Modeling Emergent Lexicon Formation with a Self-Reinforcing Stochastic Process

TL;DR

This paper introduces FiLex, a self-reinforcing stochastic model that explains and predicts how finite lexicons form and evolve in emergent language systems, linking usage frequency to entropy.

Contribution

The paper presents FiLex, a novel theoretical model for emergent lexicon formation that captures self-reinforcing dynamics and links hyperparameters to language entropy.

Findings

FiLex accurately models the relationship between hyperparameters and Shannon entropy.

Empirical tests validate FiLex's ability to predict emergent language behavior.

The model provides insights into the dynamics of lexicon growth and stabilization.

Abstract

We introduce FiLex, a self-reinforcing stochastic process which models finite lexicons in emergent language experiments. The central property of FiLex is that it is a self-reinforcing process, parallel to the intuition that the more a word is used in a language, the more its use will continue. As a theoretical model, FiLex serves as a way to both explain and predict the behavior of the emergent language system. We empirically test FiLex's ability to capture the relationship between the emergent language's hyperparameters and the lexicon's Shannon entropy.