Matthew John Hoffman

Exploring Human-Technology Interaction in Layered Security Military Applications

System-of-systems modeling has traditionally focused on physical systems rather than humans, but recent events have proved the necessity of considering the human in the loop. As technology becomes more complex and layered security continues to increase in importance, capturing humans and their interactions with technologies within the system-of-systems will be increasingly necessary. After an extensive job-task analysis, a novel type of system-of-systems simulation model has been created to capture the human-technology interactions on an extra-small forward operating base to better understand performance, key security drivers, and the robustness of the base. In addition to the model, an innovative framework for using detection theory to calculate d’ for individual elements of the layered security system, and for the entire security system as a whole, is under development.

Modeling human-technology interaction as a sociotechnical system of systems

As system of systems (SoS) models become increasingly complex and interconnected a new approach is needed to capture the effects of humans within the SoS. Many real-life events have shown the detrimental outcomes of failing to account for humans in the loop. This research introduces a novel and cross-disciplinary methodology for modeling humans interacting with technologies to perform tasks within an SoS specifically within a layered physical security system use case. Metrics and formulations developed for this new way of looking at SoS termed sociotechnical SoS allow for the quantification of the interplay of effectiveness and efficiency seen in detection theory to measure the ability of a physical security system to detect and respond to threats. This methodology has been applied to a notional representation of a small military Forward Operating Base (FOB) as a proof-of-concept.

Maximizing the U.S. Army’s Future Contribution to Global Security Using the Capability Portfolio Analysis Tool (CPAT)

Recent budget reductions have posed tremendous challenges to the U.S. Army in managing its portfolio of ground combat systems (tanks and other fighting vehicles), thus placing many important programs at risk. To address these challenges, the Army and a supporting team developed and applied the Capability Portfolio Analysis Tool (CPAT) to optimally invest in ground combat modernization over the next 25–35 years. CPAT provides the Army with the analytical rigor needed to help senior Army decision makers allocate scarce modernization dollars to protect soldiers and maintain capability overmatch. CPAT delivers unparalleled insight into multiple-decade modernization planning using a novel multiphase mixed-integer linear programming technique and illustrates a cultural shift toward analytics in the Army’s acquisition thinking and processes. CPAT analysis helped shape decisions to continue modernization of the

Materiel availability modeling and analysis for a complex Army weapon system

10 billion Stryker family of vehicles (originally slated for cancellation) and to strategically reallocate over

Method for Determining the Sensitivity of a Physical Security System.

20 billion to existing modernization programs by not pursuing the Ground Combat Vehicle program as originally envisioned. More than 40 studies have been completed using CPAT, applying operations research methods to optimally prioritize billions of taxpayer dollars and allowing Army acquisition executives to base investment decisions on analytically rigorous evaluations of portfolio trade-offs.

Risk Evaluation for Identification and Intervention in Dual Use Research of Concern (DURC) for International Biological R&D Activity

Materiel availability (A<inf>m</inf>) is a new US Department of Defense Key Performance Parameter (KPP) implemented through a mandatory Sustainment Metric consisting of an Availability KPP and two supporting Key System Attributes (KSAs), materiel reliability and ownership cost. Sandia National Laboratories (Sandia), in conjunction with several US Army organizations, developed the analytical foundation, assumptions, and brigade-level modeling approach to support lifecycle, fleet-wide A<inf>m</inf> modeling and analysis of a complex Army weapon system. Like operational availability (A<inf>o</inf>), A<inf>m</inf> is dependent on reliability, but A<inf>m</inf> is also affected by other factors that do not impact A<inf>o</inf>. The largest influences on A<inf>m</inf> are technology insertion and reset downtimes. A<inf>m</inf> is a different metric from A<inf>o</inf>. Whereas A<inf>o</inf> is an operational measure, A<inf>m</inf> is more of a programmatic measure that spans a much larger timeframe, additional sources of downtime, and add itional sources of unscheduled maintenance.