The classical versus quantum-like model of interdependence: The science of autonomous human-machine, robotic and AI teams

Interdependence is an enduring puzzle in the physics of complex social and technical systems. When humans, robots, and learning algorithms act together, the state of the team cannot be reconstructed from the states of its members: roles are complementary in Bohr’s sense (Pais, 1991), measurements disturb what they measure, and the order of decisions changes the outcome (Busemeyer & Bruza, 2024). Classical, independent-agent probability handles none of this. Quantum probability and quantum information theory, with superposition, non-commutativity, contextuality, and entanglement as a formal description of non-separable states, offer a mathematics built for these properties (Bagarello & Khrennikov, in press). Meanwhile robotics has moved to heterogeneous multi-agent systems coordinating under uncertainty, and machine learning supplies both the engines inside those systems and the tools to analyse them at scale (Endsley et al., 2022). The physics linking these strands has yet to be assembled.The stakes are practical. Wherever an environment is too hazardous for people, from disaster response and search and rescue to nuclear decommissioning, deep-sea operations and space exploration, robots will work alongside human teams as soon as they are capable of it. Yet while large language models have advanced quickly, large action models have not, and combined language-action models scarcely exist. That gap is where quantum-like models of human-machine and robot-only teams apply.At the centre sits the problem itself. Its difficulty should govern the size and structure of the team assembled to solve it, and the trade-off between structure, productivity, and difficulty is uncharacterised (Lawless, 2026). The hardest problems bear the most fruit once solved, yet we have no agreed way to say whether a team is progressing, nor any observable for which of two competing decisions is the better. Entropy production is a candidate: a team producing the largest entropy productively often advances towards the best available solution, whereas higher structural entropy production correlates with low conventional productivity. For example, since 1997, under majority rules, the Department of Energy’s Savannah River Site Citizens Advisory Board has approved the closing of eight high-level radioactive waste tanks; however, stymied by consensus-seeking rules, the Hanford board has recommended none and showed markedly more internal conflict (Bradbury et al., 2003).The editorial team approaches this from three directions. Khrennikov, with Bagarello (in press), develops open-system quantum-like models linking collective behaviour, including social laser dynamics, to cognitive observables in the human brain. Aspalter’s (2025) research has offered a solution to Friedrich A. von Hayek’s unsolved “Problem of Coordination” of social and economic communication applied to economic and financial markets, with quantum models of human entanglement using all forms of inter- and intrapersonal, plus AI-based, communication, with a focus on power and power relations. Lawless (2026) studies how the uncertainty of interdependence limits classical team science and non-separable data hides information, and is exploring debate with classical and quantum-like models.This Research Topic brings classical versus quantum-like formalisms, robotics, and machine learning into one conversation. We welcome researchers in interdisciplinary and statistical physics, quantum information, robotics and control, artificial intelligence, and systems engineering willing to test whether classical models versus entanglement-like descriptions of team states best earn their keep.Specific objectives include:• formalising interdependence in human-machine and multi-robot teams with classical or quantum-like models, and stating what each formalism predicts• relating problem difficulty to team size and structure, and characterising the structure-productivity trade-off• defining and measuring progress, or its absence, in interdependent teams, and the advantages that classical or quantum-like models afford• testing entropy production, including structural entropy production, as an observable for decision quality and loss of team integrity• grounding these models in robotics and machine learning, as team members and as a route to falsify interdependent, classical or quantum-like team science models from dataThemes of interest include, but are not limited to:• Classical versus quantum-like models of cognition, decision-making, team dynamics, and social and economic systems (Aspalter, 2025)• Entanglement, non-separability, and contextuality in interdependent agents• Problem difficulty, team size, and the structure-productivity trade-off• Entropy production and thermodynamic approaches to team progress and decision quality• Robotics and multi-agent systems: swarms, shared autonomy, human-robot teaming, and especially control• Machine learning, reinforcement learning, causal representation learning, and dependent-data modelling (Schölkopf et al., 2021)• Explainable and context-aware AI, framed dynamically rather than statically• Metrics, benchmarks, and experimental designs for measuring interdependence: bistability, measurement problems, non-separable data, and the advantages of classical versus quantum-like models, given that interdependent teams outperform non-interdependent ones (Cummings, 2015)• Governance, safety, extreme AI risk, and the human-centered framing of AI within a quantitative model, including whether classical or quantum-like models can best counter the risks posed by AI (Bengio et al., 2024; Leo XIV, 2026)Together, these connect formal theory, robotic systems, and independent, data-driven inference, moving the science of autonomy from metaphor towards measurable, testable models of interdependent teams.We invite Original Research, Review, Mini Review, Methods, Perspective, and Hypothesis and Theory articles. All contributions must advance the theoretical, computational, or empirical understanding of interdependence in human-machine or robotic systems, with relevance to interdisciplinary physics. References• Aspalter, C. (2025). Quantum Economics: The New Economics of Communication, Power and Power Relations. Cheltenham: Edward Elgar. doi: 10.4337/9781035366811• Bagarello, F., and Khrennikov, A. (in press). Quantum-Like Modeling of Cognition and Society: From Decision Theory to Social Laser Dynamics. Oxford: Oxford University Press.• Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., et al. (2024). Managing extreme AI risks amid rapid progress. Science 384, 842–845. doi: 10.1126/science.adn0117• Bradbury, J. A., Branch, K. M., and Malone, E. L. (2003). An Evaluation of DOE-EM Public Participation Programs. PNNL-14200. Richland, WA: Pacific Northwest National Laboratory.• Busemeyer, J. R., and Bruza, P. D. (2024). Quantum Models of Cognition and Decision, 2nd Edn. Cambridge: Cambridge University Press.• Cummings, J. (2015). “Team science successes and challenges,” in National Science Foundation Sponsored Workshop on Fundamentals of Team Science and the Science of Team Science (Bethesda, MD).• Endsley, M. R., Hoffman, R. R., and National Academies of Sciences, Engineering, and Medicine (2022). Human-AI Teaming: State of the Art and Research Needs. Washington, DC: National Academies Press. doi: 10.17226/26355• Lawless, W. F. (2026). Towards tunable advantages of quantum-like teams: the physics of interdependent teams to “squeeze” uncertainty. Front. Phys. 13. doi: 10.3389/fphy.2025.1715888• Leo XIV (2026). Magnifica Humanitas: Encyclical Letter on Safeguarding the Human Person in the Time of Artificial Intelligence. Vatican City: The Holy See. Available online at: https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html• Pais, A. (1991). Niels Bohr’s Times: In Physics, Philosophy, and Polity. Oxford: Oxford University Press.• Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., et al. (2021). Towards causal representation learning. arXiv [Preprint]. arXiv:2102.11107. Topic editors: William Frere Lawless, Andrei Khrennikov, Christian Aspalter Source: https://www.frontiersin.org/research-topics/84833/the-classical-versus-quantum-like-model-of-interdependence-the-science-of-autonomous-human-machine-robotic-and-ai-teams