Some analogies are so useful that they don’t merely shed light on a concept, they actually become platforms for novel thinking. For example, the metaphor of the brain as a computer has been central to the insights generated by cognitive psychologists during the past fifty years. It’s easier to define how a computer works than to define how the brain works. For this reason it can be fruitful for psychologists to use various, well-understood aspects of a computer—such as memory, buffers, or processors —as inspiration to locate similar functions in the brain.

Good metaphors are “generative.” The psychologist Donald Schon introduced this term to describe metaphors that generate “new perceptions, explanations, and inventions.” Many simple sticky ideas are actually generative metaphors in disguise. For example, Disney calls its employees “cast members.” This metaphor of employees as cast members in a theatrical production is communicated consistently throughout the organization:

• Cast members don’t interview for a job, they audition for a role.

• When they are walking around the park, they are onstage.

• People visiting Disney are guests, not customers.

• Jobs are performances; uniforms are costumes.

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.

An organisation does things, and it systematically does some things rather than others. But that’s as far as it goes. Systems don’t make mistakes — if they do something, that’s their purpose. But it also works the other way around. Systems don’t have inner desires, so they don’t do things intentionally either. There’s just a network of cause and effect. We might think they’re conspiring, but they’re working within structures that made the outcome inevitable. Or we might see everything as a terrible cock-up, but we don’t understand that the outcome was the inevitable result of the way the system works.

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.

There is a solution to this kind of problem in the viable system model — Beer emphasises it in his later work, particularly in the parts of the second edition of Brain of the Firm that were written after his experience in Chile. A truly viable system needs to have communication channels that link the operations to higher-level management functions and even to higher levels of recursion. Stafford Beer called these ‘algedonic signals’ — a kind of neurological metaphor, coined from the Greek words for pain and pleasure.

It might be easier, though, to understanding them as the kind of messages sent by the red handle in a train driver’s cab. As well as a physical check on the individual vehicle, the emergency brake sends an organisational message to the whole railway, informing it that a piece of track can’t be used.