# 圣彼得堡博弈揭示LLM风险决策的表面行为对齐与机制差异

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-03 08:00
- AIHOT 分数：66
- AIHOT 链接：https://aihot.virxact.com/items/cmpzq5l0b05wgslkpmbvmfu7b
- 原文链接：https://arxiv.org/abs/2606.04978

## AI 摘要

以圣彼得堡博弈为平台，评估28个大语言模型。多数模型在原始博弈中生成有限出价，看似与人类风险行为相似；但通过扰动截断、重复玩法、财富量、职业身份等控制变体发现，模型转向条件性和计算合理性行为，人类提示词和指令微调仅降低出价而未改变机制层面响应模式。结果表明风险决策中的行为对齐可能停留在表面。

## 正文

LLMs can appear cautious in risk decision-making tasks, yet cautious-looking outputs do not necessarily indicate alignment with human decision-making mechanisms. We investigate this distinction using the St. Petersburg game as a controlled testbed, a classical paradox in which the expected payoff is infinite, yet humans typically report low, finite willingness to pay. We evaluate 28 LLMs with a structured prompt suite that includes the original game; controlled decision variants that perturb truncation, repeated play, numeric endowment, and occupational identity; a human-perspective prompt that asks models to reason as human decision makers; and paired comparisons between base models and their instruction-tuned counterparts. In the original game, most models generate finite bids, creating the appearance of human-like risk behavior. However, this outcome-level resemblance masks substantial mechanism-level differences. The controlled variants reveal that rather than maintaining human-like behavior seen in the original game, models often shift to conditionally and computationally rational behavior. Human-cue prompting and instruction tuning often lower bids and reduce some visible pathologies, but most mechanism-level response patterns remain largely unchanged. These findings show that behavioral alignment in risk decision-making can be surface-level: LLMs may produce human-like risk decisions without exhibiting human-consistent mechanisms. High-stakes evaluations of LLM decision-making should therefore move beyond outcome similarity and examine whether the alignment is supported by mechanism-level consistency.