# Reaching Consensus via non-Bayesian Asynchronous Learning in Social Networks

arXiv: https://arxiv.org/abs/1408.5192 · HTML: https://mlsanity.com/paper/1408.5192/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2014.

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

- [The Effect of Iterativity on Adversarial Opinion Forming](https://mlsanity.com/paper/2111.15445/) (2021)

## Abstract (arXiv)

We study the outcomes of information aggregation in online social networks. Our main result is that networks with certain realistic structural properties avoid information cascades and enable a population to effectively aggregate information. In our model, each individual in a network holds a private, independent opinion about a product or idea, biased toward a ground truth. Individuals declare their opinions asynchronously, can observe the stated opinions of their neighbors, and are free to update their declarations over time. Supposing that individuals conform with the majority report of their neighbors, we ask whether the population will eventually arrive at consensus on the ground truth. We show that the answer depends on the network structure: there exist networks for which consensus is unlikely, or for which declarations converge on the incorrect opinion with positive probability. On the other hand, we prove that for networks that are sparse and expansive, the population will converge to the correct opinion with high probability.