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LLMs show human-like probability distortion but less loss aversion, study finds

By PulseAugur Editorial · [1 sources] ·

A meta-analysis of 102 fitted estimates from 11 studies indicates that large language models (LLMs) exhibit some, but not all, characteristics of prospect theory observed in human decision-making. While LLMs show modest loss aversion, with a pooled lambda of 1.26, they display less pronounced diminishing sensitivity to outcomes compared to humans, with value functions appearing close to linear under one method and more concave under another. However, LLMs do demonstrate a human-like inverse S-shaped probability weighting, suggesting they can distort probabilities in a manner similar to people. AI

IMPACT LLMs exhibit human-like probability distortion, suggesting potential for more nuanced AI decision-making, but their reduced loss aversion may impact risk-sensitive applications.

RANK_REASON The cluster contains an academic paper presenting research findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show human-like probability distortion but less loss aversion, study finds

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The cluster contains an academic paper presenting research findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Adi Tarabeih ·

    Prospect Theory in Large Language Models: A Meta-Analysis of Loss Aversion, Diminishing Sensitivity, and Probability Weighting

    arXiv:2610.33103v1 Announce Type: new Abstract: Prospect theory splits risky choice into three parts: loss aversion, measured by the relative weight of losses to gains, $\lambda$ (e.g., $\lambda = 2$ implies that losses weigh twice as much as gains), diminishing sensitivity to th…

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