Introduction:

“First and foremost, thank you for opening this book. I hope you enjoy reading it as much as I have enjoyed living, learning, and gathering it.

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.

Stafford Beer’s cybernetics tells us that in these cases, while people’s opinions are important, the facts of the organisational outcomes are what we need to work with. In his most pithy formulation of the principle, he expands the black box principle to a rather more uncomfortable statement.

The purpose of a system is what it does.

Stafford Beer takes an approach to this problem that helps to mark the distinction between information theory and cybernetics. That solution is to say that information and action are one and the same; variety coming in from the environment, or being transferred from one system to another, only counts as ‘information’ if it has a causal role in decision-making. Otherwise it’s just ‘data’ — collections of facts that hang around on disk drives, waiting to be erased* or for the format to become obsolete.

*There are a lot of people is Silicon Valley who might do well to consider how much money they have invested in ‘data’ without bearing this distinction in mind.

Among many works detailing the state of management science, I’d recommend Management Studies in Crisis: Fraud, Deception and Meaningless Research by Dennis Tourish (Cambridge University Press, 2019) and Nothing Succeeds like Failure: The Sad History of American Business Schools by Steven Conn (Cornell University Press, 2019). It is a bit of an indictment of things that H. Thomas Johnson and Robert S. Kaplan’s Relevance Lost: The Rise and Fall of Management Accounting (Harvard Business School Press, 1987) is still itself every bit as relevant today as when it was published.