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February 15 - February 19, 2024
Moreover, fat-tailed distributions, not normal distributions, are typical within complex systems, both natural and human, and we all live and work within increasingly complex systems, which means increasingly interdependent systems.
Projects that fail tend to drag on, while those that succeed zip along and finish.
Planning is a safe harbor. Delivery is venturing across the storm-tossed seas.
“Lock-in,” as scholars refer to it, is the notion that although there may be alternatives, most people and organizations behave as if they have no choice but to push on, even past the point where they put themselves at more cost or risk than they would have accepted at the start.
“commitment fallacy.”
“strategic misrepresentation,” the tendency to deliberately and systematically distort or misstate information for strategic purposes.[5] If you want to win a contract or get a project approved, superficial planning is handy because it glosses over major challenges, which keeps the estimated cost and time down, which wins contracts and gets projects approved.
“You want the flight attendant, not the pilot, to be an optimist.”
Unchecked, optimism leads to unrealistic forecasts, poorly defined goals, better options ignored, problems not spotted and dealt with, and no contingencies to counteract the inevitable surprises. Yet, as we will see in later chapters, optimism routinely displaces hard-nosed analysis in big projects, as in so much else people do.
When people are asked to make a “best-guess” scenario—the scenario most likely to occur—what they come up with is generally indistinguishable from what they settle on when asked for a “best-case” scenario.[20]
Don’t assume you know all there is to know. If you’re a project leader and people on your team make this assumption—which is common—educate them or shift them out of the team. Don’t let yourself or them draw what appear to be obvious conclusions.
Thus, slow is a consequence of doing planning right, not a cause. The cause of good planning is the range and depth of the questions it asks and the imagination and the rigor of the answers it delivers.
Projects are not goals in themselves. Projects are how goals are achieved.
At the beginning of a project, we need to disrupt the psychology-driven dash to a premature conclusion by disentangling means and ends and thinking carefully about what exactly we want to accomplish. Frank Gehry’s question, “Why are you doing this project?,” does that.
Picture politicians who want to connect an island to the mainland.
they started with an answer—a bridge is the best solution—and proceeded from there. If they instead explored why they want to connect the island to the mainland—to reduce commuting time, to increase tourism, to provide faster access to emergency healthcare, whatever—they would focus first on the ends and only then shift to discussing the means for achieving those ends, which would be the right order of things.
Developing a clear, informed understanding of what the goal is and why—and never losing sight of it from beginning to end—is the foundation of a successful project.
What sets good planning apart from the rest is something completely different. It is captured by a Latin verb, experiri. Experiri means “to try,” “to test,” or “to prove.” It is the origin of two wonderful words in English: experiment and experience.
First, iteration frees people to experiment, as Edison did with such success.
Second, the process ensures that literally every part of the plan, from the broad strokes to the fine details, is scrutinized and tested. Nothing is left to be figured out when the project goes into delivery. This is a basic difference between good and bad planning.
Third, an iterative process such as Pixar’s corrects for a basic cognitive bias that psychologists call the “illusion of explanatory depth.”
“People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do,” researchers concluded.
But researchers also discovered that, unlike many other biases, there is a relatively easy fix: When people try and fail to explain what they mistakenly think they understand, the illusion dissolves.
That brings us to the fourth reason why iterative processes work, which I touched on in chapter 1: Planning is cheap.
Whatever can be done in planning should be, and planning should be slow and rigorously iterative, based on experiri.
It is an active process. Planning is doing: Try something, see if it works, and try something else in light of what you’ve learned. Planning is iteration and learning before you deliver at full scale, with careful, demanding, extensive testing producing a plan that increases the odds of the delivery going smoothly and swiftly.
When a minimum viable product approach isn’t possible, try a “maximum virtual product”—a hyperrealistic, exquisitely detailed model like those that Frank Gehry made for the Guggenheim Bilbao and all his buildings since and those that Pixar makes for each of its feature films before shooting.
