模仿行为推动语言词汇高效压缩，揭示了自然语言演化的潜在机制。
Agent-based imitation dynamics can yield efficiently compressed population-level vocabularies
- 通过模仿策略的演化博弈模型，模拟语言词汇的生成过程。
- 模型生成的词汇在信息瓶颈下实现近似最优压缩，符合真实语言特征。
- 适用于对语言演化、认知科学感兴趣的读者。
自然语言被认为在信息瓶颈（IB）的复杂性-准确性权衡压力下演化，以高效将意义压缩为词语。然而，驱动语言词汇向高效方向演化的社会动态机制仍不明确。与此同时，演化博弈论被用于解释语言从基础代理行为中涌现，但尚未验证该方法能否在信息瓶颈意义上实现高效压缩。本文提出一个统一模型，融合演化博弈论与信息瓶颈框架，证明在信号博弈中，通过独立动机的不精确策略模仿，群体层面可产生近似最优的压缩效果。研究发现，模型中的关键参数——如博弈精度及玩家混淆相似状态的倾向——会限制涌现出的词汇在权衡上的变化范围。结果表明，演化博弈动态可能为具有信息论最优和实证支持特性的词汇演化提供机制基础。
原文摘要 · Abstract (English)
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.
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