大语言模型偏好与不完美决策的联合建模：提出偏好-行为模型 PBM
Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models
AI 导读
研究者提出偏好-行为模型（PBM），将偏好表示与不完美效用最大化机制结合，以逆博弈问题从观测选择中联合推断 LLM 的偏好和行为参数。在公共物品博弈、集体风险困境和公共池资源困境三类社会困境中的评估显示，标准行为经济学诱导工具测得的偏好不能稳定预测 LLM 策略行为，而在一个困境中拟合的模型对其他困境仍有预测力。
正文 · 原文
Abstract:Do preferences Large Language Models (LLMs) express in standard elicitation tasks predict their strategic behaviour? LLM agents interacting with humans and other AI systems face potentially conflicting objectives, such as individual welfare, group welfare, and environmental concerns. Prior work characterizes LLM preferences through behavioral-economic and psychological elicitation or strategic games, but rarely tests consistency across settings and prediction in other strategic environments. This requires modeling how competing objectives jointly shape behavior, whereas existing approaches typically measure preference dimensions in isolation. Drawing on utility theory, we introduce the Preference-Behavior Model (PBM), which combines a preference representation with mechanisms capturing imperfect utility maximization. We formulate estimation as an inverse-game problem, jointly inferring preferences and behavioral parameters from observed choices. We evaluate PBM in three social dilemmas that expose conflicting motives: a Public Goods Game, a Collective-Risk Dilemma, and a Common-Pool Resource dilemma. We find that while preferences estimated from established behavioral economic elicitation instruments do not consistently predict behavior in these dilemmas, models fitted in one dilemma retain predictive value in others, suggesting that inferred preferences capture meaningful aspects of LLM decision-making that elicitation instruments may miss. Finally, we assess alignment with persona instructions, finding that inferred preferences broadly reflect intended priorities with specific divergences across dimensions, LLMs, and games being identified by our approach. Our methodology provides a framework for studying how consistently LLM preferences explain behavior, assessing alignment with intended priorities, and revealing trade-offs and behavioral patterns that standard elicitation may miss.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT) |
| ACM classes: | I.2.0; I.2.7; I.2.11; J.4 |
| Cite as: | arXiv:2610.33651 [cs.AI] |
| (or arXiv:2610.33651v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33651 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Axel Abels [view email]
[v1]
Sat, 24 Oct 2026 13:06:54 UTC (598 KB)
来源：arXiv cs.AI · arxiv.org