Languages are powerful solutions to coordination problems: They provide stable, shared expectations about how the words we say correspond to the beliefs and intentions in our heads. Yet, language use in a variable and nonstationary social environment requires linguistic representations to be flexible: Old words acquire new ad hoc or partner-specific meanings on the fly. In this article, we introduce continual hierarchical adaptation through inference (CHAI), a hierarchical Bayesian theory of coordination and convention formation that aims to reconcile the long-standing tension between these two basic observations. We argue that the central computational problem of communication is not simply transmission, as in classical formulations, but continual learning and adaptation over multiple timescales. Partner-specific common ground quickly emerges from social inferences within dyadic interactions, while community-wide social conventions are stable priors that have been abstracted away from interactions with multiple partners. We present new empirical data alongside simulations showing how our model provides a computational foundation for several phenomena that have posed a challenge for previous accounts: (a) the convergence to more efficient referring expressions across repeated interaction with the same partner, (b) the gradual transfer of partner-specific common ground to strangers, and (c) the influence of communicative context on which conventions eventually form.

中文翻译：

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从合作伙伴到群体：协调和约定的分层贝叶斯帐户。

语言是协调问题的有力解决方案：它们提供了稳定的、共同的期望，即我们所说的话语如何与我们头脑中的信念和意图相对应。然而，在多变且不稳定的社会环境中使用语言需要灵活的语言表示：旧词会即时获得新的临时或特定于合作伙伴的含义。在本文中，我们介绍了通过推理进行连续分层适应（CHAI），这是一种协调和约定形成的分层贝叶斯理论，旨在调和这两个基本观察之间长期存在的紧张关系。我们认为，通信的核心计算问题不仅仅是经典公式中的传输，而是持续的学习和适应在多个时间尺度上。特定于合作伙伴的共同点很快就会从二元互动中的社会推论中产生，而社区范围内的社会惯例是从与多个合作伙伴的互动中抽象出来的稳定的先验。我们提供了新的经验数据以及模拟，展示了我们的模型如何为几种现象提供计算基础，这些现象对以前的解释提出了挑战：（a）在与同一伙伴的重复互动中收敛到更有效的指称表达，（b）逐渐将特定伙伴的共同点转移给陌生人，以及（c）最终形成约定的交流环境的影响。