Econ Papers Daily / 期刊论文
基于大语言模型的推荐对不确定性下决策的影响
The impact of LLM-based recommendations on decisions under uncertainty
摘要
We study how large language model (LLM) based recommendations affect evaluations of uncertainty. We elicit certainty equivalents for (i) lotteries that share the same reduced-form winning probability but vary in cognitive demands because they are compound, and (ii) an Ellsberg-style ambiguous lottery. Across all lottery types, valuations from participants who receive LLM-based recommendations before each valuation exhibit second-order stochastic dominance relative to valuations from participants who do not. Hence, LLM-based recommendations leave mean valuations unchanged but compress the distribution, reducing the incidence of extreme valuations. Consistent with this pattern, LLM-based recommendations reduce the intensity of risk, complexity, and ambiguity aversion, while leaving the fraction of participants classified as averse unchanged.
中文摘要
我们研究基于大语言模型（LLM）的推荐如何影响对不确定性的评估。我们引出以下两类彩票的确定性等值：（i）具有相同简化形式中奖概率、但因复合性而在认知需求上有所不同的彩票，以及（ii）一个埃尔斯伯格风格模糊彩票。在所有彩票类型中，与未接受LLM推荐参与者的估值相比，在每次估值前接受基于LLM推荐的参与者的估值呈现二阶随机占优。因此，基于LLM的推荐使平均估值保持不变，但压缩了分布，降低了极端估值的发生率。与这一模式一致，基于LLM的推荐降低了风险厌恶、复杂性厌恶和模糊厌恶的强度，同时使被归类为厌恶的参与者比例保持不变。