Complexity, Risk & Uncertainty

gremToday’s “Gremlins” — Complexity & Uncertainty

Consider an airline that has fine tuned its daily flight schedule and passenger seat pricing with yield management techniques. The strategy is set to optimize asset deployment and return on investment for a flight. And that works very well until the system becomes destabilized .

One or more planes become inoperable as they experience unexpected equipment failure and are grounded. Depending on a number of factors, like degree of connectivity and inter-dependency, you might see phenomena called “cascading failure” as the failure of one “node’ (or the grounding of one airplane) cascades through the system scrambles connecting flights and strands ticket holders awaiting the grounded airplane.

The chief scientist for a corporation that pioneer airline yield management described the problem: “John, I made a fortune for airlines through incremental increases in pricing and scheduling strategies over the years. The result was a very fragile system.”

You will see similar examples of these phenomena in the electric grid, the financial market (systemic risk), and in the spread of contagion through pandemic, counterfeits entering the supply chain or adulterated food in the supply chain.

networks[1]And as you might guess, network theory and other tools in our complexity tool box can provide insights and “what if” alternatives to re-wiring highly complex time dependent systems such as yield management systems. The pertinent question is no longer focused on optimality; rather, the focus shifts to expediency and survivability. The beauty of network theory is that it can add value to describing an enormous number of problems and opportunities.

The same idea holds for commercial aviation, the layout of cities and its use in urban planning, supply chain applications are almost infinite, and its military applications (some of which business can borrow) are the foundation of “netcentric operations” where real-time communications link levels of command on the battle field. And as you might surmise, network theory is the foundational theory for communication systems and, more recently has been incorporated in to social media analysis.

As described by Barbara Tucuman in The Guns of August, once German mobilization for World War One began, an activity that was inexorably tied to precise and highly interdependent actions synchronized with the German railroad operators, it could not be stopped without throwing the entire mobilization into chaos taking weeks to recover. In the mean time leaving Germany helpless to respond to any attack. Once they started to mobilize there was, literally, no turning back without complete and utter confusion. War was inevitable.

Path dependency is a phenomenon that occurs when there is no physical or intellectual slack is built into the system. “In the strongest version of path dependencies, path transformation is presumed to be highly unlikely except through rare radical ruptures or re-orientations, which are associated with violent external shocks.”

Are there theories and tools to help overcome path dependency risk and the other risks associated with highly connected and interdependent systems?

 

Into The Matrix – Adding new tools and perspectives

graphic two

I developed this matrix to match the right tool or approach to levels of complexity and uncertainty. One of the things we see with successful consulting firms is the tendency toward a certain path dependency of their own when it comes to preferences in models and approaches.

There is an overwhelming bias toward focusing our problem solving approaches inside the red circle of The Matrix. The tools inside the red circle, e.g. cost-benefit, linear programming, ROI, predict-then-act models etc., do not work in environments exhibiting high levels of complexity and uncertainty, e.g.. complex adaptive systems (aka CAS). They work for the industrial engineer dealing with relatively simple systems designed and run by human beings. The value of the matrix is that it puts methods, and client expectations into a realistic perspective

I built this model for the Department of Homeland Security, Science & Technology Directorate, who face extremely high levels of complexity and uncertainty (aka “wicked problems”).

wicked

Wicked problems can only be successfully tamed by moving outside your comfort zone, going beyond the red circle.

Closing thoughts…

First, increasing complexity is not a passing fad. It is the new norm and it continues to produce high levels of uncertainty and change that are challenging the basic assumptions that made us successful in the past.

Second, change is not continuous or smooth. It comes in bursts. It is like a house cat that sleeps most of the day and then, suddenly starts “bouncing off the walls” That’s why we are so interested in agility and resilience.

Third, the market is brutally efficient. As the prevailing paradigm fails it will be replaced by new norms, assumptions, values and expectations. What doesn’t work is replaced – that’s evolution. That holds true for business models, political constitutions, and consulting firms. The irony is that while CAS are inherently unpredictable, there is a deterministic certainty that we can’t avoid complexity, and that we need to build in resilience in our systems/businesses if we want to make the evolutionary “cut.”

Forth, all risk is transitive. If you are at risk on the Internet, and I depend on you, then I too am at risk.

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2 thoughts on “Complexity, Risk & Uncertainty

  1. Pingback: The Foundational Models Thinking About Complexity, Risk & Resilience (Part 1) | Prologue

  2. Pingback: The Foundation Models for Thinking About Complexity, Risk & Resilience (Part 2) | Prologue

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