Stability and Evolution of Synonyms and Homonyms in Signaling Game

Area of Science:

• Computational Linguistics
• Evolutionary Game Theory
• Artificial Intelligence

Background:

• Synonyms and homonyms are fundamental aspects of natural language.
• Understanding their evolutionary dynamics is crucial for linguistic theory.
• Previous models may not fully capture observed linguistic patterns.

Purpose of the Study:

• To model the evolution of synonyms and homonyms using a signaling game framework.
• To investigate the impact of different learning rules on word usage dynamics.
• To explore potential drivers of linguistic evolution, such as positive feedback mechanisms.

Main Methods:

• Agent-based modeling with reinforcement learning.
• Simulation of word selection and interpretation probabilities based on communication success.
• Analysis of evolutionary trajectories under linear and nonlinear learning rules.

Main Results:

• Linear learning rules lead to overly stable synonyms and rapidly declining homonyms, contradicting linguistic observations.
• Nonlinear learning rules, where success probability increases faster than linearly with experience, yield more realistic dynamics.
• The findings suggest a potential role for Metcalfe's Law (network effects) in language evolution.

Conclusions:

• The rate of learning or reinforcement significantly impacts the stability of synonyms and homonyms.
• Nonlinear reinforcement mechanisms provide a better fit for observed linguistic patterns of word evolution.
• Metcalfe's Law may be a relevant principle driving certain aspects of natural language development.