Demonstrating social-scientific fidelity of generative ABMs to human actions

Show that LLM-driven generative agent-based models can reproduce human actions with sufficient fidelity to support socially scientific productivity by providing rigorous empirical evidence that agent behaviors align with human behaviors relevant to the modeled phenomena.

Background

A key claim of generative ABMs is their potential to better represent human behavior. The paper emphasizes that, despite this promise, there is no demonstrated evidence yet that such models reproduce human actions at a level that makes them productively useful for social science.

Addressing this gap requires explicit empirical tests and benchmarks that link modeled agent actions to human actions in the relevant contexts, beyond surface-level believability or stylistic similarity of outputs.