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 Money
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.
Instead of innovating based on prediction (design, then build), which leads to big bets, we need to innovate based on experiments. This leads to little bets.
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?
Great post Tim!
Funny enough, I wrote something similar a while ago: http://www.digitaltonto.com/2010/justin-bieber-social-networks-and-how-numbers-can-lie/
Thanks Greg!
Not that funny really – we’ve covered similar things many times before!
It’s interesting how statistics’ mean, median, mode and range calculations are almost designed to discount the outliers yet as you point out they are really important to outcomes…I’d not thought of this before. Obviously haven’t been reading your blog often enough!
It’s a really interesting issue Robyn – and one that has had a fairly significant influence on my research. The wealth example is a good one. In nearly every country, the median wealth is WAY below the mean. In the US stats that I found for this it was about half. In a normal distribution, median and mean are always very close in value. So a lot of our normal assumptions about how the numbers work & what they mean go out the window…
Ioan Tenner (a wonderful thinker) makes a relevant point. There are three strategies for coping with surprise: preparing for surprise, preparing against surprise, and preparing the surprise … for others.
The last of these is your strategy of inventing the future.
See http://wisdom.tenner.org/1/category/surprise/1.html
That’s a great post Geoffrey – thanks for the pointer. Good to see a James Carse reference in it too.
Thanks also for your post, it’s a good one and it helped trigger my thoughts for this one.
Great post Tim
All of this talk about outliers and their unpredictable yet very large impact reminds of Black Swan theory (Nassim Taleb).
I came across an extension of this idea when applied to innovation selection criteria. The proposition was that we could use two simple criteria in place of our normal tendency to look at ROI; (i) potential impact and (ii) cost to experiment. A benefit here is that we can hold off on premature evaluation and groupthink often found in workshops … “okay, which of these ideas will work” or “which ones do we think the board will approve”. Particularly useful in complex systems with rapid change.
See http://fivewhys.wordpress.com/2012/02/01/idea-selection/
Thanks Brendan. I was going to include a bit from Taleb in this too, but it was long enough already.
I love the “cost to experiment” selection criteria! Thanks for that. I’ll definitely write about that when I get back from my break.
As a former student of languages and linguistics, I look for patterns in speech and text to decide what information I can trust. I think we probably all do. So when I find text with phrases like these, I stop trusting it:
“Inventing the future”
“Because most of our lifetime return is determined by how many of these outliers we experience”
“returns to innovation follow the kinds of returns that we see in the wealth distribution”
“a small number are gigantic”
“To compound things change cycles themselves are becoming faster”
Most of your page was comprehensible, but the parts I couldn’t understand rendered the overall piece meaningless to me.
For example, I tried to follow the sexy comic-book-style worksheet, and while it looked fun, it seemed to be out of context. All I could tell was that it had something to do with the size of an army correlated with temperature, but then morphed into giving advice about running a successful website. I couldn’t see what it was meant to be an example of.
I’m not claiming to know that what you have written (and quoted) here is bulls**t, just that it’s impossible to tell if it’s bulls**t, because your grammar is contorted. As a reader, I had to work so hard to make my own logical connections between its parts that I knew I was only guessing what your overall train of thought was.
Another cause of confusion is your use of everyday words like “return” in what must be a specialist sense, one which is beyond me. It makes me wonder how many other everyday words were in there that I thought I had understood, but in fact had other specialist meanings.
This much cause for confusion in a presentation is usually either the result of muddled thinking, or is an invidious attempt to muddle the thinking of others. To use a metaphor, you have mixed some mud into your puddle to make it look deep.
All I can extract from your page is: “Don’t make vast elaborate plans and then try to execute them to the letter: change things step-by-step with small experiments.” It would make a worthy tweet, but it isn’t not enough to build a profit-making business consultancy out of.
Thanks for the thoughtful comment Richard. Sorry to hear you didn’t find the post trustworthy.
The core message that you extract at the end is basically the point that I was trying to get across. In my experience both in and working with businesses this is actually a significant problem that needs to be addressed. Consequently, I’ve ended up writing a number of posts trying different methods of making this point.
Return is a very common phrase in business – so common that I wouldn’t think of it as jargon, but obviously it can be taken as such. It’s meant in the sense of definition 2 in the noun section here:
http://oxforddictionaries.com/definition/english/return?q=return