# Bandit Social Learning with Exploration Episodes

arXiv: https://arxiv.org/abs/2602.05835 · HTML: https://mlsanity.com/paper/2602.05835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Collaborating in Multi-Armed Bandits with Strategic Agents](https://mlsanity.com/paper/2605.13145/) (2026)

## Abstract (arXiv)

We study a stylized social learning dynamics where self-interested agents collectively follow a simple multi-armed bandit protocol. Each agent controls an ``episode": a short sequence of consecutive decisions. Motivating applications include users repeatedly interacting with an AI, or repeatedly shopping at a marketplace. While agents are incentivized to explore within their respective episodes, we show that the aggregate exploration fails: e.g., its Bayesian regret grows linearly over time. In fact, such failure is a (very) typical case, not just a worst-case scenario. This conclusion persists even if an agent's per-episode utility is some fixed function of the per-round outcomes: e.g., min or max, not just the sum. Thus, externally driven exploration is needed even when some amount of exploration happens organically.