Project Controls for an Unknowable Future

 

by Lynda Bourne

One thing we learned from 2020 is that it's difficult to make predictions, especially about the future. (Danish physicist Niels Bohr’s words from 1971 still ring true.) Still, the world doesn’t stand still, and project managers need to keep looking ahead. That’s the whole purpose of a project controls function: to produce information that helps us make decisions about the future.

In many respects, project plans (schedules, budgets, etc.) are similar to economic forecasts. For decades, both have been used to make predictions more academically rigorous through mathematical techniques. The problem is these models are suited to the stationary physical world, where everything that happens is governed by the unchanging laws of physics—or to games of chance, in which the probability of something happening can be calculated fairly easily and accurately. They do not neatly apply to the intricacies of a dynamic project or economy.

Two leading British economists, professor John Kay of Oxford University and professor Mervyn King, a former governor of the Bank of England, recently launched a critique of the unrealistic assumptions their peers have added to conventional economics in the book, Radical Uncertainty: Decision-Making for an Unknowable Future.

Their view is that making predictive models more mathematical does not improve the accuracy of the predictions. The models assume the decision-maker and all of the other actors will follow the logic underpinning the model. But we all know the people being modeled do not behave rationally and rarely, if ever, actually work to the plan.

Kay and King call this type of modeling “small world,” as the right and wrong answers can be clearly identified. Projects (and economies) operate in a “large world” occupied by consumers, businesses and government policymakers, and characterized by what they call “radical uncertainty.” People in the large world have to make decisions based on a small part of the information actually needed about both the present and the future. Most of the time we can never really know if we made the best decision, even after the event.

Fortunately, like Alice in Wonderland facing the appearing and disappearing Cheshire cat, people are very good at coping with uncertain situations. And it’s amazing how often we get it right. Kay and King conclude: “Our knowledge of context and our ability to interpret it has been acquired over thousands of years. These capabilities are encoded in our genes, taught to us by our parents and teachers, enshrined in the social norms of our culture.” Human intelligence is effective at understanding complex problems within an imperfectly defined context, and at finding courses of action that are good enough to get us through the remains of the day and the rest of our lives. They are not necessarily the best solutions, but they’re ones that are good enough.

So where does that leave project controls? We have predictable tools such as earned value, critical path and the like built on the basis of predictable calculations. Unfortunately, these calculations are rather bad at accurately predicting actual future outcomes. But is the imprecise information useful?  

My thinking is that control tools can provide useful insights, but only if you accept there will always be a difference between the prediction and reality as the future unfolds.

How do you use project controls to chart paths forward into the unknown?