用大模型定制跨文化环保助推策略，提升航空碳抵消率3-7%。
Large Language Models Enable Design of Personalized Nudges across Cultures
- 用大模型根据个人和文化特征生成个性化助推方案。
- 在德、新、美三国使碳抵消率提升3-7%，中印无显著效果。
- 低成本模拟助推策略，适合行为干预研究者快速验证想法。
助推策略能有效影响行为，但其效果依赖个体偏好。针对不同个体，同一策略可能适得其反。我们假设大语言模型（LLMs）可在无需昂贵行为数据收集与建模的前提下，实现个性化助推设计。为此，我们利用LLM设计基于备选诱饵的个性化助推方案，针对不同个人画像与文化背景，旨在鼓励航空旅客自愿抵消飞行产生的二氧化碳排放。通过在五个国家开展的大规模问卷实验（n=3495）评估其有效性。结果表明，由LLM指导的个性化助推比统一设置更有效，在德国、新加坡和美国将抵消率提高3-7%，但在中国和印度未见显著提升。本研究凸显了LLM作为低成本试点平台在助推策略开发中的潜力。同时，文化异质性限制了其普适性，强调需结合LLM模拟与针对性实证验证。
原文摘要 · Abstract (English)
Nudge strategies are effective tools for influencing behaviour, but their impact depends on individual preferences. Strategies that work for some individuals may be counterproductive for others. We hypothesize that large language models (LLMs) can facilitate the design of individual-specific nudges without the need for costly and time-intensive behavioural data collection and modelling. To test this, we use LLMs to design personalized decoy-based nudges tailored to individual profiles and cultural contexts, aimed at encouraging air travellers to voluntarily offset CO$_2$ emissions from flights. We evaluate their effectiveness through a large-scale survey experiment ($n=3495$) conducted across five countries. Results show that LLM-informed personalized nudges are more effective than uniform settings, raising offsetting rates by 3-7$\%$ in Germany, Singapore, and the US, though not in China or India. Our study highlights the potential of LLM as a low-cost testbed for piloting nudge strategies. At the same time, cultural heterogeneity constrains their generalizability underscoring the need for combining LLM-based simulations with targeted empirical validation.
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