@inproceedings{feng-etal-2025-noise,
    title = "Noise, Adaptation, and Strategy: Assessing {LLM} Fidelity in Decision-Making",
    author = "Feng, Yuanjun  and
      Choudhary, Vivek  and
      Shrestha, Yash Raj",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.391/",
    pages = "7704--7717",
    ISBN = "979-8-89176-332-6",
    abstract = "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-to-round variability relative to humans, revealing a persistent alignment gap in behavioral fidelity. Future evaluations of LLM-based social simulations should prioritize process-level realism."
}