Bandit Social Learning with Exploration Episodes

Kiarash Banihashem; Natalie Collina; Aleksandrs Slivkins

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arXiv · 2602.05835

Bandit Social Learning with Exploration Episodes

Kiarash Banihashem·Natalie Collina·Aleksandrs Slivkins

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Abstract

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.

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BibTeX RIS

Kiarash Banihashem, Natalie Collina, Aleksandrs Slivkins. 2026-06-09. Bandit Social Learning with Exploration Episodes. https://arxiv.org/abs/2602.05835

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