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

Abstract

In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human–machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels—some low, and others high. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few results that can be generalized to human–machine interactions; by the replication crisis of today (e.g., the invalid scale for self-esteem); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us theorization about the bistability of mind and body, but it poses a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We review the science of teams and human–machine team research in the laboratory versus in the open field; justifications for rejecting traditional social science while supporting our approach; a fuller understanding of the complexity of teams and tasks; the mathematics involved; a review of results from our quantum-like model in the open field (e.g., tradeoffs between team structure and performance); and the path forward to advance the science of interdependence and autonomy.

Shannon Holes, Black Holes and Knowledge: Can a Machine become a “Self-Aware" Teammate?

“…Instead, as we have argued in other forums ( [1]; [79]), debate requires a search for the truth that probes for weaknesses and the use of all of the tools available in seeking truth's essence.…”

“…As the pieces of an algorithm fit together like the pieces of a puzzle, its entropy drops. We have proposed that an entropy drop occurs when the structure of a well-functioning team fits together [1].…”

“…Social science has made and accepted their own decision that reality is fully accessible to individuals (see the review in [1]). However, according to the National Academy of Sciences [92], and supported by assembly theorists [93], the attributions of the causes of a team's results to the individual(s) in a team are not possible, even for those who are fully conscious and fully aware, meaning that "holes" exist in reality for Shannon information.…”

“…Shannon's experimental results with humans illustrate the embodied effects of information in social situations, creating a loss of Shannon information [1] with the existence of what we have named "Shannon holes. "…”

“…In a past review of our theory of interdependence [1], we concluded first, that humans make trade-offs in energy-entropy production between structure and performance, where a poorer team structure reduces performance, characterized by more structural entropy produced by wasted energy, and motivating the need to replace poorly performing members; second, that humans are unable to physically copy and replicate the choices that lead to a well-performing team, introducing random selection into new member choices; third, that interdependence requires strong boundaries to reduce interference to maintain states of interdependence; fourth, that coherence of a state of interdependence requires or reflects stability; and, among others of our findings not reviewed in this entry (e.g., [2]), fifth, that the vulnerability in a targeted team is characterized to its opponents and to itself by observing the loss of a team's structural integrity, a loss of its productivity, or both.…”