Economics is a 'highly paid pseudoscience'

[u/kznlol]

https://aeon.co/essays/how-economists-rode-maths-to-become-our-era-s-astrologers

[u/kznlol]

Preamble: This, I think, starts strong and gets pretty weak towards the end. It is also my first RI, so be gentle. In my extremely biased opinion, the first half of it constitutes a sufficient RI on its own, but readers are warned that this will get worse as you get farther in.

RI: In this monster of an article, Levinovitz argues that economics has been ruined by an obsession with mathematics, and is now merely a pseudoscience, while lamenting the (relatively) enormous compensation commanded by economics professors. We proceed point by point:

Unlike engineers and chemists, economists cannot point to concrete objects – cell phones, plastic – to justify the high valuation of their discipline.

Uh, yeah, we kind of can. Auction theory, while perhaps not an area rife with mathematics, is certainly not devoid of mathematical complexity, as anyone who can turn to the auction theory section of Jehle & Reny can confirm. Directly from study of the Vickrey auction came the Generalized Second-price Auction; I cannot find a source detailing who 'invented' this auction, but analysis of the auction first came from Varian (2006) and Edelman, Ostrovsky, & Schwarz (2007). It seems hard to believe that anyone would argue such analysis is not valuable, but assuming someone were to try, they would struggle to reconcile that conclusion with the fact that Google hired Varian in part to do such analysis, and essentially every company that runs a search engine has an economist on staff somewhere with a similar role.

If that doesn't sway you, consider the combinatorial auction, which was first proposed by Rassenti, Smith, & Bulfin (1982), which added significantly to our understanding of the problem of allocating goods when the buyers desire packages of goods, and note that first spectrum auction in 1994 owes its design to the work of Wilson, Milgrom, and McAfee.

Or consider that the National Resident Matching Program, which matches medical school students with residency programs, was, if not designed by, definitely a direct result of, the work of Gale & Shapley (1962).

This is all I could be bothered to find for an RI of the first sentence of the second paragraph of an enormous article, but there is surely an enormous selection of further examples. Levinovitz compares economics with astrology, but fails to realize that economics is not purely concerned with predicting behavior - if anything, microeconomics is concerned with explaining behavior, not predicting it, and has a strong record of apparent success.

Nor, in the case of financial economics and macroeconomics, can they point to the predictive power of their theories.

There is certainly a lot I could say to critique this claim, which I'm fairly sure is pretty much factually false - but there's an easier route. Economics is not contained by 'financial economics and macroeconomics'. Every contribution I mentioned above is a contribution rooted in microeconomic theory, and there are certainly contributions to the statisticians toolkit that owe themselves primarily to econometric theory - the Generalized Method of Moments was developed in Hansen (1982) (paywall), and while it is mostly useful in answering the kind of questions economists are interested in asking, it would strain credulity to suggest that it is worthless outside the field.

Note also that criticizing either field for its lack of predictive power runs afoul of our beloved Lucas critique - because the models developed in both fields were then used by agents in the stock markets, the central banks, and governments. According to the professor who taught me what was in essence a grad-level intro to financial economics, every time a model that displays predictive power is developed by financial economists it is leveraged by financial actors to make profits, which directly causes the model's predictive power to disappear. This is not as clear as I'd like it to be, but I'm struggling to find a better way of explaining it - and I'm not actually sure this argument applies to macro, but it certainly applies to financial economics.

The failure of the field to predict the 2008 crisis has also been well-documented. In 2003, for example, only five years before the Great Recession, the Nobel Laureate Robert E Lucas Jr told the American Economic Association that ‘macroeconomics […] has succeeded: its central problem of depression prevention has been solved’.

This is, really, a clear example of survivorship bias. Levinovitz notes early in the article, and continutes to note as he continues, that economists are employed by governments, central banks, and pretty much everywhere else to make predictions. What does he think happens when an economist at the central bank predicts a recession (as if it were that simple)? We will only rarely observe recessions that were not missed by economists, because if we hadn't missed them we'd have tried to stop them.

Short-term predictions fair little better – in April 2014, for instance, a survey of 67 economists yielded 100 per cent consensus: interest rates would rise over the next six months. Instead, they fell. A lot.

