Stability and evolution of synonyms and homonyms in signaling game

TL;DR

This paper investigates how synonyms and homonyms evolve in language using a signaling game model with reinforcement learning, revealing that positive feedback mechanisms influence their stability and decline, aligning with linguistic observations.

Contribution

It introduces a signaling game model with nonlinear reinforcement to explain the stability and evolution of synonyms and homonyms in natural language.

Findings

Synonyms are highly stable under linear reinforcement.

Homonyms decline faster with linear reinforcement.

Nonlinear reinforcement better matches linguistic data.

Abstract

Synonyms and homonyms appear in all natural languages. We analyse their evolution within the framework of the signaling game. Agents in our model use reinforcement learning, where probabilities of selection of a communicated word or of its interpretation depend on weights equal to the number of accumulated successful communications. When the probabilities increase linearly with weights, synonyms appear to be very stable and homonyms decline relatively fast. Such behaviour seems to be at odds with linguistic observations. A better agreement is obtained when probabilities increase faster than linearly with weights. Our results may suggest that a certain positive feedback, the so-called Metcalfe's Law, possibly drives some linguistic processes. Evolution of synonyms and homonyms in our model can be approximately described using a certain nonlinear urn model.