Imagine that 100 of us have gathered together in a room somewhere. It’s a social event, but I want you to think about a couple of numbers.
If we took the average height of all of us, it would be somewhere around 1.76 meters. What happens to this average if we’re joined by Sultan Kösen, the tallest man (2.51 meters) in the world? Our average height goes up to 1.767 meters. In other words, the average increased by about 0.4%.
Now think about our average wealth. The stats vary, but average net worth in the US is around $120,000. What happens to this average if we’re joined by Carlos Slim, the richest man ($63.3 billion) in the world? Our average net worth goes up to $745,544. In other words, the average increased by 521%! And that’s after Slim lost $11b due to the GFC.
The difference between 0.4% and 521% is the difference between normal and complex.
Height is distributed normally, and in a normal system, the average dominates the extremes. The economy is a complex system, and in a complex system, outliers matter.
That picture is from The Behavior Gap: Simple Ways to Stop Doing Dumb Things with Moneyby Carl Richards.
Here is what he says about the importance of outliers:
[O]utliers matter. In fact, they matter so much that they almost make the average meaningless. Because most of our lifetime return is determined by how many of these outliers we experience, it is time we stop ignoring them.
If we’re trying to innovate, our job is to invent the future. So the fact that the economy is a complex system is important.
First off, it’s important because returns to innovation follow the kinds of returns that we see in the wealth distribution. The average return to executing a new idea is small, but a small number are gigantic. This is why it’s important to manage innovation as a portfolio.
Secondly, this has a big impact on how to think about the future. Complex systems are impossible to predict. This is a problem, since we don’t like uncertainty.
Here is how Martin King frames the problem:
The problem with long term developments are that they are subject to exponential and combinatorial factors – chaotic things that we are not good at understanding at the best of times. To compound things change cycles themselves are becoming faster.
Instead of thinking of the future as something to predict, we should think about it as part of a pattern. Greg Fisher wrote an outstanding post (read it!) discussing the importance of pattern recognition in complex systems. Here is part of his prescription:
There is a relationship between patterns and prediction. In fact, I would note that not only do patterns exist and persist, we must rely on them in every day life. We make decisions in the present assuming the persistence of some patterns e.g. I will withdraw £50 from a cash machine today for spending over the next few days. I do not expect everyone else in the UK to switch to the Thai Baht during that period. Furthermore, it is particular patterns – many of which we might call institutions – that are responsible for our civilised society and a relatively high standard of living.
But – and this is to assert the point further – it is important to emphasise that the world will change and so too will the patterns around us. By “expect to persist” in my definition of patterns I was referring to making reasonable judgments that some patterns will remain broadly the same over a particular period.
How should we respond to this? Geoffrey Morton-Haworth has written an excellent post on Learning From Patterns. First he talks about the most common example of a complex system: the weather. And he says:
We cannot control the weather but if we recognize its patterns we can manage around them. And we can do the same in complex relationships.
He then discusses the work that Edward Tufte has done on effectively assessing complex data. Morton-Haworth includes this worksheet from Tufte:
Here is how he concludes:
Tufte argues for good method. That is “a shrewd intelligence about evidence, a clear logic of display and analysis, placing data in the appropriate context for assessing cause and effect”. In short, he talks about the need for “a coherent architecture for organizing and learning from images”.
A complex relationship outlasts its components, just as the ant colony outlives the individual ant, and in so doing develops a purpose of its own greater than the free will of its parts. While individuals may only be involved for a matter of months or just a few years, a complex relationship can learn, change, grow and adapt over five, ten, fifteen or more years. Nevertheless, because our lives take place at lower levels, we frequently don’t know the contribution we make to complex relationships. But we can help its intelligence to emerge.
This is why it’s important to think about inventing the future. Another important feature of complex systems is that the systems co-evolve with their parts. In simple terms, small changes among the parts can cause large changes within the system.
These are the small number of innovations that end up having big impacts. And how can we find these? We can’t predict which innovations will hit big – knowing this is one of the important outcomes of thinking about the economy as a complex system.
Harold Jarche does a nice job of framing some of the issues here:
When we move away from a “design it first, then build it” mindset, we can then engage everyone in critical and systems thinking. Workers in agile workplaces must be passionate, adaptive, innovative, and collaborative. Autonomy is the beginning.
In a complex economy, the way to think about the future is this:
- We can’t predict the future.
- But we can learn about the patterns from which the future will emerge.
- In fact, while we can’t control the future, we can influence it.
- The best way to influence the future is by innovating through experiments.
What experiment can you try today?