A Mathematical Theory of Human Machine Teaming
This addresses a critical failure in human-machine collaboration systems, with foundational implications for AI and robotics, though it is incremental in proposing a theoretical condition rather than a new method.
The paper identifies that human-machine teaming often underperforms compared to individual members due to decision fusion deficiencies, and proposes a performance bound ensuring teams do not perform worse than either member alone, independent of various factors.
We begin with a disquieting paradox: human machine teaming (HMT) often produces results worse than either the human or machine would produce alone. Critically, this failure is not a result of inferior human modeling or a suboptimal autonomy: even with perfect knowledge of human intention and perfect autonomy performance, prevailing teaming architectures still fail under trivial stressors~\cite{trautman-smc-2015}. This failure is instead a result of deficiencies at the \emph{decision fusion level}. Accordingly, \emph{efforts aimed solely at improving human prediction or improving autonomous performance will not produce acceptable HMTs: we can no longer model humans, machines and adversaries as distinct entities.} We thus argue for a strong but essential condition: HMTs should perform no worse than either member of the team alone, and this performance bound should be independent of environment complexity, human-machine interfacing, accuracy of the human model, or reliability of autonomy or human decision making.