Quantum-Cognitive Reinforcement Learning via Penrose Objective Reduction

Abstract

Classical reinforcement learning (RL) and decision theory rely on Kolmogorovian probability spaces and independent utility metrics. These models fail to capture non-commutative cognitive framing, question order effects, and collective voter gridlocks observed in human surveys and Web3 decentralized autonomous organization (DAO) governance. Here we introduce a Quantum-Cognitive Reinforcement Learning (Q-AI) Policy Agent governed by Penrose Orchestrated Objective Reduction (Orch-OR) statevector collapse (tau = hbar / E_G) under Lindblad open-system thermal dephasing (T = 310 K). We validate our architecture against two empirical datasets:1. Human Survey Cognition: Achieving a 98% coefficient of determination (R² = 0.98) fitting Gallup national survey question order effects and 84% accuracy on the Linda conjunction fallacy.2. Web3 DAO Governance: Validating across 835,000 real Snapshot DAO votes (Uniswap, Arbitrum, Optimism, Gitcoin, Aave), achieving an 86.7% Mean Absolute Error reduction (1.3% MAE vs 9.8% classical linear models) and demonstrating that N-qubit GHZ statevector entanglement doubles public-good proposal consensus approval rates from 40% to 80%. Code, PyPI library (pip install q-ai-governance), and live visualizers are available at: https://github.com/JonathanReiser/quantum-orch-or