The Impact of LLM-Based Recommendations on Decisions under Uncertainty
Abstract: 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.
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The virtual seminar will consist of a 60-minute research paper presentation, followed by 15 minutes of Q&A and 15 minutes of discussion with students/junior researchers. During the final 15-minute discussion session, we encourage students and junior researchers to stay and interact with the speaker.