A Bayesian Approach to the Naming Game Model
Abstract
We present a novel Bayesian approach to semiotic dynamics, which is a cognitive analogue of the naming game model restricted to two conventions. The one-shot learning that characterizes the agent dynamics in the basic naming game is replaced by a word-learning process, in which agents learn a new word by generalizing from the evidence garnered through pairwise-interactions with other agents. The principle underlying the model is that agents, like humans, can learn from a few positive examples and that such a process is modeled in a Bayesian probabilistic framework. We show that the model presents some analogies but also crucial differences with respect to the dynamics of the basic two-convention naming game model. The model introduced aims at providing a starting point for the construction of a general framework for studying the combined effects of cognitive and social dynamics.
Cite
@article{arxiv.1911.13012,
title = {A Bayesian Approach to the Naming Game Model},
author = {Gionni Marchetti and Marco Patriarca and Els Heinsalu},
journal= {arXiv preprint arXiv:1911.13012},
year = {2020}
}
Comments
16 pages, 10 figures. Presented at the Conference on Complex Systems 2018, September 22-28, Thessaloniki, Greece, with the title "Statistical Learning Approach To Naming Game Dynamics In Complex Networks"