用多智能体模拟语言中形态交替现象的演化，解释为何'go/went'这类不规则变化能长期存在。
Agent-based models for the evolution of morphological alternation patterns
- 通过智能体听觉学习和随机采纳新形式，模拟词汇交替的传播机制。
- 在真实社会网络和随机采纳条件下，生成的形态更接近实际语言结构。
- 适用于对语言演化、认知语言学或计算历史语言学感兴趣的读者。
为什么英语过去式 'go' 变成看似无关的 'went'？这类形态交替在语言中频繁出现，虽无助于交流或习得，却可延续数百年甚至数千年。本文提出一种多智能体仿真模型，模拟词干与屈折交替的起源。新形式由音变或特定人群的词汇替代产生（如 'go/went'）。当智能体听到他人使用某词的某一格式的新形式（如过去式），会以一定概率采纳，可能扩散至共享原形的其他词形。因此，替代形式可在群体中传播并固化为交替形式。系统支持数百至数千词汇条目、数十至百名智能体，以及多种网络拓扑、扩散模式与采纳策略。评估难题在于：生成的形态是否真实？为此我们引入AI历史语言学家——一个由大语言模型驱动的双语史语言学家辩论系统，对比真实语言形态、伪装形态与实验演化形态。结果表明，具有无标度社交网络和伯努利随机采纳策略时，生成形态更合理。此外，我们还进行三个案例研究，模拟已知历史变迁，探究若历史不同可能带来的后果。所有代码与数据均已公开。
原文摘要 · Abstract (English)
Why is the past of English "go" the apparently unrelated "went"? Such alternations are frequent in languages. They neither aid communication nor learnability, yet they can be persistent, surviving over centuries or millennia. We present a multi-agent simulation of the emergence of morphological stem and inflection alternations. Alternate forms arise by phonological changes or, as with "go/went", from lexical alternatives associated with a subset of the population. When an agent 'hears' another agent use a novel form for a slot in the paradigm of a word (say, the past tense of go), they will with some probability adopt that form, possibly spreading its use to other slots in the paradigm that shared the same original form. Thus alternative forms can spread through the population and become entrenched as stem or inflectional marker alternants. Unlike many previous computational studies, our system allows for naturalistic lexical forms, realistic phonological rules, lexicons with hundreds or thousands of entries, and agent populations in the tens or hundreds. It supports several network topologies, diffusion patterns and agent adoption policies. One issue with such simulations is evaluation: how realistic is the resulting morphology compared to those of real languages? We introduce the AI Historical Linguist, a novel Large Language Model-driven system that models a debate between two historical linguists. We use this to compare a set of real language morphologies, disguised morphologies, and experimentally evolved morphologies. The results suggest that among the factors that favor more plausible morphologies are scale-free social networks and random Bernoulli adoption of forms. We also present three case studies modeling attested historical changes, allowing us to test what might have happened if history had been different. All code and data are released.
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