The Classical Multi-Armed Bandit Problem
This second article in the Adaptive Intelligence series examines the classical Multi-Armed Bandit problem, the simplest mathematical model of learning through repeated action, feedback, and uncertainty. Building on the previous discussion of the 37% Rule and the exploration–exploitation dilemma, the article explains how an agent must choose among uncertain options, estimate their value, and balance immediate reward against the need for information. It introduces the casino analogy, the basic mathematical structure of bandits, expected reward, regret, and early strategies such as random exploration, greedy exploitation, epsilon-greedy, optimistic initialization, Upper Confidence Bound, and Thompson Sampling. The central argument is that intelligent systems do not merely use data — they create data through action, paying the price of learning before they can make better decisions.
