mlsanityData to 14 Oct 2026

A Global View of How LLMs Value Human Time

arXiv 2610.18515 ↗First posted 13 Oct 2026cs.CY cs.AIcited by 0

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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.

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.

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References · 7

  1. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
  2. 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 103
  3. 2024Large Language Models Reflect the Ideology of their Creatorscited by 57
  4. 2024Large Language Models Assume People are More Rational than We Really arecited by 54
  5. 2025EconEvals: Benchmarks and Litmus Tests for Economic Decision-Making by LLM Agentscited by 10
  6. 2024One world, one opinion? The superstar effect in LLM responsescited by 6
  7. 2025Temporal Preferences in Language Models for Long-Horizon Assistancecited by 2

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  1. 2023Can LLMs Capture Human Preferences?
  2. 2026LLMs Contain Multitudes: How Deployment Context Reshapes Model-Level Preferences and Values
  3. 2026People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe
  4. 2025Think Again! The Effect of Test-Time Compute on Preferences, Opinions, and Beliefs of Large Language Models
  5. 2026Evaluating LLM Behavior in Hiring: Implicit Weights, Fairness Across Groups, and Alignment with Human Preferences
  6. 2026Evaluating LLM-Generated Preference Distributions
  7. 2025An Evaluation of Cultural Value Alignment in LLM
  8. 2025Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values