{"slug": "agent-based-models-for-the-evolution-of-morphological-alternation-patterns", "title": "Agent-based models for the evolution of morphological alternation patterns", "summary": "A multi-agent simulation reveals how morphological alternations like English \"go/went\" emerge and persist through population dynamics, with agents adopting novel forms from others and spreading them across word paradigms. The study introduces the AI Historical Linguist, a Large Language Model system that evaluates the realism of evolved morphologies against real languages, finding that scale-free social networks and random adoption patterns produce more plausible results. Three case studies modeling attested historical changes allow researchers to test alternative historical scenarios, with all code and data publicly released.", "body_md": "arXiv:2606.12748v1 Announce Type: new\nAbstract: 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.\nWe 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.\nOne 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.\nWe also present three case studies modeling attested historical changes, allowing us to test what might have happened if history had been different.\nAll code and data are released.", "url": "https://wpnews.pro/news/agent-based-models-for-the-evolution-of-morphological-alternation-patterns", "canonical_source": "https://arxiv.org/abs/2606.12748", "published_at": "2026-06-12 04:00:00+00:00", "updated_at": "2026-06-12 04:55:30.687764+00:00", "lang": "en", "topics": ["ai-research", "natural-language-processing", "large-language-models", "artificial-intelligence"], "entities": ["AI Historical Linguist"], "alternates": {"html": "https://wpnews.pro/news/agent-based-models-for-the-evolution-of-morphological-alternation-patterns", "markdown": "https://wpnews.pro/news/agent-based-models-for-the-evolution-of-morphological-alternation-patterns.md", "text": "https://wpnews.pro/news/agent-based-models-for-the-evolution-of-morphological-alternation-patterns.txt", "jsonld": "https://wpnews.pro/news/agent-based-models-for-the-evolution-of-morphological-alternation-patterns.jsonld"}}