Yuanjun Feng

Xuan Liu, HaoYang Shang, Zizhang Liu et al.

We propose using validated behavioral hypotheses as a lens for evaluating human-likeness in LLM-based agents. Our key idea is simple: If an agent is human-like, a population of such agents should reach the same inferential conclusion as the human population when run through the same experiment. Decades of social science have produced many such validated findings, each anchored to concrete experimental protocols and robustly established through independent replication. This yields an evaluation that is objective, decomposable, and scalable. We operationalize this lens through HumanStudy-Bench, an open platform that turns published human-subject studies into reusable simulation environments and administers the evaluation to configurable agents. It scores agent-human alignment on two metrics: the Probability Alignment Score (PAS) for inferential agreement and the Effect Consistency Score (ECS) for effect-size agreement. We curated an initial suite of 12 studies whose hypotheses are robustly established through independent replication, and evaluated 10 models under 4 agent designs. Results show that agent responses polarize between full replication and complete failure; agent design influences alignment more than model scale, but its effect is non-monotonic.

Yuanjun Feng, Vivek Choudhary, Yash Raj Shrestha

Large language models (LLMs) are increasingly used in social science simulations. While their performance on reasoning and optimization tasks has been extensively evaluated, less attention has been paid to their ability to simulate human decision-making's variability and adaptability. We propose a process-oriented evaluation framework with progressive interventions (Intrinsicality, Instruction, and Imitation) to examine how LLM agents adapt under different levels of external guidance and human-derived noise. We validate the framework on two classic economics tasks, irrationality in the second-price auction and decision bias in the newsvendor problem, showing behavioral gaps between LLMs and humans. We find that LLMs, by default, converge on stable and conservative strategies that diverge from observed human behaviors. Risk-framed instructions impact LLM behavior predictably but do not replicate human-like diversity. Incorporating human data through in-context learning narrows the gap but fails to reach human subjects' strategic variability. These results highlight a persistent alignment gap in behavioral fidelity and suggest that future LLM evaluations should consider more process-level realism. We present a process-oriented approach for assessing LLMs in dynamic decision-making tasks, offering guidance for their application in synthetic data for social science research.

Yuanjun Feng, Vivek Chodhary, Yash Raj Shrestha

This study examines the understudied role of algorithmic evaluation of human judgment in hybrid decision-making systems, a critical gap in management research. While extant literature focuses on human reluctance to follow algorithmic advice, we reverse the perspective by investigating how AI agents based on large language models (LLMs) assess and integrate human input. Our work addresses a pressing managerial constraint: firms barred from deploying LLMs directly due to privacy concerns can still leverage them as mediating tools (for instance, anonymized outputs or decision pipelines) to guide high-stakes choices like pricing or discounts without exposing proprietary data. Through a controlled prediction task, we analyze how an LLM-based AI agent weights human versus algorithmic predictions. We find that the AI system systematically discounts human advice, penalizing human errors more severely than algorithmic errors--a bias exacerbated when the agent's identity (human vs AI) is disclosed and the human is positioned second. These results reveal a disconnect between AI-generated trust metrics and the actual influence of human judgment, challenging assumptions about equitable human-AI collaboration. Our findings offer three key contributions. First, we identify a reverse algorithm aversion phenomenon, where AI agents undervalue human input despite comparable error rates. Second, we demonstrate how disclosure and positional bias interact to amplify this effect, with implications for system design. Third, we provide a framework for indirect LLM deployment that balances predictive power with data privacy. For practitioners, this research emphasize the need to audit AI weighting mechanisms, calibrate trust dynamics, and strategically design decision sequences in human-AI systems.