Noah Smith has a post about why macroeconomics doesn’t work (well):
1. There are a number […] “heterodox” schools of thought, [which] claim that macro’s relative uselessness is based on an obviously faulty theoretical framework, and that all we have to do to get better macro is to use different kinds of theories – philosophical “praxeology”, or chaotic systems of nonlinear ODEs, etc. I’m not saying those theories are wrong, but you should realize that they are all just alternative theories, not alternative empirics. The weakness of macro empirics means that we’re going to be just as unable to pick between these funky alternatives as we are now unable to pick between various neoclassical DSGE models.
2. Macroeconomists should try to stop overselling their results. Just matching some of the moments of aggregate time series is way too low of a bar. [It is important] when models are rejected by statistical tests […] When models have low out-of-sample forecasting power, that is important. These things should be noted and reported. Plausibility is not good enough. We need to fight against the urge to pretend we understand things that we don’t understand.
3. To get better macro we need better micro. The fact that we haven’t fond any “laws of macroeconomics” need not deter us; as many others have noted, with good understanding of the behavior of individual agents, we can simulate hypothetical macroeconomies and try to do economic “weather forecasting”. We can also discard a whole slew of macro theories and models whose assumptions don’t fit the facts of microeconomics. This itself is a very difficult project, but there are a lot of smart decision theorists, game theorists, and experimentalists working on this, so I’m hopeful that we can make some real progress there. (But again, beware of people saying “All we need to do is agent-based modeling.” Without microfoundations we can believe in, any aggregation mechanism will just be garbage-in, garbage-out.)
This led to a very interesting Twitter discussion:
Ashok Rao Personally, I’d frame it that modern theory is fundamentally deductive in nature whereas the marcoeconomy is inductive/Bayesian.
Noah Smith I think that’s a wrong way of seeing things. Real science involves an iterative process of induction and deduction.
Ashok Rao But your claim also assumes there’s something “fundamental” about the economy in the sense of a real science. Is there?
Noah Smith Maybe. There’s real science in earthquakes but we can’t predict them at all.
Ashok Rao Hm. So there are systemic laws. But can these not be “understood” only through induction? As in the economy as machine learning.
Noah Smith Maybe!
Ashok Rao As long as we agree that there is a lot of doubt! 🙂
This conversation is at the very heart of my discomfort with much of modern economics, and I’ve been wanting to blog about this for a while, so now is as good a time as ever to dive right into it. Before I go on, I want to clarify that it seems like Noah and I have a very different understanding of what inductive is (or at least should be):
Ashok Rao Yes but the 3 ‘main’ equlibria frameworks (general, classical game theory, and rational expectations) are all deductive. Right?
Noah Smith No, you can easily make a Walrasian equilibrium happen in a lab, it’s very robust under certain conditions!
Of course, to the extent that empirical creations in the lab or double auctions are inductive Noah is right. But the macroeconomics behind this is principally deductive. By this I mean mathematicians economists have employed mathematics (the major premise) to a set of assumptions (the minor premises) to infer a conclusion. Ultimately, the theory is a grand syllogism, and highly deductive in nature. Further, the comparison to earthquakes doesn’t sit well with me. Physicists have very good microfoundations about how the earth works, and they’re not in perpetual motion. Scientists might fail at the aggregation of these bits of knowledge, but economics has a much more inherent flaw.
This is precisely the reason classical game theoretic approaches work only in “small lab settings” and that the Walrasian equilibrium holds only under “certain conditions”. That they do granted the right assumptions is tautological. Indeed, mathematics is internally consistent and hence in a concocted economy (the double auction) specific deductive models have to hold.
But by induction, I don’t mean experimental confirmation tempered by statistical reasoning. W. Brian Arthur at the PARC puts it better than anyone else:
This ongoing materialization of exploratory actions causes an always-present Brownian motion within the economy. The economy is permanently in disruptive motion as agents explore, learn, and adapt. These disruptions, as we will see, can get magnified into larger phenomena.
If economists want to import one idea from physics, it should be Brownian motion:
One way to model this is to suppose economic agents form individual beliefs (possibly several) or hypotheses—internal models—about the situation they are in and continually update these, which means they constantly adapt or discard and replace the actions or strategies based on these as they explore. They proceed in other words by induction
The best way I can imagine this idea is a “Bayesian machine”, if you will. While classical game theory, rational expectations, and competitive (Walrasian) theory might have inductive verification, Arthur is suggesting that the economy is inherently inductive.
The catch here is that for something that is at its heart inductive, there is not deductive verification. This is why many such as myself are skeptical of the mathematical models that dominate economics as they cannot either explain or verify anything. Often criticized, is the unrealistic nature of rational expectations. But in a real economy, I not only know that I’m not rational, but I also know that fellow agents are irrational too. This means I have subjective preferences, but also have subjective preferences about other people’s subjective preferences. These two degrees of subjectivity make many economic assumptions not just wrong, but impossible. (Think the epistemological difference between is not and can not be).
This is why I disagree with Noah. While in specific circumstances – equilibrium is a sub-class of non-equilibrium, after all – deductive engines work, macroeconomics has failed because the economy is inductive. At every moment in time there is a constant ferment, a change in attitude and belief. Standard economics holds that we all have one, perfectly rational prior. Induction holds that we all have pretty crappy priors that are constantly updated not only by economic outcomes, but also political and institutional motion.
Talk to goldbugs (actually, avoid it if you can). They’ll tell you about how they fear a government-Jewish orchestrated New World Order meant to line the pockets of rich bankers at the cost of the worker, by debasing our currency. Every economic indicator tells you they are wrong.
In a deductive model, it is impossible to accommodate for such people. If we modify a standard DSGE to tolerate such granularity it becomes intractable. A computer scientist would think about this as a machine learning problem. While there are a handful for which analytical solutions might work, the driving theme behind modern data mining and machine learning projects, even as simple as classification problems, is the flexibility of statistical computer science.
But the problem with induction is that, well, it’s not deduction. A well-formed syllogism guarantees its inference. Very much like the sum of two and two has to be four. On the other hand, induction is fuzzy and unclear. You can’t prove any sweeping laws and ideas with inductive reasoning, as Karl Popper has brilliantly argued. Indeed, inductive thinking is fragile against “black swan” events.
These aren’t real limitations, though. In the economists’ imagination theory trumps empirics. For the same reason, running large simulations on supercomputers is hardly as appealing as theorizing Walrasian economists. Proving things is really fun (if you’re smart enough).
But just as natural evolution doesn’t lend itself to equilibrium analysis, economists cannot believe that the fundamental structure of the economy is static. It is constantly reborn in updated preferences, political upheaval, and institutional ferment. Human minds and mathematics can never model this. But a supercomputer might help.