The field of human-agent interactions and evolutionary game theory is moving towards a deeper understanding of how humans respond to and interact with intelligent agents. Recent studies have shown that while humans can exhibit similar functional behaviors and interactive experiences with agents as they do with humans, there are still significant differences in social attributions and moral/prosocial concerns. The development of new models and frameworks, such as hypergame dynamics and game-theoretic social distancing, is allowing researchers to better capture the complexities of human behavior and decision-making in these contexts. Notably, the incorporation of heterogeneous levels of knowledge and experience among individuals is leading to more nuanced and realistic models of evolutionary game dynamics. Overall, the field is advancing our understanding of how to design and regulate agents that can effectively interact with humans and promote cooperation and trust. Noteworthy papers include:

• A systematic review and meta-analysis that compared psychological and behavioral responses in human-agent vs. human-human interactions, finding that individuals exhibited less prosocial behavior and moral engagement when interacting with agents.
• A study on evolutionary hypergame dynamics that introduced introspection reasoning and social learning, and found that heightened rationality significantly promotes cooperative behaviors.