Automata networks model for alignment and least effort on vocabulary formation

TL;DR

This paper introduces an Automata Networks model to study vocabulary formation in artificial communities, analyzing how alignment and effort minimization strategies influence the emergence of shared language, with simulations indicating effort minimization leads to better language sharing.

Contribution

It presents a novel Automata Networks model incorporating effort strategies for vocabulary formation, extending previous linguistic convention models like the Naming Game.

Findings

Effort minimization strategy enhances shared vocabulary formation.

One-dimensional lattices favor effort-minimizing strategies.

Simulations show local agreement correlates with language convergence.

Abstract

Can artificial communities of agents develop language with scaling relations close to the Zipf law? As a preliminary answer to this question, we propose an Automata Networks model of the formation of a vocabulary on a population of individuals, under two in principle opposite strategies: the alignment and the least effort principle. Within the previous account to the emergence of linguistic conventions (specially, the Naming Game), we focus on modeling speaker and hearer efforts as actions over their vocabularies and we study the impact of these actions on the formation of a shared language. The numerical simulations are essentially based on an energy function, that measures the amount of local agreement between the vocabularies. The results suggests that on one dimensional lattices the best strategy to the formation of shared languages is the one that minimizes the efforts of speakers on communicative tasks.