by Paul Curzon, Queen Mary University of London
In May 2017, British Airways IT system had a meltdown. Someone mistakenly disconnected the power for a short time. The fleet was grounded and tens of thousands of passengers were left stranded for days. One suggestion was it was due to “cost cutting”. Willie Walsh, the Head of BAs parent group came out fighting, defending the idea of doing things cheaply: “You talk about it as cost-cutting, I talk about it as efficiency … The idea that you would accept inefficiency – I just don’t get it.”
The fact that many business leaders don’t get it may be exactly the problem. Doing things more cheaply than the competition is an idea that is at the core of capitalism. It is often taken as a given. But, is it really always true?
The best and only the best
Computer Scientists actually use the word “efficiency” in a subtly different way. When they talk about a program or algorithm being efficient, they do not mean that it was cheap. They mean it did exactly the same job, but faster or with less memory. This is one of the really creative areas of computer science. Can you come up with an algorithm that does exactly the same thing but in fewer steps?
The business version of efficiency would be fine if it had the same underlying principle. Do it cheaper, yes, but only if it really does do exactly the same thing in all circumstances. To company bosses, however, the trade-off can be seen as cut costs at all costs. ‘Waste’ is anything you think no one will notice. You accept the 1 in a million chance of it not working at all – just as with the BA meltdown, taking the hit (or rather your passengers do) because you think you will make more money overall as a result.
Even with algorithms we do accept inefficiency though. Engineering is often about trade-offs. Sometimes, you will accept inefficiency in the use of memory because it gives a way to get a faster algorithm. Sometimes you accept a slower algorithm because it is just easier to be certain your code really does do the right thing. Sometimes slow is good enough. Sometimes it is the bigger picture that matters. The fastest algorithms for searching for information require sorted data. That is why a dictionary is in alphabetical order. Finding the word you want is quick – you don’t have to check every word in turn to find the one you want. However, if you were only ever going to look for a single thing in a data source, you wouldn’t sort it first. You would use an inefficient search algorithm, because overall that would be faster than sorting and then searching once. Efficiency can be subtle.
There are actually even more powerful reasons for demanding inefficiency. In the area of safety-critical systems, computer scientists build in redundancy on purpose. When the consequences of the computer not working is that lives are lost, we definitely want inefficiency, as long as it is well-engineered inefficiency. Dependability and safety matter more.
An algorithm is a mathematical object. If it works, it always works. However, programs operate in the real world where things can go wrong. Hardware fails, clocks drift, criminals hack, technicians do silly things by accident (like unplug the power). Systems that matter have to be resilient. They have to cope with the unexpected, with the never before seen. One way that is achieved is by designing in inefficiency. For example, if your single computer goes down, you are stuffed. If instead two computers run the same program in parallel, then if one goes down the other can take over. Ahh, but how do you know which is wrong when they disagree? Be even more ‘inefficient’ and have three computers ‘wastefully’ doing the same thing. Then, if one goes rogue, the three vote on who is at fault … cut them out and carry on.
Computer Scientists have developed many ingenious ways to build in guarantees of safety even when the world around conspires against us. To a cost cutter these extras may seem like inefficiency but the inefficiency is there, apparently unused most of the time, waiting to step in and avert disaster, waiting to save lives. Personally, I would accept inefficiency. I hope, for the sake of saved lives, society would too.