Collective Cognitive Coherence in Human Decision-Making About Artificial Intelligence: An Applied Framework for Oversight, Governance, and Human-Machine Teaming

Abstract

The integration of artificial intelligence into high-stakes organizational systems is reshaping how groups of humans make collective decisions, particularly under conditions of shared narrative context, ambiguity, and AI-induced uncertainty. Classical decision theory, which models group cognition as the linear sum of independent rational agents, cannot adequately capture what happens in these environments. Distributed decisions exhibit relational structure – patterns of coherence that classical aggregation cannot detect – and tracing them requires tools drawn from complexity science, information theory, and quantum-inspired multipartite analysis. This paper introduces the Quantum Forces of Change Index™ (Q-FOCI™), an applied systems instrument designed to measure multipartite cognitive coherence in human decision environments and to inform AI oversight design, alignment review architecture, and human-machine team cognition. Drawing from the mathematical formalism of quantum many-body entanglement, Q-FOCI™ rests on an empirical foundation. A mixed-design experiment immersed 40 participants, organized into eight non-communicating five-person groups, in an ambiguous AI malfunction scenario set in 2126. Responses were analyzed using multipartite correlation functions, entanglement-witness analogs, mutual information, and Shannon entropy, then benchmarked against Monte Carlo-generated classical independence baselines. Seven of eight groups exceeded the classical null on at least one measure. Subjective items elicited stronger inter-participant coupling than objective items, suggesting that contextual ambiguity organizes group cognition more powerfully than factual reasoning. A W-state structural analysis further revealed qualitatively distinct coherence classes – GHZ-dominant, W-dominant, and mixed – providing the first behavioral evidence that the GHZ-W distinction has detectable analogs in collective human cognition. Building on this foundation, Q-FOCI™ translates many-body coherence detection into a measurable framework for AI governance, oversight architectures, and human-machine teaming, with a research agenda to validate it in operational contexts.