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

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Human decision-making under uncertainty and social interactions are better modeled by quantum probability frameworks, which account for interference and entanglement-like correlations, than by classical Bayesian probability. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Andrew Stewart Caldin

doi:10.5281/zenodo.22719853

September 12, 2026Open Access

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

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Key Points

To evaluate whether quantum probability models human decision-making and social interactions under uncertainty more effectively than classical Bayesian probability.
Replaced standard Kolmogorov axioms with the Born rule to calculate decision probabilities using complex amplitudes and signed interference terms.
Modeled multi-agent social interactions using density matrices and partial tracing to test for non-separability across dimensionless cognitive action scales.
Decision pathways violated classical additivity through a signed interference term bounded by the relation |δ| ≤ √[P(A)P(B)].
Social agents exhibited non-separable states characterized by ρ_AB ≠ ρ_A ⊗ ρ_B, demonstrating entanglement-like correlations and quantum discord under uncertainty.

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Implication

Computational modeling study reveals that quantum probability accounts for human choices better than classical Bayesian frameworks, suggesting non-separable social correlations.

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Andrew Stewart Caldin (2026) studied this question.

synapsesocial.com/papers/6aa7b3d00926e14a848b314b https://doi.org/10.5281/zenodo.22719853

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

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

Key Points

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