John Bryden

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).

A model might be an equation, a map, a simulation, a story or a physical arrangement of stones. A Zen garden is not a landscape, yet it preserves or evokes something important about one. Metaphors work similarly: describing a career as a ladder or life as a journey creates an implicit model with particular structures, directions and possibilities. What happens when we make those assumptions explicit?

This topic weaves around the two core topics, Systems Thinking: Understanding Political Dynamics and Building Evidence: Data Science in the Age of AI. Those topics introduce ways to model systems and build evidence from data; this one turns those methods back on themselves. What assumptions are built into a model? What does it make visible, and what becomes harder to see? The aim is not to retreat from systems thinking or empirical evidence, but to use them more imaginatively and self-consciously.

Play gives us a way to explore and test models without treating them as settled truths. Roger Caillois distinguished competition, chance, simulation and vertigo, alongside a spectrum from spontaneous play to formal games. We can compete models against one another, introduce chance, inhabit them through role-play or deliberately destabilise their categories. Play both creates temporary systems of rules and allows us to bend, replace or step outside them.

I have spent much of my career constructing mathematical and computational models, but models need not begin with mathematics. We would build models of ourselves using metaphors, diagrams, objects, stories or games, then allow other people to interpret and disrupt them. The aim is to become more imaginative and self-conscious model-makers, better able to see what any representation reveals, assumes and excludes.

Core reading

• Roger Caillois, Man, Play and Games (University of Illinois Press, 2001).
• George Lakoff and Mark Johnson, Metaphors We Live By (University of Chicago Press, 1980).
• Ezequiel Di Paolo et al. "Simulation Models as Opaque Thought Experiments" (2000).

This topic builds directly on Building Evidence: Data Science in the Age of AI and assumes familiarity with the foundations introduced there: how data represent the phenomenon being studied, how samples are constructed, and how patterns can be measured without claiming more than the evidence supports. Social media listening puts those methods to work on a particularly difficult form of political data. It begins with a deceptively simple question: what are people saying?

Before we can answer it, we have to decide which people, which platforms, which posts and which kinds of speech count as data. APIs, commercial datasets, scraping and platform search each produce different windows onto the conversation, shaped by technical constraints and design choices. None of them is the public itself, but each is a partial representation that must be made explicit if we are to treat it as evidence.

Once collected, the data can be filtered, classified and organised into issues, positions, communities, places and changes over time. Network analysis can show how conversations cluster and travel. New language models can process volumes that would previously have been impossible, but sophisticated analysis does not make a biased sample representative.

I have spent years analysing online political behaviour and now build pipelines that turn large collections of posts and media into structured, reviewable evidence. I can help you construct a small listening workflow—from defining a question and collecting data to categorisation, network analysis and a short political brief. The aim is to learn how to turn online conversation into useful intelligence while making its blind spots and uncertainties explicit.

Core reading

• Matthew R. DeVerna et al., “CoVaxxy: A Collection of English-Language Twitter Posts About COVID-19 Vaccines” (2021).
• Deen Freelon et al., “The Post-API Age of Social Media Data Access: Past, Present, and Future” (2025).

This topic builds on both Building Evidence: Data Science in the Age of AI and Listening to Politics at Scale. It applies the same questions about representation, sampling, classification and inference to an especially difficult and politically contested problem. Misinformation is not a single observable property of a post. An algorithm might examine language, compare claims with evidence, score the history of a source, trace patterns of diffusion or look for coordinated behaviour. Each method detects something different, and none gives us a simple mechanical test for truth.

The difficult cases expose the politics inside the technical problem. Who defines the ground truth? Is a sincerely mistaken claim the same as deliberate deception? How should a system handle satire, contested interpretations or knowledge that is still changing? A detector can also become a political instrument if its errors fall unevenly or if nobody can challenge its decisions.

I have worked on research involving bots, online communities and the spread of contested information. I can help you compare content-, source-, bot- and coordination-based methods, then design and test a practical detection process on difficult cases. The aim is to understand what a detector is really detecting, evaluate its errors and identify where human judgement and political power enter the system.

Core reading

• Francesco Pierri et al., “One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study” (2023).
• Salvador Lopez-Joya et al., “Dissecting a Social Bot Powered by Generative AI: Anatomy, New Trends and Challenges” (2025).

Using AI to build evidence can easily produce a house of cards. Give it some data and it can quickly return code, categories, charts, networks and a confident conclusion, with each layer resting on choices we may never have examined. That is a good reason to look underneath—but also an invitation to play. AI gives us a fast way to try methods, change assumptions and see what happens when we pull out a card.

At heart, building evidence involves two deceptively simple questions. How do our data represent the thing we want to study? And what are those data telling us, in those terms? I can show you some fundamental methods for counting, comparing, plotting distributions and finding relationships. Networks are a particularly vivid example: choose what counts as a node and a connection, draw the result, then explore centrality, clustering and communities. Change the representation and the evidence may change with it.

I have spent much of my career using data, mathematical models and network analysis to study biological and political systems. I can show you how to use AI and Python to investigate small political datasets, learning fundamental methods for counting, comparing, visualising and analysing networks as we go. The aim is to build enough practical understanding to produce a simple analysis, explain what its data represent and recognise when its conclusions outrun the evidence.

Core reading

• David Spiegelhalter, The Art of Statistics: Learning from Data (Pelican, 2019).
• Duncan J. Watts, Six Degrees: The Science of a Connected Age (W. W. Norton, 2003).

This topic builds directly on Systems Thinking: Understanding Political Dynamics. Once we have a shared language for feedback, stability, adaptation and failure, we can ask what cybernetics adds: how systems use information to regulate themselves when no single actor can understand the whole.

Norbert Wiener developed cybernetics across machines, organisms and societies; Stafford Beer applied it to organisations and government. Their work shares Friedrich Hayek’s starting point that knowledge is dispersed, local and often tacit, but asks whether autonomous parts can still be coordinated through carefully designed feedback, information flows and escalation. I know this territory less well than mathematical modelling and network analysis, so this is genuinely exploratory: we can investigate the tradition together rather than treat it as a settled doctrine.

AI makes the attempt to reinvent cybernetics especially urgent. We can examine or redesign a real institution as an information-processing system, asking what should remain local, central, automated or contestable. The aim is to use cybernetic ideas to evaluate—and begin designing—forms of coordination that enlarge collective intelligence without simply strengthening surveillance and control.

Core reading

• Norbert Wiener, The Human Use of Human Beings: Cybernetics and Society (Houghton Mifflin, 1950).
• Stafford Beer, Designing Freedom (J. Wiley, 1974).

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).