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

arXiv: https://arxiv.org/abs/2603.15903 · HTML: https://mlsanity.com/paper/2603.15903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 71 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [The Consensus Game: Language Model Generation via Equilibrium Search](https://mlsanity.com/paper/2310.09139/) (2023)
- [Semantic categories of artifacts and animals reflect efficient coding](https://mlsanity.com/paper/1905.04562/) (2019)
- [Cultural evolution via iterated learning and communication explains efficient color naming systems](https://mlsanity.com/paper/2305.10154/) (2023)
- [Learning Approximate and Exact Numeral Systems via Reinforcement Learning](https://mlsanity.com/paper/2105.13857/) (2021)
- [The Emergence of Grammar through Reinforcement Learning](https://mlsanity.com/paper/2503.01635/) (2025)

## Abstract (arXiv)

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.