# Stabilizing Consensus with Many Opinions

arXiv: https://arxiv.org/abs/1508.06782 · HTML: https://mlsanity.com/paper/1508.06782/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2015.

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

- [Beyond Fixed Points: Superpolynomial Capacity of Asymmetric Hopfield Networks](https://mlsanity.com/paper/2605.24611/) (2026)

## Abstract (arXiv)

We consider the following distributed consensus problem: Each node in a complete communication network of size n initially holds an opinion, which is chosen arbitrarily from a finite set Sigma. The system must converge toward a consensus state in which all, or almost all nodes, hold the same opinion. Moreover, this opinion should be valid, i.e., it should be one among those initially present in the system. This condition should be met even in the presence of an adaptive, malicious adversary who can modify the opinions of a bounded number of nodes in every round. We consider the 3-majority dynamics: At every round, every node pulls the opinion from three random neighbors and sets his new opinion to the majority one (ties are broken arbitrarily). Let k be the number of valid opinions. We show that, if k leqslant nα, where α is a suitable positive constant, the 3-majority dynamics converges in time polynomial in k and log n with high probability even in the presence of an adversary who can affect up to o(√n) nodes at each round. Previously, the convergence of the 3-majority protocol was known for |Sigma| = 2 only, with an argument that is robust to adversarial errors. On the other hand, no anonymous, uniform-gossip protocol that is robust to adversarial errors was known for |Sigma| > 2.