Large Language Models Enable Design of Personalized Nudges across Cultures

TL;DR

This study demonstrates that large language models can be used to design personalized behavioral nudges tailored to individual and cultural differences, effectively increasing environmentally positive actions in some countries.

Contribution

The paper introduces a novel approach using LLMs to create personalized nudges without extensive behavioral data, highlighting their potential and limitations across cultures.

Findings

LLM-informed nudges increased CO2 offsetting by 3-7% in some countries.

Cultural differences affect the effectiveness of personalized nudges.

LLMs serve as a low-cost platform for testing nudge strategies.

Abstract

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.