterns emerge. Simulations can be mathematical, computational, physical, or even played out by people.

Traditionally, computer simulation required programming and mathematical expertise. LLMs change that. It is now possible to describe a system in ordinary language and have an LLM help turn it into a working model. That makes simulation modelling much more accessible, but it does not remove the hard part: deciding what to include, what to leave out, what assumptions you are making, and whether the results mean anything.

In this topic, I can help you will build simple models of social and political systems and experiment with them. I can help you choose a system, identify a possible mechanism, turn it into a toy model, use an LLM to implement it, and interpret the results.

The emphasis is on learning how to think with models rather than learning to code.

Core reading

• Marco A. Janssen, Introduction to Agent-Based Modeling (open textbook).
• Ezequiel A. Di Paolo, Jason Noble & Seth Bullock, “Simulation Models as Opaque Thought Experiments” (2000).
• John Bryden, Eric Silverman & Simon T. Powers, “Modelling transitions between egalitarian, dynamic leader and absolutist power structures” (2022).
• Joshua M. Epstein, “Modeling Civil Violence: An Agent-Based Computational Approach” (2002).