Rational Use of Time for Social Coordination in Humans and AI

A study of 150 humans and two classes of AI models playing the cooperative card game The Mind found that human timing-based coordination is best captured by a Bayesian strategy that infers a partner's belief from their timing, and frontier models follow a similar pattern, according to arXiv paper 2611.14766v1. However, none of the tested models efficiently adapted their behavior to their partners over the course of the game, unlike humans. The authors conclude that humans perform implicit probabilistic reasoning about continuous-time signals in dynamic social settings, and that such mechanisms may emerge without real-time experience, while flagging adaptation to implicit signals as a new challenge for AI systems.

arXiv:2611.14766v1 Announce Type: new 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.