Explore
These are the simulations built for the essays, collected so they can be reached directly. Each one states the question it was built to answer, an experiment worth running, and what it assumes. They are models, not measurements: every one generates its own data from parameters you set, and none of them reads a market. Where that distinction matters most, the page says so.
Feedback and Cascades
Systems where an event makes the next event more likely: self-organized criticality, self-excitation, herding, forced selling, and the multiplier on flow.
Chaos and Fixed Points
What iteration does: convergence, stability and its loss, period doubling, bounded chaos, self-similar sets, and the fixed points behind self-reference.
Tails and Uncertainty
What you can and cannot know from data: recognizing fat tails, when averages stop converging, time versus ensemble averages, and two early-warning signals with honest limits.