Agent-based models for the evolution of morphological alternation patterns

2026-06-10 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A multi-agent simulation models the emergence and evolution of morphological stem and inflection alternations in language, addressing phenomena like "go/went." This 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 simulates how novel forms spread through a population via agent adoption, becoming entrenched. To evaluate the realism of evolved morphologies, the study introduces the "AI Historical Linguist," a novel Large Language Model-driven system that simulates a debate between two historical linguists. Results indicate that scale-free social networks and random Bernoulli adoption of forms favor more plausible morphologies. The research also includes three case studies modeling attested historical changes.

Key takeaway

For research scientists developing agent-based models of language evolution, this work demonstrates a robust simulation framework capable of handling realistic lexical and phonological complexity. You should consider integrating LLM-driven evaluation systems, like the "AI Historical Linguist," to objectively assess the plausibility of your evolved linguistic structures. Furthermore, prioritize scale-free social networks and Bernoulli adoption policies in your model design to achieve more naturalistic morphological outcomes.

Key insights

Agent-based simulations, evaluated by an LLM-driven "AI Historical Linguist," model the emergence and spread of morphological alternations in language.

Principles

• Scale-free social networks favor plausible morphologies.
• Random Bernoulli adoption aids plausible forms.

Method

A multi-agent simulation models form adoption and spread. An LLM-driven "AI Historical Linguist" system then evaluates the realism of evolved morphologies by simulating a debate between two historical linguists.

In practice

• Model attested historical language changes.
• Simulate alternative historical linguistic paths.

Topics

• Agent-based Models
• Language Evolution
• Morphological Alternation
• Large Language Models
• Historical Linguistics
• Social Networks

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.