Pascal Institute
Research and educational content exploring forecasting, uncertainty, model evaluation, market efficiency, risk, and the difference between good decisions and good outcomes.
/PASCAL
The Pascal Initiative
BETTER DECISIONS. BETTER OUTCOMES.
Founder, The Pascal Initiative
Independent research, education, and software initiative.
A simple question
WHY PASCAL?How can we make better decisions when the outcome is uncertain?
The Pascal Initiative brings together decision science, forecasting, data engineering, education, and software development to build practical tools for evaluating evidence, measuring uncertainty, and improving the decision-making process. Sports provide the initial proving ground: frequent decisions, measurable outcomes, rich data, and markets against which judgment can be tested.
After decades of building data systems, years working with professional baseball data, and completing a master's degree in data science, I became increasingly interested in what happens after we have the data.
More information doesn't automatically produce better decisions. Models can be wrong. Good decisions can produce bad outcomes. Bad decisions can occasionally be rewarded. Pascal grew out of my desire to understand that problem better—and build tools that help people navigate it.
Four working areas
PASCAL/*AnalytIQ
A model-development and evaluation environment for building forecasts, tracking predictions, measuring performance, and comparing models over time.
Capital Management Toolkit
Decision support for translating probability estimates and expected value into disciplined capital-allocation decisions using Kelly sizing, risk limits, and performance measurement.
Forecasting Proving Ground
An evolving concept for public forecasting, model comparison, leaderboards, and experiments designed to measure how well people and models make predictions under uncertainty.
FUTURE NOTE
You don't have to outrun the bear.
Forecasting isn't necessarily about predicting reality perfectly. In many decision environments, you only need to estimate reality better than the alternative you're being offered. The hard part is knowing whether you're actually better—or randomness has simply made you look better.