Mechanism Plausibility in Generative Agent-Based Modeling
Patrick Zhao, David Huu Pham, Nicholas Vincent

TL;DR
This paper introduces a four-level Mechanism Plausibility Scale to evaluate how well generative models, especially LLM-based agent models, can produce and explain phenomena by distinguishing capability from mechanistic plausibility.
Contribution
It operationalizes a novel plausibility scale grounded in philosophy of science to differentiate between generative sufficiency and mechanistic explanation in agent-based modeling.
Findings
Developed a four-level scale for plausibility assessment
Clarified the distinction between capability and explanation in models
Integrated philosophy of science with LLM-ABM evaluation
Abstract
Large language models (LLMs) can generate high-level diverse phenomena without explicitly programmed rules. This capability has led to their adoption within different agent-based models (ABMs) and social simulations. Recent studies investigate their ability to generate different phenomena of interest, for example, human behavior on social media platforms or alien behavior in game-theoretic scenarios. However, capability, prediction, and explanation are different--drawing from the philosophy of science and mechanisms literature, explanation requires showing, to some degree, how a phenomenon is produced by related organized entities and activities. For modelers, describing the characteristics of an experiment or whether a simulation provides progress in capability (or explanation), can be difficult without being grounded in potentially distant research areas. We integrate recent work on…
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