Frontiers in Physics (Feb 2020)
A Bayesian Approach to the Naming Game Model
Abstract
We present a novel Bayesian approach to semiotic dynamics, which is a cognitive analog of the naming game model restricted to two conventions. The model introduced in this paper provides a general framework for studying the combined effects of cognitive and social dynamics. 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 with the basic two-convention naming game model but also some relevant differences in the dynamics, which we explain through a geometric analysis of the mean-field equations.
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