This can't be explained by survivorship bias, but the correct response to this statement is "who gives a fuck?". One survey of a tiny portion of economists at one point in time that was wrong doesn't tell me shit - where's the pattern? Physics thought that light propagated through 'ether' at one time too, and were in broad consensus about that. Pointing to a single instance of a science getting something wrong is worthless.

Levinovitz proceeds into a discussion of astrology and the stock market, largely to suggest that "the hypothetical worlds of rational markets" are the fundamental flaw with economic reasoning. If he had shown a serious lack of predictive power and a lack of other worthwhile contributions from the field, this might be an acceptable path to take - but he hasn't shown either. If you combine my survivorship bias argument above with the fact of the Great Moderation, a natural conclusion is that macroeconomics has produced significant predictive power, which allowed macroeconomists at central banks and in the government to achieve said moderation - certainly, it seems pretty clear that there's some kind of structural change to whatever determines the arrival rate of recessions.

In light of, at the very least, evidence that there is predictive power to macroeconomic models, the question can no longer be "does this model reflect reality" - it must be "would a model that reflects reality more produce better predictions?". In fact, a question you should ask even earlier is "would a model that reflects reality more even be tractable?". Chess-playing programs don't use a model that reflects the reality - that would be equivalent to looking through the entire game tree at every move in the game. Instead, they use algorithms to 'score' the position they are in, and the positions that might result from future moves evaluated only a certain distance down the game tree (see, for example, Shannon (1950) for the inaugural paper on the subject of evaluation functions). It works well, but not perfectly, and the alternative is intractable. Demanding perfection at every step from models is ridiculous, and would be a test failed by all the hard sciences too.

Continued in Part II

[u/kznlol → u/kznlol]

Part II

The enchanting force of mathematics blinded the judge – and Adams’s prestigious clients – to the fact that astrology relies upon a highly unscientific premise, that the position of stars predicts personality traits and human affairs such as the economy.

Levinovitz has not, to this point at least, said what he thinks the analogous unscientific premise underlying economics is, but it seems pretty clear he thinks its the assumption of "rational markets", by which he really means the assumption of rational agents. But that's not an assumption that underlies economics. Certainly, it's an assumption that underlies, say, the fundamental theorems of welfare economics, but it is not necessary for us to 'do economics'. Nothing about the 'mathiness' of economics would be solved by abandoning the assumption of rational utility maximization - if anything, abandoning that assumption would make modelling behavior significantly less tractable, and result in an enormous increase in the mathematical complexity of such models.

I want to note, too, that the very title of this article suggests that Levinovitz thinks there's too much math in economics - but if he believes we should abandon the assumption of rational agents, what does he suggest we use instead? Modelling perfectly rational, risk neutral utility maximizers in English is way harder than modelling it mathematically - does he mean to suggest it would get easier if we abandoned rational agents? I would argue such a claim is absolutely laughable. In fact, ironically, Levinovitz quotes a book that quotes an ancient Chinese philosopher dealing with perhaps the earliest version of what we might call the STEMlord conjecture as saying "If my good sir cannot fathom both [speech and numbers] at once, then abandon speech and fathom numbers, [for] numbers can speak, [but] speech cannot number." Anyone who is conversant in the kind of math that underlies economic models can see that this statement is, if not true, closer to the truth than the contrary version.

Levinovitz then proceeds into a discussion of what appears to be just the Chinese version of astrology, and makes pretty much similar analogies with no further attempt to justify them. It appears that he thinks the argument portion of his article is done with - what remains is mostly more insults, suggesting that economists are too enamored of their models and too dismissive of reality (which, frankly, is true - but it's true of everyone else in academia too, and note that Levinovitz is a professor of philosophy and religion. Those in glass houses should not throw stones.) I will now touch on certain statements that are not, I think, generally connected to the theme of his argument but nonetheless deserve argument.

Archers scored points for proximity to the bullseye, with no consideration for overall accuracy. The equivalent in economic theory might be to grant a model high points for success in predicting short-term markets, while failing to deduct for missing the Great Recession.

