William Lawless
Biography
W.F. Lawless (6/17/2025) retired in 1981 from the USMC-R as a Lt.Col aviator (approx. 1700 flight hours: A4-E, H-34, RF-8, 130 carrier landings). In 1977, he became a mechanical engineer with the Department of Energy's (DOE) facility at its Savannah River Site, SC, where he was in charge of nuclear waste management, but in 1983, he blew the whistle on DOE's mismanagement of its military radioactive wastes. For his dissertation, he wrote about the mistakes made by large organizations with world-class scientists and engineers. After his PhD in 1992, DOE invited him to join its board at DOE-SRS, where he coauthored the first and numerous other recommendations on environmental remediation to treat DOE's mismanagement of its radioactive wastes (e.g., the regulated closure in 1997 of the first two high-level radioactive waste tanks). His research today is on autonomous human-machine teams. With the Naval Research Lab, DC, he has co-edited 12 books on AI (Springer 2016; 2017; CRC 2018; Elsevier 2019; 2020 [Elsevier nominated the latter, "Human-Machine Shared Contexts," to ASIS&T for its Information Science book of the year award for 2020]; Springer, 2021; Springer LNCS, 2021; Frontiers in Physics, 2023; Elsevier, 2024; 2024; 2025; 2026 proposed; Entropy 2025). He co-organized a special issue on human-machine teams and explainable AI for AI Magazine (2019; 1st editorial). He authored/co-authored 300+ peer-reviewed publications (e.g., https://www.preprints.org/manuscript/202307.0607/v1). He co-organized thirteen AAAI symposia at Stanford (2025: Current and Future Varieties of Human-AI Collaboration). For Frontiers in Physics, he co-organized: "Interdisciplinary Approaches to the Structure and Performance of Interdependent Autonomous Human Machine Teams and Systems (2nd editorial). He co-organized AHFE's 2023 SIG: Data dependency. He co-organized Entropy's Special Issue, 2025: "An Entropy Approach to the Structure and Performance of Interdependent Autonomous Human Machine Teams and Systems (3rd editorial); he has 9 award nominations, the top article in AHFE 2024, and he has been chosen for a distinguished faculty research award at NRL, summer 2025; he has had numerous invited articles and talks.
Research Interest
Team interdependencies counter the limits of big data and information losses in the interaction
Abstract
Big data, the foundation for Generative Artificial Intelligence (Gen AI), combined with gen AI, together pose an existential threat to human existence, are credited with misleading results as complexity increases (e.g., hallucinations), and provide information to users that has exploited the vulnerable. These problems ignore the concept of agency (responsibility), the chief characteristic of interdependence. In comparison, we have written that interdependence is a resource of agency that big data and generative AI, as presently constituted, cannot surpass for several reasons.
First, generative AI models use interaction information to produce knowledge. Large data bases are often curated to develop statistics in a confined domain for a narrow range of decisions with dedicated applications (e.g., driving a car). The big data, gen AI or knowledge transmitted should be stable, valid and generalizable from laboratories to applications in the field. However, the information derived is usually collected from interaction products while operating in closed systems, even if each step of an interaction occurs with real-time data collection systems, but the information produced is based on independent and identically distributed (i.i.d.) data, and modeled by algorithms with separable elements (e.g.,tensors). Second, however, and by definition, i.i.d. data cannot replicate the interdependent data generated and processed by interacting agents (similar to the frames in a movie or video). Third, stated in 2022 by the National Academy of Sciences (NAS), the interaction cannot be disentangled, similar to the inability to look inside of quantum entanglement or superposition, suggesting hidden information, and where tensors do not apply.
In contrast, first, for interdependence, the results of interactions and choices in the marketplace are probabilistic, random on average, and in dicative that interdependence among free agents is a synergistic resource (e.g., compared to central decision-making, or CDM, it produces more innovation, more productivity per employee, and less corruption than CDM). Second, the primary benefit we have found is that the least structural entropy production (SEP) generated by the structure of the best teams, which agrees with the claim by NAS, reflects a trade-off between SEP and the maximum entropy production (MEP) for team productivity, a trade off exploited by the best teams. The structure of the best teams produces insufficient information when a team directs the maximum amount of its free energy to increase productivity. Third, the information derived from the i.i.d. data collected from interactions explains the replication crisis in the social sciences; moreover, our quantum-like theory of interdependence explains that the social data collected from self-organized roles are orthogonal, thus, resulting in the poor correlations that have caused the crisis. Fourth, when interdependent agents assume agency to self-organize, the advantages afforded counter the existential risks posed by big data and Gen-AI; further, to enable authoritarian rule, CDM decision-makers suppress interdependence to govern by using the i.i.d. data that they have made predominant, while interdependent agents when free to self-organize around the hidden interdependent information they produce provides a significant advantage to counter authoritarians, gangs, kings and any others who are not free. Fifth, and final for now, as an example of generalization, based on Noether?s time symmetry conservation of energy, given least SEP and maximum MEP
based on n teammates, the only way to increase MEP is to have n+1 members, as predicted by Cummings in 2015, accounting for the motivation among organizations to merge. Overall, we lay the theoretical foundation for AI integrated into a free market, free political economy, open-ended debates and superior decision-making.
Keywords: Interdependence; quantum likeness; human-machine teams; artificial intelligence (AI); advantages
