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Friday, November 5, 2010

Quote of the Day...

"I believe that something drastic has happened in computer science and machine learning. Until recently, philosophy was based on the very simple idea that the world is simple. In machine learning, for the first time, we have examples where the world is not simple. For example, when we solve the "forest" problem (which is a low-dimensional problem) and use data of size 15,000 we get 85%-87% accuracy. However, when we use 500,000 training examples we achieve 98% of correct answers. This means that a good decision rule is not a simple one, it cannot be described by a very few parameters. This is actually a crucial point in approach to empirical inference.
This point was very well described by Einstein who said "when the solution is simple, God is answering". That is, if a law is simple we can find it. He also said "when the number of factors coming into play is too large, scientific methods in most cases fail". In machine learning we dealing with a large number of factors. So the question is what is the real world? Is it simple or complex? Machine learning shows that there are examples of complex worlds. We should approach complex worlds from a completely different position than simple worlds. For example, in a complex world one should give up explain-ability (the main goal in classical science) to gain a better predict-ability."

-V.N. Vapnik

My question is: What, if any, applicability does this quote have to economics?  Should we be willing to trade-away explain-ability for predictability? 


  1. I've always seen the complexity of an advanced economy as requiring we give up predict-ability for explain-ability. We don't know what will happen tomorrow, but we can explain why certain things happen in certain ways. Take the fall of socialism. Mises and Hayek knew it wouldn't last, but they couldn't predict when it would end.

  2. Rick, I absolutely agree with be honest I was a bit floored when I came across this Vapnik quote but thought I would share it anyway to encourage some discussion.

    I really admire his work, but I suspect that this quote does not have very much applicability to economic systems. Thanks for contributing!