Naming game with learning errors in communications
Yang Lou, Guanrong Chen

TL;DR
This paper introduces a model of the naming game incorporating communication errors, analyzing how errors influence consensus, memory requirements, and convergence across different network topologies, with strategies to mitigate errors.
Contribution
It proposes the NGLE model with strategies to prevent learning errors and studies their effects on convergence and memory in various network structures.
Findings
Learning errors slightly slow convergence.
Errors increase memory requirements linearly.
A threshold error rate impairs convergence.
Abstract
Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network topology. By pair-wise iterative interactions, the population reaches a consensus state asymptotically. In this paper, we study naming game with communication errors during pair-wise conversations, where errors are represented by error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks with different parameters, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but…
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 · Evolutionary Game Theory and Cooperation
