Publications
Research on AI agents, backtesting and audits of market forecasts, and the platform's architecture.
Thinking in probabilities
Ignoring how often something usually happens is one of the most common forecasting mistakes. What the base rate fallacy is, the classic example, and how good forecasters avoid it.
3 min read5 sections
Thinking in probabilities
A forecast becomes useful only when you compare its probability to a price. Here is how expected value works, why positive EV is never a promise, and the calibration it depends on.
4 min read5 sections
Thinking in probabilities
A prior is your starting belief before new evidence arrives. What priors are, why they make or break forecasts, and how to build one — explained in plain English with football examples.
4 min read6 sections
Thinking in probabilities
Learn what a prior is in Bayesian forecasting, how it shapes predictions, and why it matters for AI-driven market analysis. Jargon-free explanation.
7 min read8 sections
Thinking in probabilities
Volatility and risk are not the same thing. Understanding the difference can change how you think about markets, decisions, and uncertainty itself.
8 min read8 sections
Thinking in probabilities
Learn how professional forecasters use Bayesian thinking, signal filtering, and calibration to update beliefs with new data — and why it matters in markets.
7 min read9 sections
Thinking in probabilities
Learn how to read probability distributions in markets — from bell curves to skew and fat tails — and use them to make more structured, informed decisions.
8 min read8 sections
Thinking in probabilities
Forecasting and prediction are not the same thing. Learn the critical distinctions and why they matter when evaluating AI tools in financial markets.
7 min read10 sections
Thinking in probabilities
Understand frequentist vs Bayesian probability — two frameworks for reasoning under uncertainty, and why the difference matters in AI and markets.
8 min read8 sections
Thinking in probabilities
Risk and uncertainty are not the same thing. Learn the Knightian distinction, why markets conflate them, and what it means for AI-assisted decision-making.
7 min read8 sections
Thinking in probabilities
Markets generate endless data. The challenge is knowing what matters. Signal vs noise in market data — and why transparency changes everything.
8 min read8 sections
Thinking in probabilities
Learn the most common cognitive biases that distort market decisions and how awareness combined with systematic thinking can help you act with clearer judgment.
7 min read6 sections
Thinking in probabilities
Most traders think in outcomes. The best think in probabilities. Learn the mental framework that separates disciplined decision-makers from the rest.
8 min read7 sections
