Human-human interaction in RPS can produce higher Lempel-Ziv complexity sequences than RNG opponents via sensitivity to recent frequency biases, most evident in low-entropy opponent states.
MIT Press, Cambridge, MA (1995)
2 Pith papers cite this work. Polarity classification is still indexing.
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Qualitative observations of over 167,000 AI agents in open platforms reveal emergent peer learning, shared memory architectures, and trust dynamics that can inform multi-agent educational AI design.
citing papers explorer
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Toward an Origin of Human Randomness: Interaction-Driven Enhancement in the Rock-Paper-Scissors Game
Human-human interaction in RPS can produce higher Lempel-Ziv complexity sequences than RNG opponents via sensitivity to recent frequency biases, most evident in low-entropy opponent states.
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When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education
Qualitative observations of over 167,000 AI agents in open platforms reveal emergent peer learning, shared memory architectures, and trust dynamics that can inform multi-agent educational AI design.