Would a Large Language Model Pay Extra for a View? Inferring Willingness to Pay from Subjective Choices
arXiv:2602.09802v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) are increasingly deployed in applications such as travel assistance and purchasing support, they are often required to make subjective choices on behalf of users in settings where no objectively correct answer exists. We study LLM decision-making in a travel-assistant context by presenting models with choice dilemmas and analyzing their responses using multinomial logit models to derive implied willingness to pay (WTP) estimates. These WTP values are subsequently compared to human benchmark values from th
The increasing deployment of LLMs in subjective decision-making roles, particularly in consumer-facing applications, necessitates understanding their economic behavior and biases.
Understanding how LLMs make subjective choices and inferring their 'willingness to pay' is crucial for their ethical deployment and for shaping future AI-driven commerce and assistance models.
This research provides a methodology to quantify LLMs' subjective economic decision-making, moving beyond objective task performance to encompass nuanced human-like preferences in AI applications.
- · AI developers
- · E-commerce platforms
- · Travel industry
- · Consumer experience designers
- · Companies relying on opaque AI decision-making
- · Undifferentiated online services
LLMs will be designed with more sophisticated and quantifiable preference models for personalized services.
The ability to quantify LLM preferences will lead to new business models centered on AI-driven recommendation and purchasing agents.
This could accelerate the integration of AI agents into personal finance and complex purchasing decisions, potentially altering consumer behavior on a macro scale.
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Read at arXiv cs.CL