# Observational Learning with a Budget

arXiv: https://arxiv.org/abs/2504.19396 · HTML: https://mlsanity.com/paper/2504.19396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Social Learning with Endogenous Network Formation](https://mlsanity.com/paper/1504.05222/) (2015)
- [How fragile are information cascades?](https://mlsanity.com/paper/1711.04024/) (2017)
- [Social Learning with Questions](https://mlsanity.com/paper/1811.00226/) (2018)
- [Impact of Fake Agents on Information Cascades](https://mlsanity.com/paper/2005.05518/) (2020)

## Abstract (arXiv)

We consider a model of Bayesian observational learning in which a sequence of agents receives a private signal about an underlying binary state of the world. Each agent makes a decision based on its own signal and its observations of previous agents. A central planner seeks to improve the accuracy of these signals by allocating a limited budget to enhance signal quality across agents. We formulate and analyze the budget allocation problem and propose two optimal allocation strategies. At least one of these strategies is shown to maximize the probability of achieving a correct information cascade.