In a previous Sunday article on the theory of innovation, I emphasized the balance between optimization and exploration, and between order and chaos.  In order to innovate, we need to do two things:

1) We need to make something new. (chaos)

2) That new thing needs to persist over time. (order)

Christopher Langton coined the phrase “edge of chaos” to describe how this happens in a computer model called a cellular automata (CA).  A CA is a grid that uses rules to calculate changes in the color of cells on the grid, over time.  There can be a very large number of rules, but Langton found that they can be described by a parameter he called “lambda”.  Lambda tells you what proportion of the cells on the grid will not become black in each time period.  If lambda is low, the grid just goes to black – perfect order.  If lambda is high, the cells on the grid oscillate – chaos or noise.  In between, you can see lines and patterns on the grid, representing things that were created, and then persisted.

Follow this link to find an applet that actually runs a CA, and lets you play with the lambda variable (the slider on top).  Select example “ca_params_4” under the “examples” menu, and let it run for a while.  You will see how patterns can start, spread, and interact.  In example 4, you will get random noise if the lambda slider is over about .44, static black under .14, stable triangles between .14 and .22, and interesting behavior between .22 and .43

A business organization is like this “params_4” system just under .22.  Most of the time, it is out of the chaotic zone (except on Mondays) and it produces the same products and services (the red triangles).

Sometimes, you can push it closer to the edge of chaos, and it starts to do new and colorful things.  However, you generally need to push it.

It turns out that if you pump energy into a system you can get to “self-organized criticality” – a natural progression to the edge of chaos state where interesting things can both happen, and persist.

In practice, most organizations stop innovating and exploring, and start optimizing in a very small area, as soon as they find something profitable to do. They back off from the critical point, toward order.  As noted before, innovation is very inefficient and wasteful.  It involves trial and error, mostly error. Businesses want to be efficient.

It’s often laughable when big, successful organizations say that they want to be more innovative. Sometimes, they say they want to be “efficient innovators” which is almost a perfect contradiction.  These organizations are big and successful because they found something profitable to do, and they do it efficiently. They hire people with specific job descriptions, to fit into this efficient process. They don’t want employees to do something completely different. It would wreak havoc with profitability.

This isn’t just a human tendency.  When I run computer models of genetic programming, one of the most powerful forces is an evolution toward stability.  This can happen very fast – within three or four generations of starting.  And, it can produce a population that basically stays the same for hundreds of generations after that.  In genetic programming, the mechanism is a buildup of redundancy in the genome, to the point where  you can cut and splice the computer program “genomes”, and almost always get the same algorithm.  Back in 1992, I described this as “defense against crossover”.  The objects of a genetic operation tend to evolve the genome to defend themselves from changes.

We see the same thing in biological evolution.  Species are actually very stable.  It is not common to see entire species evolve continuously, as Darwin suggested they might do.  What we actually see is “punctuated equilibrium” – a species that is the same for a long time, and then suddenly displaced by a new species.

That’s why I am so sure that we can make evolution run faster. It’s designed to be slow.

Does innovation stop when we switch to optimization?  No.  It doesn’t stop.  It just gets delayed, and pressure builds up, like the pressure for an earthquake.  This pressure buildup leads to the signature phenomenon of self-organized criticality, which is called a “power law distribution”.  Basically, the system builds up enough pressure so that innovation doesn’t just happen incrementally.  It can happen at any scale.

One visible effect in biological evolution is punctuated equilibrium and mass extinctions.  One visible effect in industrial economics is a distribution of startup sizes, where some startups can grow to be as big as the biggest previously existing companies.

So, there are forces which stop innovation.  Necessity – a lack of resources – is one of them.  Necessity is NOT the mother of invention.  Necessity is the mother of efficiency.  Efficiency is in some ways the enemy of invention.  Where we see poor people, poor countries, and declining industries, we do not see innovation. Where we see "east coast" venture capitalism, we do not see invention.  We see efficient and conservative lifestyles and operations.

What is the real mother of invention?  I will cover that in the next Sunday article on the theory of innovation.