Modelling & Simulation

Watch

• What does three degrees of global warming look like? | The Economist ↗The Economist
• Differential equations, a tourist's guide | DE1 ↗3Blue1Brown
• The Map of Particle Physics | The Standard Model Explained ↗Domain of Science

Read

• Thinking in Systems: A Primer ↗Essential reading for grasping the fundamental principles of complex systems and how to model their behaviour, offering clarity to an often opaque discipline.Donella H. Meadows
• Simulation Modeling and Analysis ↗The authoritative textbook for anyone seeking a rigorous understanding of discrete-event simulation, from model construction to statistical analysis of results.Averill M. Law
• The Art of Computer Programming, Vol. 2: Seminumerical Algorithms ↗A foundational text for the mathematically inclined, dissecting the algorithms underpinning random number generation and other numerical methods critical to robust simulation.Donald Knuth
• The Monte Carlo MethodThe seminal paper that introduced the revolutionary Monte Carlo method, demonstrating how random sampling could solve complex deterministic and stochastic problems.Nicholas Metropolis and Stanislaw Ulam
• Discrete-Event System Simulation ↗An accessible yet comprehensive guide to the principles and practice of discrete-event simulation, indispensable for practitioners building and analyzing models.Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol

Voices to follow

• Steven Strogatz ↗His lucid explanations and foundational research in nonlinear dynamics and complex systems illuminate how seemingly simple rules can generate intricate, emergent behaviours in both natural and engineered worlds.Professor of Applied Mathematics, Cornell University
• Melanie Mitchell ↗A leading interpreter of complexity science, she explores the fundamental principles governing intelligent and adaptive systems, often through computational experiments, bridging AI and the study of emergent phenomena.Professor of Computer Science, Portland State University and Santa Fe Institute
• Scott E. Page ↗His rigorous application of computational models to social science reveals how diversity and interaction shape collective outcomes, offering critical insights for understanding complex human systems.John Seely Brown Distinguished University Professor of Complexity, Social Science, and Management, University of Michigan
• Jennifer Tour Chayes ↗Her mathematical prowess in graph theory and algorithms provides the theoretical bedrock for understanding the limits and capabilities of large-scale computational models, from network analysis to machine learning.Associate Provost and Dean of the College of Computing, Data Science, and Society, University of California, Berkeley

Glossary

• DataThe facts, numbers, or information used to build, run, or check the accuracy of a model. It's the raw material for your model. For example, to create a model of student performance, you would use data like past test scores, homework completion rates, and attendance records.
• InputThe information, conditions, or actions you put into a model to start a simulation or influence its behavior. It's what you feed the model. For example, when you use a weather model, the current temperature, humidity, and wind speed are inputs.
• ModelA simplified version of a real thing or process, like a toy car is a model of a real car. It helps us understand how the real thing works without needing to use the actual thing. For example, a miniature globe is a model of the Earth, showing continents and oceans in a smaller, easier-to-study form.
• OutputThe results, data, or information that the model produces after a simulation. It's what you get back from the model after giving it inputs. For example, if you input your grades and attendance into a model that predicts your final score, the predicted final score is the output.
• ParameterA fixed value or setting in a model that usually doesn't change during a single simulation. It describes a constant characteristic of the system. For example, in a model of a car's speed, the car's weight or engine size would be parameters, while the driver pressing the accelerator would be a variable.
• PredictionWhat a model suggests will happen in the future or under certain conditions, based on the rules and data it uses. It's an educated guess made by the model. For example, a climate model might make a prediction about how much the average global temperature will rise in the next 50 years.
• ScenarioA specific set of conditions or a particular situation that you test using your model. You might run a simulation multiple times with different scenarios to see various outcomes. For example, you could test a traffic model with a "rush hour scenario" or a "school holiday scenario" to see how traffic flows differently.
• SimulationThe act of running a model to see how something might behave or change over time. It's like playing a video game where you control events to see different outcomes. For example, a flight simulator lets pilots practice flying a plane without actually leaving the ground, showing them how the plane reacts to different controls.
• SystemThe actual thing or process from the real world that you are trying to understand or represent with a model. It could be anything from a single machine to an entire ecosystem. For example, your school's lunch line, with students, servers, and food, is a system that can be modeled to make it faster.
• ValidationThe process of checking if your model accurately represents the real world and produces reliable results. It's like comparing your model's predictions to what actually happens. For example, after building a model of how a new game will sell, you'd validate it by comparing its sales predictions to actual sales figures once the game is released.
• VariableA part of a system or model that can change or be changed. These are the things you might adjust to see how they affect the outcome. For example, in a model of a plant's growth, the amount of water or sunlight it receives would be variables.

Threads 10

Where this connects to other fields, and why it's worth knowing.

• Public Policy Political Science

  Build more lanes to fix traffic and, weirdly, traffic often gets worse, because more road tempts more people to drive. Systems with delays and feedback loops love to backfire on the 'obvious' fix. That's exactly why governments build computer simulations of these systems: plain common sense keeps guessing wrong when everything loops back on itself.

• The Russia-Ukraine War Political Science

  With modern guns that shoot from a distance, an army's strength grows with the square of its size, so double your troops and you're roughly four times as strong. That's why 5 soldiers against 3 isn't a close fight; the bigger side wins and barely loses anyone. This is Lanchester's square law: piling up numbers beats raw bravery, mathematically.

• Ecosystem Tipping Points Environment

  A clear lake can suddenly turn into a green algae swamp overnight, not slowly. Weirdly, the exact same equation describes a brain sliding into a seizure and a stock market crashing in a panic. It turns out one small piece of math, called a 'fold,' decides when any complicated system suddenly snaps instead of gently bending.

• Climate Zones and Weather Systems Geography

  A scientist named Lorenz found the weather equations follow exact rules, yet you still can't predict past about two weeks. That's the 'butterfly effect,' and here's the twist: it isn't caused by missing data or weak computers. It's a hard wall built into the math itself, one no machine will ever break through.

• Migration & Diaspora Sociology

  How many people move between two cities follows a formula copied straight from Newton's gravity. More people in each city pulls harder, and more distance between them pushes flow way down. So crowds of humans drift toward each other almost like planets tugged by gravity, big masses pulling, distance weakening the pull.

• Elections & Voting Political Science

  When a famous poll predicts an election, people read it and change how they vote, which wrecks the very prediction. It's a weird loop: a model of people that gets popular starts to break itself. The closer everyone watches the forecast, the less likely it is to come true.

• The Population Question Sociology

  A country's population can keep growing for decades even after families start having fewer kids than needed to replace themselves. All the young people already born keep having babies, so the slowdown is hidden in the pipeline, and governments always notice the turn a generation too late.

• Environmental and Climate Law Law

  A climate lawsuit can hang on one question: will the judge accept a computer simulation as real evidence? You're asking a courtroom to treat a model's prediction about the future as proof of a harm that hasn't fully happened yet.

• Mechanical Engineering Technology

  Mechanical engineers simulate a design before building it, then compare predictions with prototype measurements.

  Sources: U.S. Bureau of Labor Statistics — Mechanical Engineers ↗ · ABET engineering program criteria 2025–2026 ↗

• Mechatronics Technology

  Mechatronic design uses models of interacting mechanical and electronic components to predict system response.

  Sources: NPTEL — Mechatronics ↗ · ABET engineering program criteria 2025–2026 ↗