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Explore how artificial intelligence can design mechanisms that promote human cooperation and sustainable behavior in this conference talk by Google DeepMind Research Scientist Raphael Köster. Learn about the intersection of behavioral economics, mechanism design, and deep reinforcement learning as applied to real-world coordination problems like auctions, elections, and resource allocation. Discover how neural networks can model human behavior to create authentic simulations where RL agents learn to develop novel policies for common-pool resource problems. Examine specific examples of AI-designed mechanisms that successfully address free-rider problems while promoting more equitable outcomes and larger surpluses across all participants. Gain insights into the technical challenges of modeling human strategic behavior in neural networks and understand how these models enable RL agents to discover innovative solutions that traditional mechanism design might miss. The presentation covers practical applications of human-centered mechanism design and demonstrates how AI can serve as a tool for creating better coordination systems in society.