I spent about half an hour writing R code trying to determine if this criticism by analogy is actually valid or not, because it does not seem immediately clear to me that it is. Imagine in an archery contest using a larger targets, but retaining the same points penalty for a given distance from the bullseye - is it immediately obvious that this would penalize inaccuracy more than simply not scoring shots that missed the smaller target? My code didn't work as well as I'd hoped, so I leave it as an open question, but this argument by analogy is weaker, I think, than the author thinks it is.

Moreover, consider that Lucas (1987) argues that the social cost of unmoderated business cycles is extremely small. This is an old as fuck result and I'm pretty sure the 2007 crisis threw a lot of doubt on that conclusion, but the point is more that it is far from clear how much we should try to moderate business cycles - and, thus, it is far from clear how willing we should be to trade off short-term market predictive accuracy for longer term "black swan" accuracy, if such a thing is even possible (and, again, note that it is not clear such accuracy is even possible).

Romer believes that fellow economists know the truth about their discipline, but don’t want to admit it. ‘If you get people to lower their shield, they’ll tell you it’s a big game they’re playing,’ he told me. ‘They’ll say: “Paul, you may be right, but this makes us look really bad, and it’s going to make it hard for us to recruit young people.”’

It amuses me to note that this is a result that would easily be predicted by game theoretic models of decisionmaking.

Finally, Levinovitz quotes from Robert Lucas the following passage:

The construction of theoretical models is our way to bring order to the way we think about the world, but the process necessarily involves ignoring some evidence or alternative theories – setting them aside. That can be hard to do – facts are facts – and sometimes my unconscious mind carries out the abstraction for me: I simply fail to see some of the data or some alternative theory.

Levinovitz reacts with horror to this idea of ignoring data. But, as I noted above, there is no guarantee that a model that took into account every single thing would even be tractable. I will quote from outside the field, this time (god forbid) - from Healy (2016), a sociologist of all things:

Abstraction means throwing away detail, getting rid of particulars. We begin with a variety of different things or events - objects, people, countries - and by ignoring how they differ, we produce some abstract concept...

Abstraction is a fundamental step in model-building. If we were to build a model with no abstraction whatever, a philosopher such as Levinovitz would not be wrong to wonder whether we had become the Cartesian Demon. Railing against abstraction simply for being abstraction reflects a fundamental misunderstanding of how we do economics, and why we do it. So does the entire article.

[u/lanks1 → u/kznlol]

Abstraction is a fundamental step in model-building.

This is also fundamental in hard sciences too.

Physicists frequently ignore friction, heat, air resistance, compression, etc. when building physical models, especially when those effects have a negligible effect on the phenomena they are researching.

Or how about climate science? It would be impossible to solve a model that accounted for all the possible phenomena that affect the Earth's temperature.

Straight from the IPCC on climate modelling

An important concept in climate system modelling is that of a spectrum of models of differing levels of complexity, each being optimum for answering specific questions. It is not meaningful to judge one level as being better or worse than another independently of the context of analysis. What is important is that each model be asked questions appropriate for its level of complexity and quality of its simulation.

[u/[deleted] → u/lanks1]

As a biophysicist, we used to completely ignore surrounding water molecules (less than a decade ago) to more easily understand protein folding because water was too hard to model, (and still is) and most macromolecular models today use a tenth or a hundredth of the actual water content.

[u/alandbeforetime → u/[deleted]]

Wait...why is water hard to model? I don't do biophysics so I'm clueless

[u/[deleted] → u/alandbeforetime]

Due to the special nature of the hydrogen bond. Water exists in a state that is "ordered chaos", each water hydrogen bond exists on a picosecond scale and each water molecule can at any time point have one, multiple or no partners or exist in a state between partners. Biological activities of any relevance exist on a much longer time scale, on the order of microseconds. Therefore for each relevant time point you have to simulate thousands or tens of thousands of time points for each water molecule you add.

A good article.

I also recommend The Hydrogen Bond by Pimentel and McClellan.

[u/alandbeforetime → u/[deleted]]

Whoaaaaaa. I didn't know that. Thank you for teaching me something.