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MTBR: The Two-Step Memory That Transformed Cooperation in AI
Manage episode 523435754 series 3690682
İçerik Mike Breault tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Mike Breault veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.
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.
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