Digital Engineering & Decision Modeling: Winning the New Telephone Game

Digital Engineering & Decision Modeling: Winning the New Telephone Game

What if the linear childhood game you played became multidimensional?

Kenneth Running

The traditional systems engineering process can be likened to the "telephone game," which you probably played as a kid.

Participants line up, and the first one whispers a phrase in the ear of the next person and so on. When the last participant yells out the heard phrase, everyone finds out how distorted the phrase had become. Everyone inevitably snickers and giggles when they find out "My mom mopped the motel" turned into "Mimes stopped caring that they smell."

While the telephone game is fun, complex systems engineering is a serious and multidimensional challenge.

Let's now change the rules of the game a little bit. The game instructor asks each participant a different question about the message at the end of the game. Each participant could only communicate what it thinks is important in answering the question. Finally, each one can only relay its message to those within reach.    

The engineering of complex systems is like the telephone game's challenge in 2020, requiring all participants to know the original message. This is especially important, because the interests of each participant are different, operating in different disciplines while collaborating on the system. If there isn't a central anchor, it is easy to imagine that over time, multiple and conflicting messages will be circling around.

A digital engineering strategy combats this by moving the participants into an ecosystem that allows them to more openly share systems engineering information the message and, more important, curate from that information to fit within the context of their individual needs.

How digital engineering optimizes decision modeling

When we talk about connecting and exchanging information from one party to another, we recognize that our real purpose is to drive sound decision-making. Decision modeling provides a means to understand how each decision impacts program requirements, risks, and opportunities that may then lead to another decision and so on.

Decision modeling forms the basis for a trade analysis where you could examine a gain in one benefit that might result in a loss of another. If you replace X, how does it affect Y? Or, how does the totality of factors X, Y, and Z contribute to mission effectiveness?

Most decisions I deal with are not simple and can have millions or even billions of outcomes. My job involves hundreds of complex “what if?” questions that I can’t efficiently answer without leveraging digital engineering strategies.

Digital engineering maps out the hand-offs between parties and reduces the errors in the process. When I think about how replacing a legacy system architecture will impact an entire organization’s capabilities, I use digital engineering-aided decision modeling to rapidly weigh the pros and cons.


Decision modeling provides a means to understand how each move impacts downstream program requirements, risks, and opportunities that may impact the next move and so on.

What this means for our customers

Recently, a customer needed a performance analysis of its hypersonic defense. We modeled six classes of defenders and ran four billion different Monte Carlo simulations. This provided a high-quality analysis that started the agency on the right path for designing a hypersonic defense architecture.

The agency now understands its strengths, weaknesses, opportunities, and threats in ways it couldn’t through other assessments. Without quick response modeling, decision modeling, and digital engineering, you couldn't even get one iota of that type of in-depth analysis done in rapid fashion.

If one of our customers wants to launch a new satellite constellation and see broad implications of something as general as orbit, it can start with model-based systems engineering (MBSE) architecture. But before long, once the customer identifies needs, we must conduct quick response analyses to properly understand how to meet those needs.

What this means for the industry

Decision modeling determines the right fidelity to meet the mission's needs as new threats constantly emerge. Without a digital engineering paradigm, we will talk about the impact of decisions, discuss what threats we face, inevitably leave some information out, and come to conclusions based on pared-down PowerPoint presentations. The analogous telephone game would persist, and the line leader would wonder how his words became so distorted.

With our digital engineering strategies of quick response and decision modeling, errors are practically eliminated and lead times are cut into fractions. Digital engineering enables our customers to be more informed and move our nation's warfighters forward at the speed of relevance against fast-evolving threats.

Posted by: Kenneth Running

Modeling and Simulation Engineer Manager

Kenneth Running II is modeling and simulation engineer manager in the digital engineering practice within SAIC’s Strategy, Growth, and Innovation group. Running manages programs, oversees direct technical work, and manages complex simulation development programs. He leads SAIC’s modeling and simulation enterprise modernization through research and development and coordination with stakeholders, developing transformative infrastructure, tools, and techniques.

Running has extensive technical understanding of advanced warfighting architectures and ballistic/hypersonic missile defense system architectures and experience in the development of countermeasures and fire control solutions. He has been with SAIC since 2007, working in various capacities within the fields of aerospace, engineering, and modeling and simulation.

Running has led several teams and departments to develop models and simulations for Department of Defense customers. He has been responsible for technical analysis of sensor options, rules of engagement, concepts of operations, weapons, and hypersonic systems. He has been published in IEEE Transactions on Automatic Control, a peer review journal.

Running double majored at North Carolina State University, earning a bachelor’s degree in both applied mathematics and aerospace engineering. He earned his master’s degree in aerospace engineering from the University of Maryland, College Park.

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