Understanding Complexity (and what to do about it)
People like to say, “keep it simple, stupid.” It’s a down-home remedy for our overly complex, technology infused modern life. Like much good advice, it is often given, but rarely followed.
The problem is that simplicity is not so simple. We live in a complex universe where much that happens is beyond our control. Merely wishing things to be simpler does not make it so. In fact, making facile assumptions often leads to disaster.
How we deal with complexity determines how we innovate, build organizations that can compete effectively and navigate an increasingly technological marketplace. We need to take it seriously, not gloss over it. Fortunately, this has been an area of intense study since the beginning of the digital age and there are some basic principles that can guide us.
The Complexity of an Entity
The idea of complexity is fairly new. It really began in earnest with Claude Shannon’s 1948 paper that spawned the field of information theory. It established, among other things, a measurement unit for information – the bit– a binary piece of information.
That led to the first serious thinking about the subject. The Kolmogorov-Chaitin principle, formally defines complexity (and therefore simplicity) by the number of bits that it takes to describe an object without losing information.
For instance, a googol is a very large number but simple to describe (i.e. 10100). While 479,001,599 is much smaller, it is also a prime number that can’t be reduced to anything simpler, which makes it extremely complex. That’s why large prime numbers are used in encryption, because we want our transactions to be hard to decode.
There have been some other approaches, but the general theme is that complex things take a lot of information to communicate; simple ones do not.
Complexity of a System
Simplicity, however, doesn’t just apply to individual entities, but systems as well. Simple systems are fairly easy to understand. They tend to look like this:
Statisticians use curves like these so often that whenever data follows this pattern they refer to it as normally distributed. We know a lot about systems like these and can predict much about them. For instance, if we have a relatively small sample of people’s heights, we can know a lot about the heights of other people.
Unfortunately, most real world systems don’t work like that, because they include feedback. While your height won’t affect mine height, your use of e-mail will. The same goes for your purchase of a house, your enthusiasm for a brand and so on.
That makes most of the things we interact with in the real world considerably more complex than the ones you’ll find in statistics textbooks. These type of systems are governed by power law distributions that look like this:
Unlike so-called “normal” systems, power law systems are problematic for two reasons. First of all, a few large entities drive the system, so an average value means little. It makes no sense to talk about an “average” social network when Facebook has almost a billion members.
Secondly, while normal systems quickly degrade at the margins, power laws have “long tails,” so there are no possibilities that we can entirely dismiss. The probability of a Justin Bieber or an Instagram may be very low, but unlike the possibility of a ten foot tall man, we have to take them into account.
While the Mandelbrot set is amazingly intricate (click to enlarge and zoom in), it is actually a fairly simple sequence repeating itself:
zn+1 = zn2 + c
By the formal definition of complexity it’s very simple, because we can describe it with very little information. However, as the sequence repeats itself we get a very complex structure that we only know how to simplify because we know the starting point.
Emergent complexity explains how the relatively information poor human genome (about 800 MB) can create a brain which supasses the computational capacity of the world’s most powerful supercomputers.
Scientists (and certain types of investors like Elliot Wave theorists) spend their careers dealing with this type of complexity, taking systems that have built up over millennia and trying to factor them down to a few simple factors. When they are successful, they are called geniuses (i.e. E=mc2), when they are not we call them quacks.
Dealing with Complexity
As Einstein said, we should make things as simple as possible, but no simpler. Ignoring complexity won’t make it go away. However there are some strategies we can utilize that can help keep the mess manageable.
Factor Down: The first place to start is with entities themselves. Acronyms and buzzwords can be convenient within organizations and communities which reuse the same terms constantly, but they are a disaster when communicating with a larger audience.
A good rule to use is Wittgenstein’s principle: if you can’t communicate in a common language, you probably don’t know what you’re talking about. If you are confusing others, chances are that you are confusing yourself as well.
Minimize Options: Although we like to have choices, they also make things more complex. Therefore, it often helps to minimize your options, especially in negotiations. As I’ve explained before, someone with a gun to his head is in a very strong negotiating position (with people other than the gunman, of course).
In his book, The Paradox of Choice, author Barry Schwartz shows that this principle applies in much less life threatening situations as well. When confronted with fewer choices, consumers tend to buy more. 37 signals built a software company with a cult-like following by stripping away features.
Compare an Apple product to any of their competitors and the first thing your notice is it has fewer buttons and doodads.
Stay Robust: Nicholas Nassim Taleb, the bestselling author of The Black Swan, has thought profoundly about complexity and how it affects our everyday lives (not to mention the recent financial crises). He suggests that the best way to deal with complexity is to stay robust enough to survive the volatility that comes along with it.
Certainly, Jamie Dimon and JPMorgan Chase benefited from a stronger balance sheet during the financial crises. While other banks increased risk when times were good, they stayed conservative and were able to buy up assets on the cheap when the bust came. Later, when their London Whale trade went horribly wrong, they absorbed it easily.
So the key to keeping things simple is to tackle complexity on every level. Keep entities simple, but understand that once they start interacting with each other a new more complex kind of order will emerge.
Keep simple what you can, make allowances for what you can’t.