Autotune (Bandits)
Introduction to multi-armed bandits in Statsig Autotune, including when to use them instead of A/B tests for explore-versus-exploit optimization problems.
Multi-Armed Bandits automatically find the best variant among a group of candidates by balancing between "exploring" options and "exploiting" the best option through dynamic traffic allocation. On Statsig, Bandits are used to select the best user experience to drive a target metric or action.
Statsig's Autotune (the Multi-Armed Bandit solution) allocates traffic towards high-performing variants and can eventually identify a winning variant.Statsig's Autotune AI (the Contextual Bandit solution) is a personalization tool that serves users the best variant determined by a machine learning model trained on previous observations.How Autotune works
Autotune is Statsig's Bayesian Multi-Armed Bandit, and Autotune AI is Statsig's Contextual Bandit.Both Autotune products test and measure different variations and their effect on a target metric.
- The multi-armed bandit continuously adjusts traffic toward the best-performing variations until it can select the best variation with confidence. The winning variation then receives 100% of traffic.
- The contextual bandit personalizes which variant a user sees based on provided user or interaction attributes, serving each user the variation predicted to be best for them.
Contextual Bandits are a subset of Multi-Armed Bandits. Both seek to balance the explore/exploit problem: choosing between exploiting the current best known solution and exploring to gather more information about other solutions.
The blog posts on Multi-Armed Bandits and Contextual Bandits cover use cases and considerations in depth. The table below summarizes the main considerations for when to use bandits, a ranking engine, or an experiment.| A/B/n Test | Multi-Armed Bandit (Autotune) | Contextual Bandit (Autotune AI) | Ranking Engine | |
|---|---|---|---|---|
| Typical # Variants | 2-3 | 4-8 | 4-8 | Arbitrary # |
| Personalization Factor | None | None | Moderate | High |
| Input Data Required | None | Very Little (100+ samples) | Little - generally 1000+ samples | Tens of thousands to millions of samples |
| Model Efficacy | None | Basic | Moderate | High |
| Identifies Best Variant | Yes | Yes | No | No |
| Consistent User Assignment | Yes | No | No | No |
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