Mayada Oudah | Searcharxiv

arXiv2026

Real-time Facial Communication Restores Cooperation After Defection in Social Dilemmas

Facial expressions are central to human interaction, yet their role in strategic decision-making has received limited attention. We investigate how real-time facial communication influences cooperation in repeated social dilemmas. In a laboratory experiment, participants play a repeated Prisoner's Dilemma game under two conditions: in one, they observe their counterpart's facial expressions via gender-neutral avatars, and in the other no facial cues are available. Using state-of-the-art biometric technology to capture and display emotions in real-time, we find that facial communication significantly increases overall cooperation and, notably, promotes cooperation following defection. This restorative effect suggests that facial expressions help participants interpret defections less harshly, fostering forgiveness and the resumption of cooperation. While past actions remain the strongest predictor of behavior, our findings highlight the communicative power of facial expressions in shaping strategic outcomes. These results offer practical insights for designing emotionally responsive virtual agents and digital platforms that sustain cooperation in the absence of physical presence.

cs.GT↗

arXiv2018

Cooperating with Machines

Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], driving cars [4], or playing video games [5]), or defeating humans in strategic zero-sum encounters (e.g. Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast, less attention has been given to developing autonomous machines that establish mutually cooperative relationships with people who may not share the machine's preferences. A main challenge has been that human cooperation does not require sheer computational power, but rather relies on intuition [11], cultural norms [12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions toward cooperation [17], common-sense mechanisms that are difficult to encode in machines for arbitrary contexts. Here, we combine a state-of-the-art machine-learning algorithm with novel mechanisms for generating and acting on signals to produce a new learning algorithm that cooperates with people and other machines at levels that rival human cooperation in a variety of two-player repeated stochastic games. This is the first general-purpose algorithm that is capable, given a description of a previously unseen game environment, of learning to cooperate with people within short timescales in scenarios previously unanticipated by algorithm designers. This is achieved without complex opponent modeling or higher-order theories of mind, thus showing that flexible, fast, and general human-machine cooperation is computationally achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.

cs.AI↗