))
MTBR: The Two-Step Memory That Transformed Cooperation in AI
Manage episode 523435754 series 3690682
Content provided by Mike Breault. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Mike Breault or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
We explore how memory-two bilateral reciprocity (MTBR) emerged from multi-agent Q-learning, revealing a dominant social strategy that combines forgiveness with a cycle-breaker. Learn about the dual objective—maximize your relative advantage to deter exploitation while also maximizing your own total payoff to encourage cooperation—and how these rules drive robust cooperation across Prisoner’s Dilemma, Stag Hunt, and evolving networks. Discover why MTBR can lift the average payoff of entire populations and what this means for real-world collaboration and the design of cooperative AI.
Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.
Sponsored by Embersilk LLC
1582 episodes
Share
