Agent-based models for the evolution of morphological alternation patterns
This paper presents a multi-agent simulation explaining the emergence and persistence of morphological alternations like "go/went". Alternate forms arise from phonological changes or lexical variants and spread through population dynamics. To evaluate realism, the authors introduce the AI Historical Linguist, an LLM-driven system that simulates debates between linguists, comparing real and simulated morphologies. Results indicate scale-free networks and random Bernoulli adoption produce more plausible patterns. Three case studies model attested historical changes.
[2606.12748] Agent-based models for the evolution of morphological alternation patterns
[Submitted on 10 Jun 2026]
Title:Agent-based models for the evolution of morphological alternation patterns
View a PDF of the paper titled Agent-based models for the evolution of morphological alternation patterns, by Aravinth Kulanthaivelu and 1 other authors
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Abstract: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.
Comments: 51 + 37 pages. 31 Figures
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2606.12748 [cs.CL]
(or arXiv:2606.12748v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.12748
arXiv-issued DOI via DataCite
Submission history
From: Richard Sproat [view email] [v1] Wed, 10 Jun 2026 23:26:44 UTC (5,283 KB)
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