“The future is uncertain… but this uncertainty is at the heart of human creativity.“ — Ilya Prigogine.
“Information is the resolution of uncertainty.” — Claude Shannon.
Simulation (Computational Modeling) is now widely regarded as one of the three pillars of science, joining theoretical and experimental science.
How can you model the cause and solution to a social issue like poverty?
An intriguing NetLogo model of Simple Economics presents big questions in a simple form about equality, fairness, bias, wealth, and poverty. Our values, theories, and intuitions collide with the simplest example apparently showing wide disparity of wealth starting from pure equality. No bias can be identified visually or in the code to produce this outcome. Try the simulation to see for yourself: NetLogo Simple Economy Model (one outcome is shown below.
A June, 2025, report from Swiss bank UBS found about one-tenth of American adults are millionaires, with 1,000 freshly minted millionaires added daily last year. Thirty years ago, the IRS counted 1.6 million Americans with a net worth of $1 million or more. It’s estimated the U.S. had 23.8 million millionaires in the U.S. in 2024, a nearly 15-fold increase.
The expanding ranks of millionaires come as the gulf between rich and poor widens. The richest 10% of Americans hold two-thirds of household wealth (per Federal Reserve), averaging $8.1 million each. The bottom 50% hold 3% of wealth, with an average of $60,000 each. Asian people outpace white people in the U.S. in median wealth, while Black and Hispanic people trail in their net worth.
Agent-Based Models in Behavioral Research
Agent-Based Models and Generative Explanations in Complex Systems – Data Hub Tech Talk, Dr. Bert Baumgaertner (Politics & Philosophy), Univ. of Idaho, 3/2024. The first half of the talk presents an overview of Agent-Based Modeling with NetLogo. The last half show how NetLogo is used by researchers to run a scripted batch of model runs, changing critical variables, and recording the data to an exported data file (csv) for statistical analysis. This process uses NetLogo’s Behaviorspace feature, allowing you to perform hundreds or thousands of runs on the model in an automated way. After this demonstration of an automation of hundreds/thousands of “experiments,” he demonstrates how he uses ABM to organize and analyze the complex systems of changes in political opinion that he studies.
Overview of Simulation as a “Third Kind of Science” (quoted from Google’s AI)
… simulation, particularly computer simulation, acts as a “third kind of science,” complementing theoretical and experimental science. This idea stems from the increasing complexity of real-world phenomena that are difficult or impossible to study solely through experiments or analytical theory. Here are examples of simulation as a third kind of science:
1. Predicting future outcomes
- Weather Forecasting: Simulation models process vast amounts of climate data and physical equations to generate predictions about future weather conditions, including extreme events like heatwaves and storms.
- Disease Outbreak Modeling: Simulations help researchers understand how diseases might spread and inform strategies for vaccine trials and public health interventions.
- Economic Forecasting: Simulation models can project complex economic trends, helping to understand potential future scenarios, such as interest rate fluctuations or the impact of policy changes.
2. Exploring what is difficult or impossible to study directly
- Astrophysics: Scientists cannot create a supernova in a lab, but simulations allow them to study what might happen during such events and interpret astronomical data.
- Black Hole Research: Simulations can model black holes and phenomena like Hawking radiation, offering insights into physics where direct observation is currently impossible.
- Climate Change Research: Simulations help scientists investigate how changes in sea levels might affect volcanic activity over very long timescales, which is impossible to observe directly or recreate experimentally.
- Nanotechnology: Simulation allows researchers to explore phenomena and design materials at the nanoscale, where direct experimentation can be challenging.
3. Designing and optimizing systems
- Engineering Design: Simulations are crucial in designing everything from airplanes to bridges and buildings, allowing engineers to test performance under various conditions, identify potential problems, and optimize designs before physical prototypes are built.
- Manufacturing Optimization: Simulation models can be used to optimize manufacturing processes, identify bottlenecks in production lines, and improve efficiency.
- Healthcare Systems: Simulations can be used to improve hospital operations, optimize resource allocation, and even design new medical devices and procedures, according to the Mayo Clinic College of Medicine and Science.
4. Understanding complex systems
- Social Sciences: Simulation allows researchers to study complex human systems, like populations, cities, or societies, and understand how they might behave under different conditions.
- Cognitive Modeling: Simulation can be used to create models of human minds and study how religion interacts with complex human thought processes, according to a professor of philosophy, theology, and ethics at the University of Boston.
In essence
Simulation acts as a powerful tool that allows scientists and researchers to build models and recreate real-world scenarios in a controlled environment. This enables them to explore phenomena, make predictions, and test hypotheses in ways that might be impractical, dangerous, or impossible through traditional theoretical or experimental methods alone.