On the Role of Memory in Robust Opinion Dynamics
Luca Becchetti, Andrea Clementi, Amos Korman, Francesco Pasquale, Luca, Trevisan, Robin Vacus

TL;DR
This paper analyzes how the lack of memory in agents affects the speed of reaching a correct consensus in opinion dynamics, showing that memory is crucial for efficient convergence.
Contribution
It demonstrates that memoryless opinion dynamics cannot achieve sub-quadratic convergence time, highlighting the importance of memory in fast consensus formation.
Findings
Memoryless dynamics have at least quadratic expected convergence time.
The voter model with no memory achieves near optimal convergence speed.
Memory is essential for rapid and robust dissemination of correct opinions.
Abstract
We investigate opinion dynamics in a fully-connected system, consisting of identical and anonymous agents, where one of the opinions (which is called correct) represents a piece of information to disseminate. In more detail, one source agent initially holds the correct opinion and remains with this opinion throughout the execution. The goal for non-source agents is to quickly agree on this correct opinion, and do that robustly, i.e., from any initial configuration. The system evolves in rounds. In each round, one agent chosen uniformly at random is activated: unless it is the source, the agent pulls the opinions of random agents and then updates its opinion according to some rule. We consider a restricted setting, in which agents have no memory and they only revise their opinions on the basis of those of the agents they currently sample. As restricted as it is, this setting…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Quantum many-body systems
