Game Theory Offers Framework for Understanding Human-AI Competition and Cooperation

Researchers are turning to evolutionary game theory (EGT) to better understand how humans and artificial intelligence might interact, compete, and cooperate as both entities continue to develop. The work examines whether classical models of strategic behavior in nature can illuminate the emerging dynamics between human and machine intelligence.

Three Core Game Models#

The research focuses on three foundational EGT models with direct relevance to human-AI dynamics:

• Hawk-Dove Game: This model predicts that competing entities will settle into balanced mixed strategies—some aggressive, some passive—based on the costs of conflict and benefits of resources.
• Iterated Prisoner's Dilemma: When interaction repeats over time, this model suggests that cooperation can emerge, potentially driving cognitive coevolution between players.
• War of Attrition: Competition for limited resources may lead to asymmetric outcomes and mutually agreed conventions for resource sharing.

Mutual Evolution and Adaptation#

The analysis considers how both humans and AI are shaped by their interaction. AI systems continuously adapt to human feedback and user behavior, while human neuroplasticity allows brains to rewire in response to technological stimuli. Should humans and AI converge in their capabilities, the resulting blend of human neural flexibility and machine optimization could raise substantial ethical and cognitive questions.

Broader Implications and Future Work#

While EGT provides a promising lens for modeling human-AI coevolution, the authors note that a more complete understanding will require moving beyond game theory alone. Empirical validation, additional theoretical frameworks, and perspectives from psychology, biology, and computer science are all needed. The study includes a computational simulation to illustrate potential scenarios.

View the full paper on arXiv →