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This paper investigates how efficient language compression, as defined by the Information Bottleneck (IB) framework, can emerge from agent-level dynamics. They develop a unified model integrating evolutionary game theory with the IB framework, using imprecise strategy imitation in signaling games. The results show that near-optimal compression arises in a population, driven by parameters regulating precision and state confusion, suggesting a mechanistic basis for the evolution of efficient vocabularies.
Evolutionary game dynamics, specifically imprecise strategy imitation in signaling games, can drive the emergence of vocabularies with near-optimal information-theoretic compression, providing a potential mechanism for efficient language evolution.
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.