A Bayesian Approach to the Naming Game Model

TL;DR

This paper introduces a Bayesian model for the naming game, replacing one-shot learning with a probabilistic word-learning process, to better mimic human-like cognitive and social dynamics in semiotic interactions.

Contribution

It presents a novel Bayesian framework for the naming game, integrating cognitive learning processes with social interaction dynamics.

Findings

The model shows similarities to the basic naming game but also key differences.

It provides a foundation for studying combined cognitive and social effects.

The approach models learning from few examples using Bayesian inference.

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.