What are multi-armed bandits and where are they used instead of A/B tests?

Multi-armed bandits are an algorithmic model for solving the problem of choosing the optimal option among several, balancing exploration and exploitation. Unlike traditional A/B testing, where options are tested in parallel and results are analyzed after a fixed period, multi-armed bandits dynamically reallocate traffic in favor of more successful options during the experiment.

Where to use instead of A/B tests:

• When it is important to quickly minimize losses from ineffective options (e.g., in advertising or recommendations).
• When traffic is limited and it is necessary to find the best option faster.
• In online systems with constantly changing conditions, where real-time adaptation is needed.

Thus, multi-armed bandits allow for more efficient management of experiments, especially under conditions of limited time and resources.