Game Theory and the Dynamics of Complex Agentic Systems

Game Theory and the Dynamics of Complex Agentic Systems By Maxim Yakimenko Assisted by Gemini 2.5 pro Slides on GDrive: https://drive.google.com/drive/folder... Slides on GitHub - https://github.com/lselector/seminar (click on pptx file, then on "raw" or download button on the right) Multi-agent systems (MAS) fail with standard reinforcement learning because agent outcomes depend on each other's actions in constantly changing environments. Solution: Use the game theory to model inter-dependencies. Evolutionary Game Theory (EGT) - shows how successful strategies spread through populations via replicator dynamics - what works gets replicated (survives and grows). Key Insight: "Survival of the flattest" means robust strategies often out-compete optimal but fragile ones in realistic, high-mutation environments. Fundamental Limit: Turing's Halting Problem proves complete prediction of complex multi-agent behavior is mathematically impossible. We can simulate probabilities but never achieve certainty about emergent dynamics. Game theory provides powerful analytical tools while acknowledging inherent unpredictability.