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Rational Use of Time for Social Coordination in Humans and AI

📅 2026-11-14 ⏱️ 约 6 分钟阅读 ✍️ AI导航编辑部 🔗 arxiv.org
Rational Use of Time for Social Coordination in Humans and AI
📝 内容摘要

Computer Science > Artificial Intelligence [Submitted on 10 Nov 2026] Title:Rational Use of Time for Social Coordi

📌 核心要点

  • Computer Science > Artificial Intelligence
  • [Submitted on 10 Nov 2026]
  • Title:Rational Use of Time for Social Coordination in Humans and AI

Computer Science > Artificial Intelligence

[Submitted on 10 Nov 2026]

Title:Rational Use of Time for Social Coordination in Humans and AI

View PDF HTML (experimental)Abstract:All social interactions are situated in time. Previous studies have shown that humans are able to use implicit temporal cues, such as the duration of a pause, to infer others' mental states. But how do humans use temporal cues for dynamic, fine-grained social coordination? And can AI systems do the same, without ``experiencing'' continuous real-time as humans do? We study these questions using a cooperative card game task called The Mind, which requires players to achieve a shared goal across repeated interactions but forbids explicit communication: the only available signal is the passage of time. We evaluate 150 humans, and two classes of AI models which reflect different ways of operationalizing time in artificial systems: computer-use agents (CUAs), and standard text-prompted LLMs. We find that human behavior is best captured by a Bayesian strategy that infers a partner's belief from their timing---and similarly for frontier models. However, unlike humans, none of the tested models seem to efficiently adapt their behaviors to their partners' over the course of the game. From a cognitive perspective, our findings suggest that humans perform implicit probabilistic reasoning about continuous-time signals in dynamic social settings, and that such reasoning mechanisms may emerge without real-time experience. Practically, our study highlights new challenges for AI systems to coordinate efficiently with other agents by learning to interpret and adapt to their implicit signals in dynamic settings.

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