The renowned author Malcolm Gladwell has a book titled “What the Dog Saw” which is a compilation of his New Yorker Magazine articles. One of these articles (Open Secrets: Enron, intelligence, and the perils of too much information, The New Yorker, January 8, 2007) intrigues me with how it applies to engineering management. In this piece he describes, based on Gregory Treverton’s work, Puzzles vs. Mysteries. He applies these terms to various situations such as analyzing information on the location of a terrorist or the collapse of Enron. A puzzle has a specific solution that we are trying to “find”. With more data or the right data, one can solve a puzzle. A mystery on the other hand is unsolvable. We can only make educated guesses as to what is behind a mystery. For example, the future cannot be definitively determined no matter how much data we gather. Our predictions may become better with more data but it is still a probabilistic issue whereas a puzzle, with enough data, can be definitively solved.
From an engineering perspective I would say Gladwell’s argument is similar to saying that a puzzle is deterministic whereas a mystery is stochastic.
The search for an individual is a puzzle. They exist in a specific location but you do not know where that is. The more data you collect about the individual (think Osama Bin Laden or a fugitive) the closer you are to determining their location. The data collected electronically attempts to narrow down their whereabouts to a particular region, making the search much more tractable. This vastly limits the data needed and the search can move into a different type of data collection; direct surveillance for example.
Gladwell argues that Enron may have been more of a mystery than a puzzle but we have treated it like a puzzle. That is, Enron’s activities had many open-ended and uncertain outcomes. The leadership did not attempt to hide information that would have solved a puzzle for the investors and thereby would have uncovered a house of cards. There is no doubt that the leadership pushed boundaries but it seems they were not illegally hiding information that would help solve the Enron puzzle. Rather, perhaps the financial situation was so complex that it was a mystery – the outcome was uncertain regardless of how much information was available. Predicting the future of Enron would have been probabilistic, even if you had all the information that came out in the trials.
Regardless of what you feel about Enron or the words “Puzzle” vs. “Mystery”, there are some learnings for us here in the context of engineering management. As managers or as educators, our goal is to take the mindset of the early career engineer and move it from a classroom orientation to a leadership and customer orientation. Doing this includes moving:
- from closed-ended problems to open-ended problems
- from solving problems to defining problems
- from gathering more data to working with imperfect data
- from technical solutions to customer solutions
I think this is similar to moving from a puzzle mindset to a mystery mindset. This is exactly what is needed as we move from a classroom problem-solving orientation to a real-world leadership and customer orientation. People are complex and messy. There’s no deterministic solution to most problems that require or involve employees. Even customers are known to misunderstand what they need. I think it’s fair to say we are moving from a deterministic to a stochastic mindset as we become more adept at working with people and real world engineering issues. Perhaps the trick is to do that while simultaneously treating the people as real, important, feeling individuals; not a set of probabilities. That is called empathy and is a major part of leadership. But that’s a discussion for another day.
