Meticulous research, tireless reiteration of core concepts, and passion for the topic. You didn't ask, but if you had done that'd be what I'd say about the writing of Matthew Syed based on You Are Awesome — reviewed here a few months back — and now also Black Box Thinking.
The basic thesis of the latter is captured nicely in a blog post of his from last year:
Black Box Thinking can be summarised in one, deceptively simple sentence: learning from mistakes. This is the methodology of science, which has changed the world precisely because it is constantly updating its theories in the light of their failures. In a complex world, failure is inevitable. The question is: do we learn, or do we conceal and self-justify?Who wouldn't want to learn from their mistakes, you might ask? Lots of us, it turns out. The aviation industry tends to come out well in Syed's analysis. Accidents, mishaps, and near-misses are reviewed for ways in which future flights might be less likely to repeat them, and the knowledge is shared across the board. Blaming is minimised in order that all participants are encouraged to share their evidence and thoughts.
The medical and healthcare industries, and also politicians, tend not to do so well. In these areas, blame culture and a fear of reprisals are said to hinder the extent to which mistakes are admitted to, investigated, and subsequently mitigated.
Atul Gawande's The Checklist Manifesto makes similar points, and prescribes the use of checklists as one way to mitigate the future risk. Syed spends a lot of time on the ways in which cultural changes in philosophy, mindset, and practice, need to be made in order to get to a point where the risks are identified, accepted, and then provoke some kind of positive action.
There's so much material packed so densely into this book that I can't do it justice here. In lieu of that, here's some of the entwined key threads as I saw them:
- We live and work in complex systems
- ... where failures will happen.
- A blaming culture is likely to result in lower visibility of issues and more ass-covering
- ... whereas open cultures encourage and support self-reporting.
- A "production" failure should be seen as a learning opportunity
- ... and a chance to reduce the risk of future such failures.
- Use "development" failure as a tool
- ... particularly within an iterative development environment.
- Expertise comes from practice and feedback
- ... but a mixture of theory and practice helps avoid local maxima.
- A fixed mindset is less likely to innovate
- ... and broadening our outlook makes creative connections more likely.
- On the whole, we prefer narrative over data
- ... and when beliefs and data disagree, we tend to deny the data.
- Understanding what to measure and record is key
- ... and sometimes it's sensible to experiment to understand what to measure.
This complexity is itself hinted at in the list: there are many variables at play, and they are interconnected. There is generally no silver bullet, no quick-fix, no one size to fit all. On this point, in a particularly nice meta twist, Syed notes that the approaches espoused for learning, say, how to build a product can also be used on the approaches themselves — in order to learn better how to build, perhaps we first need to learn better how to learn.
On learning then, three things that I'm taking away from this book.
I have historically been sceptical when I hear people blithely say that we learn more from failure than success. Out of context, I still don't believe that's necessarily a given but I think perhaps now I have more nuanced thinking here.
First, using a generate-and-test approach in development, and treating each generation that doesn't improve our test metric a failure, we might say that the volume of failure drives our learning more than the final success. Syed gives the example of James Dyson who made thousands of incrementally different prototype vacuum cleaners before arriving at his first production model. Thousands of failures, each of which helped to point the way to success.
Alternatively, I wonder whether it might mean that that analysis of the differences between success and multiple failures allows us to understand the factors important to success in a way that simple (ahem!) success does not.
Also new to me, and hidden in a footnote (p. 172), there's an interesting term:
"Observational statistics" is a phrase that encompasses all the statistics drawn from looking at what happened. Randomised control trials are different because they encompass not merely what happened, but also construct a counterfactual for comparison.That counterfactual is key; it helps to balance survivorship bias. A well-known example comes from the second world war: deciding where to add armour to planes based on where there are bullet holes in those that returned to base is to miss the massive value of the unobserved data. Those that got shot down and never made it back might well have been hit elsewhere. (For a brief summary see e.g. Mother Jones.)
Another footnote (p. 220) raises an interesting potential tension that I realise I've been aware of but perhaps never surfaced before:
Getting the manufacturing process running seamlessly is often about ironing out unwanted deviations. It is about using process controls and the like to reduce variation. Creative change is often about experimentation: in other words, increasing variation.Sensitivity to variability, to the unknown, should be adjusted consciously based on the context in which we are operating. More frequently, it appears to me, we have a relatively fixed level of comfort which can compromise our ability to operate in one or other of the scenarios that Syed identifies.
Black Box Thinking, despite the repetition due to the interconnectedness of the ideas it puts forwards and despite its sardine tin consistency, is a book worth persevering with. It's helped me to both learn and reflect on many concepts I've been thinking about for some time myself. Here's a few:
- The inevitability of failure in complex systems
- Theory and practice
- Observed and unobserved data
- The value of iteration
- Meta testing
- Checklists
- Background information for connections
- Where creativity comes from
- Treating ideas as bad to provoke better
Image: Amazon