Probabilities at Work: Decision-Making under Uncertainty

Most real decisions are made without certainty. Probabilities at Work is a machine learning book devoted to understanding how probabilistic thinking improves decisions when outcomes are uncertain and information is incomplete.
The writing explores how machine learning produces likelihoods rather than guarantees—and why this is a strength, not a limitation. Readers learn how probabilities guide risk assessment, prioritization, and trade-offs across business, policy, and everyday systems.
Rather than treating probability as abstract math, the book grounds it in decision contexts. It shows how thresholds are chosen, how confidence affects action, and how misunderstanding uncertainty leads to costly mistakes. Each chapter emphasizes that good decisions depend on interpreting probabilities correctly, not eliminating uncertainty.
The tone is analytical yet practical, designed to build intuition rather than fear of ambiguity. Language remains accessible and precise, helping readers reason clearly under uncertainty.
Probabilities at Work moves through risk modeling, Bayesian thinking, prediction confidence, decision thresholds, and uncertainty communication—revealing how probabilistic systems support better judgment.
Key themes explored include:
• Decision-making under uncertainty
• Probabilistic reasoning
• Risk and confidence
• Interpreting ML outputs
• Better choices through likelihood
Probabilities at Work is for readers who make decisions with imperfect information—offering tools to act wisely without certainty.