Modern network-based communication has resulted in increased decentralization in the design and control of engineered systems as well as more rapid interactions among people and technology. These large-scale interconnected systems are sometimes referred to as systems of systems. The autonomy inherent in these types of systems can strain traditional systems engineering approaches that rely on prediction and control. Thus, it may be necessary to apply strategies to influence rather than control these systems. Systems engineering and engineering design will need to evolve to accommodate these changes.

My long-term research goal is to develop approaches to design and evolve large-scale systems of systems that consist of interacting engineered and social components. Presumably, to make informed decisions about these systems, one would need to model them.  However modeling large-scale systems of systems may require integrating knowledge from multiple, diverse disciplines including physics, biology, engineering, economics, and psychology. Unfortunately, this knowledge is not packaged for reuse and integration. Models and theories from different disciplines can be overlapping, redundant, and sometimes even contradictory. This makes it challenging to integrate this knowledge computationally to support design efforts and decision-making. There is a need for a consistent way to package scientific knowledge as models, a mechanism to integrate those models, and approaches to search alternative combinations of models.

Recent Projects

Modeling the Adoption of an Evidence-based Healthcare Intervention

Widespread TCM AdoptionWhile the use of evidence-based interventions (EBIs) has been advocated by the medical research community for quite some time, uptake of these interventions by healthcare providers has been slow. One possible explanation is that it is challenging for providers to estimate impacts of a particular EBI on their respective organizations. The randomized controlled trials (RCTs) used to evaluate EBIs measure efficacy independent of the larger healthcare system. Factors such as provider organizational structure, local patient demographics, and payment systems can affect the feasibility of actually implementing an EBI. To investigate, a collaborative team of researchers from the Stevens Institute of Technology and the University of Pennsylvania School of Nursing developed and evaluated a type of simulation called a policy flight simulator to determine if it could facilitate the adoption of a particular EBI called the Transitional Care Model (TCM). The TCM uses an advanced practice nurse led model of care to transition older adults with multiple chronic conditions from a hospitalization to home.

The net result of the effort was a pair of simulations: one modeled an individual provider system’s decision to adopt TCM and the other modeled a subset of the factors that affected widespread adoption of TCM across the US healthcare system. The single provider simulation provides a data-driven projection of the financial impact of adopting TCM for any hospital in the United States. The widespread adoption simulation provides a more stylized and qualitative analysis of how various factors could influence the spread of TCM from a few early adopters to the entire US healthcare system.

Web-based demonstration versions of the two models can be found here:

Specific findings will be presented in forthcoming publications.

Modeling Policies for Protecting Critical Infrastructure

power-electricity-line-pylon-159218Critical infrastructure includes such systems as the power grid, communications networks, transportation networks, food delivery systems, financial systems, and emergency response systems. These systems have become essential to the operation of modern society. At the same time, critical infrastructure systems have become extensively interconnected and networked. While there are clear benefits to the functionality of infrastructure from this interconnection, the inter-dependencies introduced can create vulnerabilities that are difficult to identify and safeguard. These vulnerabilities may be due to unintentional failures (e.g., faulty or aging components) or to intentional actions (e.g., terrorism, cyber-warfare, etc.). Once a failure occurs, it can cause cascading failures in other systems and infrastructures due to interconnections.

While there has been a great deal of work investigating the resilience of interconnected infrastructures, developing policies to manage and protect critical infrastructure requires consideration of enterprise level concerns. More specifically:

  •  There is no locus of control
    • Each sector of critical infrastructure is overseen and regulated by a different federal agency
    • Private firms manage different parts of the various infrastructure networks
  • There is significant adaptive behavior
    • Terrorists may adapt to different strategies to protect infrastructure
    • Populations adapt to infrastructure outages and potential outages
  • There is significant complexity
    • The market is a significant driver of participant behavior
    • There are multiple interconnected infrastructure systems that interact, with sometimes unpredictable effects

To investigate the impact these enterprise level issues have on policies to protect critical infrastructure, a collaborative effort by researchers at the Stevens Institute of Technology and the Georgia Institute of Technology led to the development of a critical infrastructure policy simulator.

More information can be found in the technical report here:
Project Technical Report

Specific findings will be presented in forthcoming publications.