# Local non-Bayesian social learning with stubborn agents

## TL;DR

This paper investigates how stubborn agents spreading false information can disrupt social learning, revealing that initial correct beliefs can be overwritten over time, and proposes strategies to mitigate such influence.

## Contribution

It introduces a non-Bayesian social learning model with stubborn agents and analyzes how misinformation persists, providing new strategies to counteract fake news influence.

## Key findings

- Agents learn the true state initially but forget it over time.
- Seeding stubborn agents can effectively disrupt correct learning.
- Proposed strategies outperform heuristics in preventing misinformation.

## Abstract

We study a social learning model in which agents iteratively update their beliefs about the true state of the world using private signals and the beliefs of other agents in a non-Bayesian manner. Some agents are stubborn, meaning they attempt to convince others of an erroneous true state (modeling fake news). We show that while agents learn the true state on short timescales, they "forget" it and believe the erroneous state to be true on longer timescales. Using these results, we devise strategies for seeding stubborn agents so as to disrupt learning, which outperform intuitive heuristics and give novel insights regarding vulnerabilities in social learning.