Planners don’t value experience to the extent they should because they commonly suffer yet another behavioral bias, “uniqueness bias,” which means they tend to see their projects as unique, one-off ventures that have little or nothing to learn from earlier projects.[5]
The consensus of researchers today is that, yes, being first to market can confer advantages in certain specific circumstances, but it comes at the terrible cost of an inability to learn from the experience of others.
Better to be—like Apple following Blackberry into smartphones—a “fast follower” and learn from the first mover.
“tacit knowledge.” We feel tacit knowledge. And when we try to put it into words, the words never fully capture it. As Polanyi wrote, “We can know more than we can tell.”
But practical wisdom, the wisdom that enables a person to see what’s right to do and get it done, requires more than explicit knowledge; it requires knowledge that can be gained only through long experience—a view supported by Michael Polanyi and a great deal of psychological research 2,300 years later.
When planning, remember the Latin word experiri, the origin of the English words experiment and experience. Whenever possible, planning should maximize experience, both frozen and unfrozen.
The mistake the planners made is as basic as it is common: When we experience delays and cost overruns, we naturally go looking for things that are slowing the project down and driving up costs. But those delays and overruns are measured against benchmarks. Are the benchmarks reasonable? Logically, that should be the first question that is asked, but it rarely comes up at all.
Once we frame the problem as one of time and money overruns, it may never occur to us to consider that the real source of the problem is not overruns at all; it is underestimation.
To create a successful project estimate, you must get the anchor right.
The cultural anthropologist Margaret Mead supposedly told her students, “You’re absolutely unique, just like everyone else.” Projects are like that. Whatever sets a project apart, it shares other characteristics with projects in its class.
Kahneman and Tversky dubbed these two perspectives the “inside view” (looking at the individual project in its singularity) and the “outside view” (looking at a project as part of a class of projects, as “one of those”). Both are valuable. But they’re very different.
After all, each of the nasty surprises I imagined is unlikely. That is true. But even with a project as simple as a kitchen renovation, the number of possible surprises, each unlikely, is long. Many small probabilities added together equal a large probability that at least some of those nasty surprises will actually come to pass. Your forecast did not account for that.
See your project as one in a class of similar projects already done, as “one of those.” Use data from that class—about cost, time, benefits, or whatever else you want to forecast—as your anchor. Then adjust up or down, if necessary, to reflect how your specific project differs from the mean in the class.
Define the class broadly. Err on the side of inclusion. And adjust the average only when there are compelling reasons to do so, which means that data exist that support the adjustment. When in doubt, skip adjustment altogether. The class mean is the anchor, and the anchor is your forecast.
In the social sciences, “survivorship bias” is the common mistake of noting only those things that made it through some selection process while overlooking those that didn’t.
“negative learning”: The more you learn, the more difficult and costly it gets.
Modularity is a clunky word for the elegant idea of big things made from small things.
“scale free,” meaning that the thing is basically the same no matter what size it is. This gives you the magic of what I call “scale-free scalability,” meaning you can scale up or down following the same principles independently of where you are scalewise, which is exactly what you want in order to build something huge with ease.
What is our basic building block, the thing we will repeatedly make, becoming smarter and better each time we do so? That’s the question every project leader should ask.
The pattern is clear: Modular projects are in much less danger of turning into fat-tailed disasters. So modular is faster, cheaper, and less risky.
But who should pick the team? Ideally, that’s the job of a masterbuilder. In fact, it’s the masterbuilder’s main job. This is why the role of masterbuilder is not as solitary as it sounds; projects are delivered by teams.
Good planning boosts the odds of a quick, effective delivery, keeping the window on risk small and closing it as soon as possible.
Think of your project as “one of those,” gather data, and learn from all the experience those numbers represent by making reference-class forecasts. Use the same focus to spot and mitigate risks. Switching the focus from your project to the class your project belongs to will lead, paradoxically, to a more accurate understanding of your project.
For fat-tailed risk, which is present in most projects, forget about forecasting risk; go directly to mitigation by spotting and eliminating dangers.
