Agent-based imitation dynamics can yield efficiently compressed population-level vocabularies

TL;DR

This paper demonstrates how agent-based imitation dynamics within evolutionary game theory can lead to the emergence of population vocabularies that efficiently compress meanings, aligning with the Information Bottleneck principle.

Contribution

It introduces a unified model combining evolutionary game theory and the IB framework, showing how near-optimal vocabulary compression can emerge from simple imitation dynamics.

Findings

Population vocabularies achieve near-optimal IB compression.

Key parameters influence the tradeoff between complexity and accuracy.

Imitative dynamics constrain vocabulary variation toward efficiency.

Abstract

Natural languages have been argued to evolve under pressure to efficiently compress meanings into words by optimizing the Information Bottleneck (IB) complexity-accuracy tradeoff. However, the underlying social dynamics that could drive the optimization of a language's vocabulary towards efficiency remain largely unknown. In parallel, evolutionary game theory has been invoked to explain the emergence of language from rudimentary agent-level dynamics, but it has not yet been tested whether such an approach can lead to efficient compression in the IB sense. Here, we provide a unified model integrating evolutionary game theory with the IB framework and show how near-optimal compression can arise in a population through an independently motivated dynamic of imprecise strategy imitation in signaling games. We find that key parameters of the model -- namely, those that regulate precision in these games, as well as players' tendency to confuse similar states -- lead to constrained variation of the tradeoffs achieved by emergent vocabularies. Our results suggest that evolutionary game dynamics could potentially provide a mechanistic basis for the evolution of vocabularies with information-theoretically optimal and empirically attested properties.