As Chip and Nancy put it, for people afflicted with component focus, “wholes are not the ‘sum of their parts,’ they are a function of one part.” The deeper a person’s expertise, the worse this narrow focus gets. Chip and Nancy show how “the curse of knowledge” accentuates the coordination troubles caused by component focus: Experts wrongly assume that—because a subject comes so easily to them after learning about it for years—what they know is obvious and can quickly be grasped by others. Experts unwittingly create coordination snafus by failing to pass along essential information to people in other positions and fields because they assume it is self-evident. Or, when they try to pass information along, experts provide explanations they believe are easy to understand but are incomprehensible to people who aren’t indoctrinated into their circle.

Barry’s attack hits home. I had once been an engineer, and I know an engineer doesn’t design a bridge that might hold its load. An engineer starts with complexity and crafts certainty. I knew what it was like to be careful, to balance literally thousands of considerations in making a system work.

What Wiseman noticed that day can be seen as a vital element of TPS: a deeply ingrained belief that problem-solving is a team sport. Failures are opportunities for improvement. Competent professionals are expected to successfully execute most of their tasks, so successes are not seen as worthy of colleagues’ valuable time. Hence the “puzzled” look on Mr. Cho’s face. Puzzlement occurred because an expected behavior (share your problems so we can work on them together) didn’t happen, while an unexpected one (bragging) did. What I love most about this story is that Wiseman’s boasting would not have raised an eyebrow in 99 percent of work environments I’ve studied. We are socialized to share accomplishments and good news in front of the boss. Nothing puzzling about it! The most impressive result of TPS in my view is that the system normalizes failure—bad news, requests for help, and problems alike. It creates a community of scientists. Not incidentally, the essence of failing well is thinking like a scientist.

2. Stafford Beer

Later on, Beer would identify it as one of his fundamental axioms: ‘It is not necessary to enter the black box to understand the nature of the function it performs.’ The underlying idea is somewhat stronger than this: if the black box is a complex system, it’s likely to be pointless — or even dangerous — to try to understand its inner workings and use that understanding to manipulate a precise outcome. This is a matter of respecting the complexity of the problem — a genuinely complex system is one in which you cannot hope to get full or perfect information about the internal structure, and cannot have any acceptable degree of confidence that the bits of information you don’t have can be safely ignored. Rather that trying to use a mixture of partial information, preconceived theory and guesswork, you need to step back, accept that the system will keep its secrets, and observe its behaviour. And of course, this means that the different observers might have different opinions. The property of ‘being a black box’ isn’t an objective one — it’s a description of a decision taken by someone working on the system that they don’t have enough understanding to safely treat it in any other way. In some of Beer’s writings, the property of complexity itself is also dealt with in this way; rather than trying to count combinations of connections, you define what it is for something to be a complex system by saying that it’s one which has to be treated as a black box.*

*if you can control something in your factory like a machine, it’s a ‘clear box’ in this language; there are even ‘muddy boxes’, where more detailed analysis could help understand the relationship between inputs and outputs but the system keeps a few secrets because really detailed analysis would require disproportionate effort.

Trying to build things meant that everything had to be kept specific, but the theory of information couldn’t straightforwardly be applied to massively connected systems. You needed to find a way of describing things that was both rigorous and representative of reality. As economists will tell you, this isn’t an easy thing to do.