Jim and Noah’s Excellent Adventure, Part 1

As always, Noah Millman has raised some excellent questions about what I’ve written. This post address the first part of his response, and I’ll try to address the second part in the next post.

Noah’s first question is whether, by the same logic applied to economics, I consider geology to be a science.

Let me start with the purpose of science as I see it. To quote Francis Bacon, from Novum Organum, the fountainhead of the scientific method: “the true and lawful goal of the sciences is none other than this: that human life be endowed with new discoveries and powers.” More specifically, the purpose of science is to create useful, reliable and non-obvious rules that allow us to predict the effects of potential interventions we might make on the physical world. Everything else – data collection, equations, laboratories, scholarly journals, tenure committees, all of it – are means to this end.

The method that science uses to do this resists formal algorithmic description, and varies somewhat by specialty and over time, but broadly includes hypothetico-deductive reasoning for building theories, and controlled experiments for testing them. The first third of my upcoming book is an attempt to describe this method, so I won’t try to do it here, beyond this very high-level description.

The fifth chapter of the book is titled “Science without Experiments.” In it, I distinguish between two kinds of fields that are generally considered science, but that do not rely much on experiments: (1) so-called historical sciences, such as geology or parts of evolutionary biology, for which most experiments are impossible in principle because these fields address only past events; and (2) fields which make forward predictions, but for which most experiments are infeasible, the principle example being the astronomical sciences. Without trying to recapitulate the whole argument here, my view is that to the extent that historical science contributes to the development of predictive rules that are subsequently tested and corroborated, it is scientific; to the extent that it does not, it is not. (Of course, we have to recognize that the route this contribution takes can be very long and circuitous.)

Noah goes on to say:

Economists can predict all kinds of boring things with great accuracy. Even things we’re interested in, they do well-enough at that people with money on the line – investors, business owners; not just politicians – rely on economic forecasts all the time. They aren’t perfect, but they are better than any available alternative. No, they can’t predict the things we’re really interested in – such as when the next recession will hit. But the geologists can’t predict the next earthquake either.

Here’s a very short excerpt from the book on this question (using the analogy to weather forecasting, rather than earthquake forecasting):

[N]ot much that is practically important about the overall development of the economy is predictable in the long-term. Short-term forecasts of complex systems can sometimes be made on a combination of “momentum” and the assumption of relatively simple set of causal mechanisms. Five-day weather forecasts can be made, for example, by more complex versions of the observation that the weather in Ohio today is highly correlated with the weather in Virginia a couple of days later because of consistent wind patterns.

“GDP will likely grow by 1.3% next quarter” kinds of forecasts that are better than naive forecasts (what meteorologists term forecasts with “skill”) are useful to have. Check. They give us material information about the near-term background against which our interventions will be executed. Further, it is often the “scientific-seeming” aspects of meteorology (lots more monitoring stations, satellites and super-computers, that collectively create a much more granular finite element model) and economics (analogously, larger data sets and more megaflops) that allow increasing skill. Check.

But because they lack a robust understanding of the extraordinarily dense causal network that governs the system in question (using “causal” here in the operational sense of the ability to make accurate predictions about the difference between the world in which we execute an intervention as compared to the counterfactual world where we do not), they do not allow us to make reliable, non-obvious predictions about the result of proposed actions. This is closely related to my fundamental criticisms of economics and other social sciences – not that they can’t do this at all, but they have extremely limited ability to do so, and many social scientists (or often, social science popularizers) make wildly excessive claims about this capability. We ought, in my view, to premise our political economy around an accurate understanding of our ignorance.

At a certain level, it seems to me to be an enormously obvious case of the emperor’s new clothes that when you go from a discussion at an abstract level like this to a very practical level – “OK, please state the rules developed by economics that can actually make useful, reliable and non-obvious predictions about the result of alternative proposed courses of government action on the issues of the day” – you get a pretty underwhelming result: non-falsifiable statements, which 10% – 20% of the practitioners in the field dispute. There are obviously debates in frontier areas of all science, but at the level of things that can be said in a freshman textbook, there is also a body of agreed-upon causal laws that, crucially, have been converted to a body of engineering praxis that can be used to solve real problems. In all of the back-and-forth with economics bloggers on this question, I haven’t yet been confronted with anything approaching F=MA plus a long, long series of experiments showing that this is an excellent engineering approximation at terrestrial scale and speed that is the basis for the following machines that will produce X effect based on Y intervention, or a list of approved drugs, derived from the following work in biology labs, that have shown in clinical trials that when you introduce the following chemical into the bloodstream of a human with the following measurable indicators of a disease state, it will change those indicators with the following distribution of change. When it comes to economics, where’s the beef?