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Just for today, I want to change the view, and tell you about my favourite metaphor for explaining what’s going on in the economy.  Here is a warning: the approach I am taking today is different from just about anything you will read elsewhere.


Danny Hillis is an interesting fellow. Wikipedia described him thus: he is an American inventor, scientist, engineer, entrepreneur, and author. He co-founded Thinking Machines Corporation, a company that developed the Connection Machine, a parallel supercomputer designed by Hillis at MIT.

But it was one specific experiment he carried out that truly fascinates me.

He tried to create a computer programme by mimicking the force we call natural selection. So he set himself the task of evolving a computer algorithm designed to sort numbers.  First he set up a computer programme in which a large number of mini-programmes could be generated randomly. He also set up certain parameters so that the programmes that were best able to sort numbers survived. The rest died out. He also made it so that surviving programmes could mutate, again in a totally random way. So he started the experiment.  At first the mini programmes  that formed were totally useless as you would expect them to be; this was after all a random process.  But some were less useless than others, these survived and mutated.  After the programme had run for a while, and each mini programme had mutated many many times he finally had a perfectly satisfactory programme for sorting numbers. Now I don’t know about you, but I already find this experiment fascinating.

But the Hillis experiment hit a problem. No matter how many times he re-ran the experiment, the resulting mini-programme was never as good as what a competent programmer could produce.

That is interesting because it would suggest a limitation in evolution; that deliberate design is superior to evolution.

I need to deviate for one paragraph to explain what happened next, and to tell you why I find this experiment so fascinating, and also relevant to the economy.

Imagine a chess board, but instead of there being 64 squares there are an infinite number. Now imagine that this giant chess board represents every possible innovation, with each square being one possible scenario. Each square is then flat, raised or depressed. The greater the elevation, the greater the innovation. A depressed square represents a backwards move in innovation. In this model, similar innovations are next to each other, so that certain areas of the grid will form something that looks kind of like a mountain range.  This is known as a fitness landscape.

Now imagine that innovators, let’s call them agents, are allowed to run one square at a time, at random over the huge chess board, searching for the most elevated squares.

Hillis applied this way of thinking to his computer programme. The way he saw it each mini-programme was an agent, but they all got stuck. They found a local mountain range, a method of creating a number sorting programme, but once there they could not move, for to do so they would need to move downwards before they could move back up. If you like, the agents found a kind of local maximum, an equilibrium from which they could not move.

The Hillis solution was ingenious. He programmed predator codes into the system that ate programmes that stood still. This forced the programmes to move, even when they had found their local maximum. By adding predator codes to the system, Hillis found he could generate number sorting programmes that were just as good as those that a top notch programmer could produce.

I think this model provides a pretty good metaphor for how evolution can work. I see it in these terms. Evolution is like Homer Simpson. It lives in the present, has no vision, and can only choose the option that provides the best short-term benefit.  Sometimes in order to progress in life we need to first move backwards, and unwind. Evolution can’t do this. This is why the fossil record often shows acceleration in evolution after some kind of natural disaster, such as the comet wiping out the dinosaurs, or the formation of the rift valley favouring a bipedal ape. This disaster had the effect of breaking equilibrium.

I think a similar fitness landscape provides a pretty good model to explain the economy too. It can show how an economy which gets stuck in a kind of local maximum cannot move forward. Until 1820, there seemed to be a limit to how high GDP per capita could grow. It took something exceptional to create growth. Maybe the economy got stuck in the 1930s too, and it took a world war to propel it forwards. I suspect that we may be at such a point now, too. The economy is stuck and cannot move forward. The banking bail-out may have been a bad move, because it just maintained that state of equilibrium. And until the economy stumbles on a way to ensure that the meagre fruits of current growth trickle down into wages, creating demand for future growth, I think the economy will remain stuck.

 

These views and comments are those of the author alone and do not necessarily reflect the view of The Share Centre, its officers and employees


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