Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making

Large language models (LLMs) are increasingly used for social-science simulations, yet most evaluations target task optimality rather than the variability and adaptation characteristic of human decision-making. We propose a process-oriented evaluation framework with progressive interventions (Intrinsicality, Instruction, and Imitation), and apply it to two classic economics tasks: the second-price auction and the newsvendor inventory problem. By default, LLMs adopt stable, conservative strategies that diverge from observed human behavior. Giving LLMs risk-framed instructions makes them behave more like humans. However, this also causes complex irregularities. Incorporating human decision trajectories via in-context learning further narrows distributional gaps, indicating that models can absorb human patterns. However, across all interventions, LLMs underexpress round-toround variability relative to humans, revealing a persistent alignment gap in behavioral fidelity. Future evaluations of LLM-based social simulations should prioritize processlevel realism. Our code and data are available here: https://github.com/diana3135/ LLM-Fidelity-in-Decision-Making .