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.

翻译：助推策略是影响行为的有效工具，但其效果取决于个体偏好。对某些个体有效的策略可能对其他人产生反作用。我们假设大型语言模型（LLMs）能够促进个体特异性助推策略的设计，而无需进行成本高昂且耗时的行为数据收集与建模。为验证此假设，我们利用LLMs设计基于诱饵选项的个性化助推策略，这些策略根据个体特征与文化背景定制，旨在鼓励航空旅客自愿抵消航班产生的CO$_2$排放。我们通过在五个国家开展的大规模调查实验（$n=3495$）评估其有效性。结果表明，基于LLM的个性化助推策略比统一设置更为有效，在德国、新加坡和美国将抵消率提升了3-7$\%$，但在中国和印度未观察到显著效果。我们的研究凸显了LLM作为低成本试验平台用于助推策略初步测试的潜力。同时，文化异质性限制了其普适性，这强调了需要将基于LLM的模拟与有针对性的实证验证相结合。