Review: “Squeezing” Quantum-like Interdependence Outweighs AI Risks, Cognitive Science Limits, Yet Its Duality Promotes Random Choices, Generalizations and Advantages that Advance Science

In this review article, we introduce the problem of team interaction, cover the mathematics, results, discussion, conclusions, and a path forward. To begin, cognitive science assumes a 1:1 relationship between beliefs and actions, whether with games, concepts, preferences, rational choices, eyewitness accounts, or self-reported pain. Unfortunately, it generalizes to reinforcement for generative-AI (gen-AI), a lower form of learning which can not account for higher-level cognition, the resistance of biases to be rectified, the inability to predict successfully without updated information, and supports Planck’s lament that physics evolves one funeral at a time. The problem with 1:1 beliefs-to-reality is that observations of social interaction only produce separable independent and identically distributed (i.i.d.) data, which, by definition, cannot reconstruct the interactions observed. Presently, Gen-AI uses separable i.i.d. data, preventing Gen-AI models from replicating interdependent human systems. Failing to account for interdependence, classical models of teams do not generalize, nor do their models predict advantages. Solving this problem is critical to advancing the science of teams arbitrarily composed of human-AI-machine-robot members. In contrast, based on interdependence, choosing how to “squeeze” uncertainty in our quantum-like (Q-L) model of teams, generalizes (e.g., vulnerability, espionage, time-energy tradeoffs), models self-organization’s ability to provide advantages (e.g., innovation) not possible under command decision-making (viz., authoritarianism), and may solve the hard-to-find connection between mind and reality. Our results suggest that humans have dual cognitive systems, one being cognition and the other embodied, but hidden, interdependence, which Simon was unable to capture and Kahneman had begun to address, our exemplar being Einstein’s decade-long struggle to construct his concept of general relativity. In the future, we propose that coupled tuning “squeezes” interdependent information to produce the advantages we have found over CDM and current AI risks.