Editorial: Interdisciplinary approaches to the structure and performance of interdependent autonomous human machine teams and systems

Abstract

Editorial on the Research Topic Interdisciplinary approaches to the structure and performance of interdependent autonomous human machine teams and systemsOur Research Topic seeks to advance the physics of autonomous human-machine teams with a mathematical, generalizable model [1]. However, limited team science exists (e.g., aircrews; in [2]). Why? Team science has been hindered by relying on observing how "independent" individuals act and communicate (viz., i.i.d. data; [3,4]), but independent data cannot reproduce the interdependence observed in teams [5]. In agreement, the National Academy of Sciences stated: The "performance of a team is not decomposable to, or an aggregation of, individual performances" ([6], p. 11), evidence of nonfactorable teams and data dependency, requiring random searches to find well-fitted teammates, all characterized by fewer degrees of freedom and reduced entropy from interdependence. We review what else we know about a physics of autonomous humanmachine teams.First, we argue that state-dependency [7] rescues traditional social science from its current validation (e.g., "implicit" bias; [8,9]) and replication crises ([10]; e.g., attempts to reduce bias are "dispiriting" [11]), caused by assuming that cognition subsumes individual behavior, needing only independent data (i.i.d.) for teams. The result: Traditional models include large language models like OpenAI's ChatGPT and game theory. Strictly cognitive, ChatGPT and two-person games are assumed to easily connect to reality, but ChatGPT skeptics exist ([12]; [13]); and in Science [14], real-world multi-agent approaches are "currently out of reach for state-of-the-art AI methods." Previewed in Science, "realworld, large-scale multiagent problems . . . are currently unsolvable" [15].Second, to describe interdependence between cogition and behavior, Bohr, the quantum pioneer [16,17]) borrowed "complementary" from psychologist, William James [18]. Later, but long before the Academy's 2021 report, Schrödinger [19] wrote that entanglement meant "the best possible knowledge of a whole does not necessarily include the best possible knowledge of all its parts, even though they may be entirely separate." [20] borrowed

A Quantum-like Model of Interdependence for Embodied Human–Machine Teams: Reviewing the Path to Autonomy Facing Complexity and Uncertainty

“…The equality in Equation ( 8 ) holds only when the parts of a whole are independent of each other, meaning that the entropy of the whole is less than or equal to the sum of the parts of the whole, validating Lewin’s [ 3 ] assertion that “the whole is more than the sum of its parts”, and the same claim by systems engineers [ 76 ], both claims that, unfortunately, are largely ignored of late [ 1 ].…”

“…Bistability also refers to an animal’s self-interest; e.g., when prey in a forest do not suspect the presence of predators, they overgraze and harm the forest [ 105 ]. Moreover, we know that individuals multitask poorly [ 106 ]; in contrast, the purpose of a team is to multitask [ 1 ].…”

“…In this review, our goal [ 1 ] is to enable autonomous human–machine teams (AHMTs) to be designed with a set of algorithms that can be deployed with Artificial Intelligence (AI) to structure a human–machine team able to reach its maximum performance in the open, specifically away from the laboratory where most team science is currently practiced. In the open, but not in the laboratory, teams face uncertainty and conflict where, as Mann [ 2 ] has concluded, rational beliefs fail.…”

“…A well-functioning team has the potential to raise the power of the individual or teammates in a team beyond that of the individuals independently performing the same functions but outside of a team (e.g., [ 14 ]). Thus, we assume that the individuals independently performing the actions of a team when not a member of a team serve as Adam Smith’s [ 81 ] “invisible hand,” forming a baseline that we have used in the past to determine the power of a team [ 1 ].…”

“…In the past, we assumed that if the structure of the perfect team becomes a unit, by taking the limit of the operator for the logarithm of SEP, we found that predictions for human–machine teams from the theory of complementarity are observations that a machine can make (e.g., to use deception inside of a team, do not contribute to the team’s structural entropy; reduce structural entropy production (SEP) to allow the ability of the free energy available to a team to increase a team’s performance; a vulnerability becomes observable after an attack by witnessing an increase in an opponent’s SEP or a decrease in its performance entropy (MEP); and by dampening interdependence, authoritarianism decreases a team’s ability to innovate and increases its need to steal technology to be competitive (in [ 1 , 40 ]). Next, we begin to include operators.…”