I didn’t find this book at ROLI, but Matthew Syed’s everywhere these days and this one looked like it might be worth a glance.
Matthew Syed is a tall man with no hair. His book is full of this kind of observation that never goes anywhere: the muscular build of a bereaved man; the hairstyle of a pilot who committed suicide. Black Box Thinking luxuriates in glib journalistic dazzle. In one instant, we are forensically deconstructing the political aftermath of a medical accident; in the next, the writer’s attention alights on a widower’s eyes, welling as his tapering fingers tremble. This is modern self-help in Dale Carnegie’s image: recounted with a ferocious zeal that that feels claustrophobic and contrived, and presented with a chattiness that sits poorly with the importance and tragic flavour of its material. It occasionally verges on voyeurism but, where a sting of criticism might hit home, it is immediately emolliated by condescension. After reading of a surgeon whose tyranny nearly killed somebody, we are reminded not to forget that even a doctor who, in an instant of hubristic idiocy, almost kills a patient, and then obstructs attempts to investigate procedural errors, is nevertheless a hero. They are all heroes. Atul Gawande trod this ground years before Syed, and did it better: directly; sincerely; sensitively.
Syed’s book is a poor recruiter for a great employer. At its core is a simple, powerful and useful message, but some flaws are unforgivable. Central to a writer’s integrity is a clear and honest use of words. You cannot, in one paragraph, tell your readers that they must learn a new subject and, in the next, treat the subject’s core vocabulary with a lazy disdain. Syed earnestly understands that there would be a better world if the scientific method were more widely understood and better applied; if objective truth were served as slavishly as the will to power. He wants his friends to know this too. But remember, journalist, that we engineers will continue to practise and hone our trade in our tiny rooms long after your friends follow the siren call of something more lucrative. Our tools and our knowledge are ours: do not abuse them.
Syed refers to ‘open-loop’ and ‘closed-loop’ thinking in a way that, for no good reason, inverts the established meaning of these terms. Hence, a ‘closed-loop’ system which, to millions of us with a modicum of technical training, is something that is ‘closed’ by a path that provides corrective feedback, is now ‘closed’ in the sense of ‘guarded against feedback and the influence of evidence’. Did anybody edit this book?
Lesson one: collect data about everything you’re doing. The title ‘Black Box Thinking’ refers to the two data recorders that capture the cockpit voice and telemetry in aircraft, so that crashes and near-misses can be better understood. Dispassionate forensic analysis of this data provides vital information about what went wrong.
Lesson two: depersonalise this information, and don’t use it to shame people. The fear of shame leads to the deliberate concealment of errors, so everybody loses opportunities to learn. Humans are fallible under stress, and the first duty of a crash investigator is to improve flight safety. Before critical failures, there are near-misses, and people must be allowed to report and challenge these without fear. The exemplary attitude in aviation allows mechanical problems to be caught at an early stage. Best practice is also improved in the cockpit. In-flight checklists control the narrowing of a pilot’s concentration under stress; improved human factors fix problems with the flying controls; Crew Resource Management addresses the psychological difficulties of cockpit hierarchy. This is why, as we know, civil aviation becomes safer even as aircraft become more complicated.
Lesson three: learn by building, make marginal gains, iterate often, create theories and try to falsify them. Syed summarises with unusual concision, ‘If I want to be a great musician, I must first play a lot of bad music. If I want to become a great tennis player, I must first lose lots of tennis games. If I want to become a top commercial architect known for energy-efficient, minimalist designs, I must first design inefficient, clunky buildings.’ Prepare to produce a lot of dross on the road to success. All performers are poor at first; nobody gets great without a lot of practice. Solicit feedback from customers at a really early stage, when you’re still a bit embarrassed by your product: you’ll learn if you’re doing a really great job designing the wrong thing.
On the subject of iteration, there is another use of the term ‘Black Box’ that is more commonly employed by engineers. A Black Box model is one in which a system is characterised merely from measurement of its inputs and outputs without attempting to understand the reasons for this relationship. This might have been woven into the central chapters on evolution and marginal gains. Here, in many places, it would have bolstered the book, but it didn’t. The dual meaning is dismissed in a footnote on Page 33 and never mentioned again.
In a central chapter, Syed notes that Unilever employed physicists and biologists to approach a difficult nozzle optimisation problem from two directions. This nozzle must create detergent granules by firing a hot, pressurised liquid into air where it solidifies and lands as a correctly-sized powder. First, as Syed narrates it, physicists tried to characterise how the nozzle worked by modelling the flow of fluid through it. Their failure to build a successful working model highlighted the intractability of the problem. A team of biologists then successfully optimised the nozzle with a typical ‘black box’ approach: starting with an existing, poorly-functioning prototype; measuring the powder, tweaking the nozzle design, and iterating the best-performing candidates over dozens of generations; finishing when it was as good as it was going to get. Hundreds of prototypes later, the ‘black box’ approach worked, and Syed narrates this as a victory for the empirical, evolutionary approach. Dyson, who created thousands of iterations of vacuum cleaner to arrive at the first commercial prototype of his dual cyclone, also finds himself press-ganged into Syed’s war. Take that, physicists!
Had Syed been a scientist — had he taken his own advice — he would have seen this story as more than a battle between practices. Both teams’ methods are in alignment with scientific best practice: each collected data and analysed it and approached a truth. Some physicists were attempting to develop a theory that solves the general case and failed. Some biologists set out to attack the specific case and succeeded. My conclusions are:
- Failure informed the approach that led to success, as it often does. Failure’s a great teacher, but a slow and expensive one.
- Changing tactics saved the project, at the cost of limiting scope. The price of a solution was paid by abandoning a general understanding of the problem.
So Unilever have a brilliant nozzle, and the method that produced it, but they’ll never know why it works or whether there’s an even better one. The only way to double its capacity or change the formula of their fluid is to make a hundred more prototypes.
Lesson four: understand and eliminate cognitive dissonance. Resist the temptation to spin failure as a success, or deny that something went wrong. Accept such failures as an opportunity to learn and improve.
If you’re involved in a technical discipline, you’re already a servant of hard physical truths, and no amount of post-event rationalisation excuses a non-working prototype. (Although, if you’re building Mars landers for the European Space Agency, it seems you can crash-land as many as you like, act as though you succeeded, and continue to get funding, but I’m talking about real jobs.)
An external perspective of scientific method will help a wider audience to understand it. There are certainly pickings in this book for technical readers too, but it’s principally for an audience who don’t get, or even seek, the same class of feedback from their work that a technologist will. Recommend it to your boss. Next time you have a corridor conversation, though, remember that ‘closed-loop’ is open-loop, ‘open-loop’ is closed-loop, and ‘black-box’ means collecting and responding to data. Or ‘science’.