# A Global View of How LLMs Value Human Time

arXiv: https://arxiv.org/abs/2610.18515 · HTML: https://mlsanity.com/paper/2610.18515/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 7 of 7)

- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [Large Language Models Reflect the Ideology of their Creators](https://mlsanity.com/paper/2410.18417/) (2024)
- [Large Language Models Assume People are More Rational than We Really are](https://mlsanity.com/paper/2406.17055/) (2024)
- [EconEvals: Benchmarks and Litmus Tests for Economic Decision-Making by LLM Agents](https://mlsanity.com/paper/2503.18825/) (2025)
- [One world, one opinion? The superstar effect in LLM responses](https://mlsanity.com/paper/2412.10281/) (2024)
- [Temporal Preferences in Language Models for Long-Horizon Assistance](https://mlsanity.com/paper/2509.09704/) (2025)

## Abstract (arXiv)

Large language models (LLMs) increasingly act as personal assistants; however, little is known about how they capture national-level preferences on a global scale. Given that "time" is a universal and representative concept, we examine LLMs' time preferences by varying only users' country and measuring the compensation LLMs deem appropriate for a 60-minute appointment delay. We then compare LLMs' responses with GDP per capita on Purchasing Power Parity (GDP_PPP), Mean Income, and a literature-based index (Patience Score). Our results across 193 countries and 5 LLMs demonstrate that people's time across the world is evaluated in noticeably different ways among LLMs, as well as varying levels of robustness under prompt variations. LLMs consistently recommend the highest compensation for Europe, but mostly the lowest for Africa. Based on GDP_PPP, LLMs underestimate the value of time for users living in Asia and South America. These findings indicate that geographical patterns are embedded in LLMs and may translate in different treatment in agentic settings.