# Would a Large Language Model Pay Extra for a View? Inferring Willingness to Pay from Subjective Choices

arXiv: https://arxiv.org/abs/2602.09802 · HTML: https://mlsanity.com/paper/2602.09802/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 18 of 18)

- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [Measuring and Improving Consistency in Pretrained Language Models](https://mlsanity.com/paper/2102.01017/) (2021)
- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://mlsanity.com/paper/2306.04528/) (2023)
- [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://mlsanity.com/paper/2402.16786/) (2024)
- [Large Language Models Assume People are More Rational than We Really are](https://mlsanity.com/paper/2406.17055/) (2024)
- [MoralBench: Moral Evaluation of LLMs](https://mlsanity.com/paper/2406.04428/) (2024)
- [Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context](https://mlsanity.com/paper/2406.05972/) (2024)
- [What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce](https://mlsanity.com/paper/2508.02630/) (2025)
- [Aligning LLMs by Predicting Preferences from User Writing Samples](https://mlsanity.com/paper/2505.23815/) (2025)
- [SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA](https://mlsanity.com/paper/2310.06675/) (2023)
- [We Need a New Ethics for a World of AI Agents](https://mlsanity.com/paper/2509.10289/) (2025)
- [ICPL: Few-shot In-context Preference Learning via LLMs](https://mlsanity.com/paper/2410.17233/) (2024)
- [The Moral Mind(s) of Large Language Models](https://mlsanity.com/paper/2412.04476/) (2024)
- [Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making](https://mlsanity.com/paper/2508.15926/) (2025)
- [Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs](https://mlsanity.com/paper/2506.02659/) (2025)
- [Cash or Comfort? How LLMs Value Your Inconvenience](https://mlsanity.com/paper/2506.17367/) (2025)

## Built on by (most cited first, 3 of 3)

- [Whose hotel does the AI recommend? An algorithm audit of reputation signals in LLM-assisted hotel selection](https://mlsanity.com/paper/2606.16344/) (2026)
- [LLM Consumer Behavior Theory: Foundations of a Novel Research Field](https://mlsanity.com/paper/2606.18005/) (2026)
- [LLM-Derived Preference Judgments Are Not Self-Consistent](https://mlsanity.com/paper/2608.17644/) (2026)

## Abstract (arXiv)

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 the economics literature. In addition to a baseline setting, we examine how model behavior changes under more realistic conditions, including the provision of information about users' past choices and persona-based prompting. Our results show that while meaningful WTP values can be derived for larger LLMs, they also display systematic deviations at the attribute level. Additionally, they tend to overestimate human WTP overall, particularly when expensive options or business-oriented personas are introduced. Conditioning models on prior preferences for cheaper options yields valuations that are closer to human benchmarks. Overall, our findings highlight both the potential and the limitations of using LLMs for subjective decision support and underscore the importance of careful model selection, prompt design, and user representation when deploying such systems in practice.