# Emergent Linguistic Phenomena in Multi-Agent Communication Games

## TL;DR

This paper introduces a computational framework where agents playing referential communication games develop linguistic phenomena similar to natural language, showing language evolution can emerge from simple social interactions without complex innate capabilities.

## Contribution

The study demonstrates that complex linguistic phenomena can emerge from basic social exchanges in multi-agent communication games using deep reinforcement learning.

## Key findings

- Contact outcomes depend on community connectivity.
- Linguistic contact can lead to creole languages or convergence.
- A linguistic continuum with varying mutual intelligibility emerges.

## Abstract

In this work, we propose a computational framework in which agents equipped with communication capabilities simultaneously play a series of referential games, where agents are trained using deep reinforcement learning. We demonstrate that the framework mirrors linguistic phenomena observed in natural language: i) the outcome of contact between communities is a function of inter- and intra-group connectivity; ii) linguistic contact either converges to the majority protocol, or in balanced cases leads to novel creole languages of lower complexity; and iii) a linguistic continuum emerges where neighboring languages are more mutually intelligible than farther removed languages. We conclude that intricate properties of language evolution need not depend on complex evolved linguistic capabilities, but can emerge from simple social exchanges between perceptually-enabled agents playing communication games.