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The Symmetry of Interdependence in Human–AI Teams and the Limits of Classical Team Science

William Lawless

Published 2025 in AppliedMath

ABSTRACT

Our research goal is to provide the mathematical guidance to enable any combination of “intelligent” machines, artificial intelligence (AI) and humans to be able to interact with each other in roles that form the structure of a team interdependently performing a team’s tasks. Our quantum-like model, representing one of the few, if only, mathematical models of interdependence, captures the tradeoffs in energy expenditures a team chooses as it consumes its available energy on its structure versus its performance, measured by the uncertainty (entropy) relationship generated. Here, we outline the support for our quantum-like model of uncertainty relations, our goals in this study, and our future plans: (i) Redundancy reduces interdependence. This first finding confirms the existence of interdependence in systems, both large and small. (ii) Teams with orthogonal roles perform best. This second finding is the root cause of humans, including scientists, being unable to appreciate the role of interdependence in “squeezing” states of teams. (iii) Cognitive reports may not equal behavior. The last finding allows us to tie our research together and to account for the absence of social scientists from leading the mathematical science of teams. In this article, we review the need for a mathematics for the future of team operations, the literature, the mathematics in our model of agents with full agency (viz., intelligent and interdependent), our hypothesis that freely organized teams enjoy significant advantages over command decision-making (CDM) systems, and results from the field. We close with future plans and a generalization about squeezing states to control interdependent systems.

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