用信息论分析亲属称谓系统，发现最优平衡可自然演化
On the Optimality of Kinship Naming: an Information-theoretic Approach
- 构建参照游戏框架，模拟语言演化中的命名优化
- 实证显示不同语言中称谓系统均逼近最优权衡
- 适合对语言演化、认知科学感兴趣的读者
自然语言中的命名系统本质上是在高信息量与低复杂度之间权衡。聚焦亲属称谓领域，本文突破以往研究的简化假设——即所有语言具有相同的沟通需求以及理想听者——通过收集四种语言的数据，分析不同沟通需求和听者模型如何影响这一权衡。采用源自涌现通信的参照游戏设定，进一步表明该权衡的最优性不仅理论上可达成，且在学习到的通信系统中也真实出现。
原文摘要 · Abstract (English)
The structure of naming systems in natural languages hinges on a trade-off between high informativeness and low complexity. Focusing on the domain of kinship naming, we analyze such trade-off while addressing simplifying assumptions of prior work, namely: (i) universal communicative need across languages, and (ii) optimal listeners. To that aim, we collect data from four different languages, and analyze how different communicative needs and variations in the listener model influence the informativeness--complexity trade-off. Adopting a referential game setup from emergent communication, we further show that trade-off optimality is not only theoretically achievable but also emerges empirically in learned communication systems.
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