Tuesday, February 7, 2017

Why not ask a scientist?

The editors of Bloomberg View ask Why Not Make Economics a Science?, and Noah Smith adds a bit more to the discussion on his blog. It's mostly good stuff.  One of the strange things is that when the editors put forward their thesis:
Reviving economics as a science will require economists to act more like scientists [pdf].
the link at the end connects you to Paul Romer's "The Trouble With Macroeconomics". Paul Romer is an economist, and gets a lot of how science is conducted wrong. His concept of a scientific model is mistaken, his analogy with string theory is misguided, his mathiness charge just demonstrates the real problem more clearly, and he's just as unscientific about his approach to explaining a theoretical result as any macroeconomist.

I have written many posts on how non-scientists get science wrong as it should apply to economics (here, here, here, and here, for example). The two biggest ways it goes wrong are an obsession with so-called unrealistic assumptions and an elevation of science to what I call Wikipedia science. Here are the editors making the first mistake:
Their ambition has been to build mathematically elegant and internally consistent models of the economy, even if that requires wholly unrealistic assumptions. Granted, just as maps have to simplify complex terrain, theoretical models must ignore aspects of reality to be any use. But there’s a line between simplification and gross distortion, and modern macroeconomics has crossed it.
This misses the forest for the trees, and misses the point of Milton Friedman's positive economics essay. The assumptions are not the issue. The editors do touch on the issue:
If models are refuted by the observable world, toss them out.
If models are refuted, toss them out. If they are not, then keep looking into them. However refuted models may have valid assumptions, and empirically valid models may have refuted assumptions. This is a principle of how modern theoretical physics proceeds -- it's called effective theory. Physicists have had to come to terms with the fact that it may be impossible to ever understand what is really happening at a fundamental level (due to e.g. lack of new accelerator experimental results) and so treat understanding as tentative, effective. Macroeconomists might have to come to terms with the fact that human behavior is not amenable to tractable mathematical description and may have to work around it (that's a good description of what I do on this blog using information theory to get around human behavior and provide a short cut to understanding complex systems). 

You may think that if the empirical validity of assumptions doesn't necessarily matter, then it doesn't matter if you use realistic or unrealistic assumptions. But this represents another problem illustrated by the editors:
Rely on experiments, data and replication to test theories and understand how people and companies really behave.
Maybe the editors are unaware of the SMD theorem, but it is entirely possible "real behavior" is not relevant to macroeconomics (or at least relevant to tractable macro theories). This restriction is exactly the kind of straitjacket that Mean Squared Errors put so well:
Consider the macroeconomist.  She constructs a rigorously micro-founded model, grounded purely in representative agents solving intertemporal dynamic optimization problems in a context of strict rational expectations.  Then, in a dazzling display of mathematical sophistication, theoretical acuity, and showmanship (some things never change), she derives results and policy implications that are exactly what the IS-LM model has been telling us all along.  Crowd -- such as it is -- goes wild.
Just substitute realistic behavior and empirically valid assumptions for rational expectations and representative agents. Until a rigorous framework is in place, macroeconomists will work their way around those straitjackets as well.

The thing is that you should add assumptions or take them away based on whether the resulting macro theory is empirically valid or not. Adding them or taking them away because you stroke your chin and make a "very serious person" face is not scientific.

It's not clear if the editors make the second mistake of thinking science is Wikipedia science. By "Wikipedia science", I mean the popular perception that science doesn't make mistakes, doesn't go backwards, or has everything figured out -- that you could look everything up on Wikipedia. These quotes give me pause:
Far from advancing, the science of economics has been going backwards. ... In just about every branch of science, theoretical research has been crucial to achieving breakthroughs. In macroeconomics, it has held progress back.
Sometimes going backwards is what needs to happen. And the second piece is not true. Theoretical paradigms in physics (ones based on making a "very serious person" face and stroking your chin) have definitely hindered progress. Aether and objections to the randomness of quantum mechanics are two that people might be familiar with. Full renormalizability is a more technical one (the rejection of that is what has lead to the embrace of all theories as effective theories). If theory doesn't make progress, that is ok. It's true that lack of progress is a heuristic that hints something might be going wrong, but it doesn't tell you what. If your car doesn't start, that just tells you something is wrong; it doesn't tell you what. You might be out of gas. Your alternator might be shot. In macroeconomics, maybe all this insistence on including human behavior is the problem (also here).

*  *  *

So, why not ask a scientist about if and how macro fails to be scientific? This scientist has put together a list (of both valid and invalid complaints, in my opinion). A short summary:

The identification problem (& complexity)

The basic point is more than one set of parameter values can result in the same macro observations. In science, we'd try to determine the values from the micro theory (which is what economists do [at least they say they do ...]), but also reduce the number of parameters by reducing the complexity of the theory. This is part of general problem that macroeconomic theories are way too complex given the limited empirical data.

Economics does not appear to treat limits properly & Economics does not deal with domains of validity (scope)

Is a model valid in a particular situation? Can you apply rational expectations when the system is far from a general equilibrium? What time period is considered a long time versus a short time? These are questions that not only aren't addressed, but often fail to be even asked.

Economics accepts stories too easily

This included making a "very serious person" face and stroking your chin. A whole lot of macro seems to proceed by narrative. Those unrealistic assumptions? Every one has a story behind it. They keep being used because of the power of the story.

Adding realistic human behavior? There's a story behind it involving a lot of psychology experiments and irrational decision making. I can assure you there isn't a macroeconomic empirical success behind every human behavior assumption (mostly because there aren't a lot of empirical successes).

... Note that the stories and lack of scope conditions are a toxic combination that mean you have no idea where to stop telling stories and adding assumptions. Just keep adding things you can tell a story about until you get some agreement with the data! Scope conditions help because they tell you whether a story is relevant.

4 comments:

  1. What you call effective theory is purely and simply curve fitting.

    ReplyDelete
    Replies
    1. It's not my term.

      And what's wrong with curve fitting?

      [Link directs the reader to a very nice example of using effective field theory to describe data.]

      Delete
  2. Nothing is wrong with curve fitting - but it is not science.

    Name it as it is.

    ReplyDelete
    Replies
    1. I recommend you read this story of Max Planck and quantum mechanics. "Curve fitting" deeply involved.

      http://galileo.phys.virginia.edu/classes/252/PlanckStory.htm

      A nice picture.

      But in general someone who says curve fitting is not science probably doesn't know what they are talking about. I also get the feeling that this the the same anonymous commenter who is generally a moron.

      Delete

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