This PhD defense presents research at the intersection of machine learning, reinforcement learning, social learning, affective computing, and human-AI interaction. The thesis is that social learning is a powerful mechanism for intelligence and explores how AI agents can learn from one another and from humans. Projects include intrinsic social influence rewards for multi-agent coordination, communication protocols emerging through influence, conversational agents trained from implicit human feedback such as sentiment, generative models improved through facial-expression feedback, and personalized well-being prediction from behavioral and physiological data. The thesis concludes that socially informed learning can improve coordination, adaptability, and human alignment.

This research introduces the “signaling gap,” showing how states use controlled media to communicate positions they cannot express formally. Analyzing 174,000 articles, it finds that Russia-aligned countries signalled disapproval of the Ukraine invasion through negative coverage. The study bridges political science and intelligence practice, highlighting informal communication under constraint.