Modeling Synergies in Large Human-Machine Networked Systems by Han-Lim Choi and Sameera Ponda

This research is a part of MURI "Modeling Synergies in Large Human-Machine Networked Systems," whose overall goal is to develop validated theories and techniques to predict behavior of large-scale, networked human-machine systems involving unmanned vehicles, to model human decision making efficiency in such networked systems, and to investigate the efficacy of adaptive automation to enhance human-system performance.
Research in ACL will focus on the lowest and the highest levels of human-machine interaction where the overall decision architecture consists of four levels of interaction layers: human-in-the-loop, high-fidelity cognitive models, large-scale simulations, and high level abstractions. At the human-in-the-loop layer, controlled experiments with human decision-makers in small group settings are performed in order for the resulting data to be used to develop, constrain and validate high-fidelity cognitive models of the various operational and decision-making roles. At the high level abstractions layer, new notions to describe behavior a large-scale heterogeneous network will be developed; this includes statistical mechanical models, and information-theoretic descriptions.