# A Mathematical Theory of Human Machine Teaming

## TL;DR

This paper develops a mathematical framework for human-machine teaming that guarantees team performance does not fall below individual capabilities, addressing fundamental decision fusion challenges.

## Contribution

It introduces a novel theoretical approach to ensure human-machine teams perform at least as well as individual members, independent of environment or modeling accuracy.

## Key findings

- Identifies decision fusion as the core challenge in HMT failure.
- Proposes a performance bound for HMT independent of environment complexity.
- Highlights the need to move beyond modeling humans and machines separately.

## Abstract

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.