Quantum Probability Outperforms Bayesian Models in Human Decision-Making Under Uncertainty — E8 Intelligence Research

FINDING: Human decision-making under uncertainty is better modeled by quantum probability (complex amplitudes, interference terms) than classical Bayesian probability, with social interaction introducing entanglement-like correlations. | MATH: Quantum probability replaces Kolmogorov axioms with Born rule: P(A) = |⟨ψ|P_A|ψ⟩|²; interference term in two-stage decisions: P(A then B) ≠ P(A)·P(B) — instead P(A∧B) = P(A)P(B|A) + δ(A,B), where δ is a signed interference term (can be negative, violating classical additivity). Social agents: density matrix ρ for N agents, decision via partial trace — ρ_AB ≠ ρ_A ⊗ ρ_B implies non-separability (quantum discord). Key constants: no fixed universal constant emerges; the framework uses ℏ-normalized action scales, but cognition operates in dimensionless probability space. | CONNECTION: The interference term δ(A,B) is bounded by |δ| ≤ √[P(A)P(B)] — this is the same inequality structure as the golden-ratio-adjacent bound in two-slit interference (max con Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com