Multi-Armed Bandit Problem

The classic explore-exploit problem: you face multiple slot machines ('arms') with unknown payoff rates. Each pull gives you information about one machine's payoff but costs you the opportunity to pull a potentially better machine. The fundamental tension: explore (try different machines to learn their rates) or exploit (keep pulling the machine that's worked best so far). Too much exploration wastes resources on inferior options. Too little exploration risks missing the best option. Every resource allocation decision—hiring approaches, marketing channels, product features, investment strategies—is a multi-armed bandit problem.

When to use it

When allocating resources across options with uncertain returns; when deciding how much to invest in proven approaches versus experimental ones; when designing experimentation programs; when the tension between exploration and exploitation needs a structured resolution.

How it can help

Apply bandit thinking to resource allocation under uncertainty. The epsilon-greedy approach: spend 80-90% of resources on your best-known approach and 10-20% exploring alternatives. As you gather data, shift exploration budget toward newly discovered winners. For marketing: test multiple channels, concentrate on winners, keep testing. For hiring: try diverse recruiting approaches, measure results, concentrate on top performers.