Friday, 11 July 2014

Post-Empiricism and Data Tables

I was reading Peter Woit's blog and stumbled with this post-modern word: post-empiricism. Apparently, a guy named Richard Dawid, said to be a physicist-turned-philosopher whatever that means, wrote a book about this and String Theory. I haven't read the book and I doubt I will, because I have already read one thousand similar arguments and not even one of them had anything new to add to the discussion. 

But to give you an idea of what Dawid means by "post-empiricism", I will reproduce part of an interview given by him which Woit put in his blog:

I think that those critics make two mistakes. First, they implicitly presume that there is an unchanging conception of theory confirmation that can serve as an eternal criterion for sound scientific reasoning. If this were the case, showing that a certain group violates that criterion would per se refute that group’s line of reasoning. But we have no god-given principles of theory confirmation. The principles we have are themselves a product of the scientific process. They vary from context to context and they change with time based on scientific progress. This means that, in order to criticize a strategy of theory assessment, it’s not enough to point out that the strategy doesn’t agree with a particular more traditional notion.

Let me start by saying that, as Sokal has made explicit, the fact that you find an intellectually good looking word for something does not make that true. In particular, although attaching the suffix 'post-' to a word gives to it an air of modernity and rebellion, that also doesn't give any extra credibility to the concept.
Okay, as I am not reading the book, I have to extract what I understand by Dawid's post-empiricism from the post. It seems to me is that he is simply rephrasing in the most Sokal-like fashion the argument that we have to relax the condition that theories should be testable. He talks about 'god-given principles', principles that 'change with time' and 'traditional notion'. All this, of course, are discourse techniques which mean nothing concrete.

I really, really understand the desperation of string theorists to defend their line of research given the fact that people cannot give credit for theoretical exploration of ideas, but that's not reason to turn to religion and mysticism or starting believing in ghosts, which is exactly what happens when one argues that one does not need to test if something works or not as long as it is interesting. Of course, not all evidence comes from direct experiments. A theory can be tested by comparing it with other tested theories to see if there is any inconsistent, but ultimately, a theory that does not make any testable prediction is nothing more than a data table. It can be a beautifully decorated table, but it is still just a table. I will explain myself. 

Think about the following toy phenomenon: a ball is in a field divided in two sides and it changes sides once in a while. My data set consists of the times at which the ball passes through the central line that divides the field. Suppose now that I have five data points: t = 1, 5, 6, 11, 20. Now, I say to you that I have a theory describing this data. My theory is 

-6600 + 9950 t - 3941 t^2 + 633 t^3 - 43 t^4 + t^5 = 0.

In other words, my theory is that the times at which the ball passes the central line are the zeros of the above polynomial. There is only one problem: there are only five zeros for the above equation and they are exactly the data in my data set. This means that the above equation, even being an equation, is nothing more than the list of points I had before written in a different way.

Any reasonable person will then complain: wait! But you are making the prediction that the ball is never going to cross the line again! And then I say to you: don't worry, it's such a nice-looking equation! Be more of a post-empiricist and give less importance to predictions. Who needs to test such a beautiful theory? Besides, do you have a better theory to describe this data?

I rest my case.

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