# Social learning via actions in bandit environments

arXiv: https://arxiv.org/abs/2205.06107 · HTML: https://mlsanity.com/paper/2205.06107/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 1 of 1)

- [Overcoming Free-Riding in Bandit Games](https://mlsanity.com/paper/1910.08953/) (2019)

## Abstract (arXiv)

I study a game of strategic exploration with private payoffs and public actions in a Bayesian bandit setting. In particular, I look at cascade equilibria, in which agents switch over time from the risky action to the riskless action only when they become sufficiently pessimistic. I show that these equilibria exist under some conditions and establish their salient properties. Individual exploration in these equilibria can be more or less than the single-agent level depending on whether the agents start out with a common prior or not, but the most optimistic agent always underexplores. I also show that allowing the agents to write enforceable ex-ante contracts will lead to the most ex-ante optimistic agent to buy all payoff streams, providing an explanation to the buying out of smaller start-ups by more established firms.