大型语言模型助力跨文化个性化助推设计
Large Language Models Enable Design of Personalized Nudges across Cultures
AI总结：
本研究利用大型语言模型生成个性化诱饵式助推，通过五国大规模实验证明其在德、新、美三国可提升碳抵消率3-7%，但受文化异质性限制，需结合实证验证。
摘要：
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。对某些人有效的策略可能对另一些人适得其反。我们假设，大型语言模型（LLM）能够在无需昂贵且耗时的行为数据收集和建模的情况下，促进个体特定助推的设计。为验证这一假设，我们使用LLM设计针对个体特征和文化背景的个性化诱饵式助推，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，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，抵消率提高了3-7$\%$，但在中国或印度则不然。我们的研究凸显了LLM作为低成本试验平台用于试点助推策略的潜力。同时，文化异质性限制了其普适性，强调了将基于LLM的模拟与有针对性的实证验证相结合的必要性。
英文摘要：
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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