Arnold Kling comments on a post by John Taylor, who writes about the Macroeconomic Modelling and Model Comparison Network (MMCN), which
is one part of a larger project called the Macroeconomic Model Comparison Initiative (MMCI)…. That initiative includes the Macroeconomic Model Data Base, which already has 82 models that have been developed by researchers at central banks, international institutions, and universities. Key activities of the initiative are comparing solution methods for speed and accuracy, performing robustness studies of policy evaluations, and providing more powerful and user-friendly tools for modelers.
Kling says: “Why limit the comparison to models? Why not compare models with verbal reasoning?” I say: a pox on economic models, whether they are mathematical or verbal.
That said, I do harbor special disdain for mathematical models, including statistical estimates of such models. Reality is nuanced. Verbal descriptions of reality, being more nuanced than mathematics, can more closely represent reality than can be done with mathematics.
Mathematical modelers are quick to point out that a mathematical model can express complex relationships which are difficult to express in words. True, but the words must always precede the mathematics. Long usage may enable a person to grasp the meaning of 2 + 2 = 4 without consciously putting it into words, but only because he already done so and committed the formula to memory.
Do you remember word problems? As I remember them, the words came first:
John is twenty years younger than Amy, and in five years’ time he will be half her age. What is John’s age now?
Then came the math:
Solve for J [John’s age]:
- J = A − 20
- J + 5 = (A + 5) / 2
[where A = Amy’s age]
What would be the point of presenting the math, then asking for the words?
Mathematics is a man-made tool. It probably started with counting. Sheep? Goats? Bananas? It doesn’t matter what it was. What matters is that the actual thing, which had a spoken name, came before the numbering convention that enabled people to refer to three sheep without having to draw or produce three actual sheep.
But … when it came to bartering sheep for loaves of bread, or whatever, those wily ancestors of ours knew that sheep come in many sizes, ages, fecundity, and states of health, and in two sexes. (Though I suppose that the LGBTQ movement has by now “discovered” homosexual and transgender sheep, and transsexual sheep may be in the offing.) Anyway, there are so many possible combinations of sizes, ages, fecundity, and states of health that it was (and is) impractical to reduce them to numbers. A quick, verbal approximation would have to do in the absence of the real thing. And the real thing would have to be produced before Grog and Grok actually exchanged X sheep for Y loaves of bread, unless they absolutely trusted each other’s honesty and descriptive ability.
Things are somewhat different in this age of mass production and commodification. But even if it’s possible to add sheep that have been bred for near-uniformity or nearly identical loaves of bread or Paper Mate Mirado Woodcase Pencils, HB 2, Yellow Barrel, it’s not possible to add those pencils to the the sheep and the loaves of bread. The best that one could do is to list the components of such a conglomeration by name and number, with the caveat that there’s a lot of variability in the sheep, goats, banana, and bread.
An economist would say that it is possible to add a collection of disparate things: Just take the sales price of each one, multiply it by the quantity sold, and if you do that for every product and service produced in the U.S. during a year you have an estimate of GDP. (I’m being a bit loose with the definition of GDP, but it’s good enough for the point I wish to make.) Further, some economists will tout this or that model which estimates changes in the value of GDP as a function of such things as interest rates, the rate of government spending, and estimates of projected consumer spending.
I don’t disagree that GDP can be computed or that economic models can be concocted. But it is to say that such computations and models, aside from being notoriously inaccurate (even though they deal in dollars, not in quantities of various products and services), are essentially meaningless. Aside from the errors that are inevitable in the use of sampling to estimate the dollar value of billions of transactions, there is the essential meaninglessness of the dollar value. Every transaction represented in an estimate of GDP (or any lesser aggregation) has a different real value to each participant in the transaction. Further, those real values, even if they could be measured and expressed in “utils“, can’t be summed because “utils” are incommensurate — there is no such thing as a social-welfare function.
Quantitative aggregations are not only meaningless, but their existence simply encourages destructive government interference in economic affairs. Mathematical modeling of “aggregate economic activity” (there is no such thing) may serve as an amusing and even lucrative pastime, but it does nothing to advance the lives and fortunes of the vast majority of Americans. In fact, it serves to retard their lives and fortunes.
All of that because pointy-headed academics, power-lusting politicians, and bamboozled bureaucrats believe that economic aggregates and quantitative economic models are meaningful. If they spent more than a few minutes thinking about what those models are supposed to represent — and don’t and can’t represent — they would at least use them with a slight pang of conscience. (I hold little hope that they would abandon them. The allure of power and the urge to “do something” are just too strong.)
Economic aggregates and models gain meaning and precision only as their compass shrinks to discrete markets for closely similar products and services. But even in the quantification of such markets there will always be some kind of misrepresentation by aggregation, if only because tastes, preferences, materials, processes, and relative prices change constantly. Only a fool believes that a quantitative economic model (of any kind) is more than a rough approximation of past reality — an approximation that will fade quickly as time marches on.
Economist Tony Lawson puts it this way:
Given the modern emphasis on mathematical modelling it is important to determine the conditions in which such tools are appropriate or useful. In other words we need to uncover the ontological presuppositions involved in the insistence that mathematical methods of a certain sort be everywhere employed. The first thing to note is that all these mathematical methods that economists use presuppose event regularities or correlations. This makes modern economics a form of deductivism. A closed system in this context just means any situation in which an event regularity occurs. Deductivism is a form of explanation that requires event regularities. Now event regularities can just be assumed to hold, even if they cannot be theorised, and some econometricians do just that and dedicate their time to trying to uncover them. But most economists want to theorise in economic terms as well. But clearly they must do so in terms that guarantee event regularity results. The way to do this is to formulate theories in terms of isolated atoms. By an atom I just mean a factor that has the same independent effect whatever the context. Typically human individuals are portrayed as the atoms in question, though there is nothing essential about this. Notice too that most debates about the nature of rationality are beside the point. Mainstream modellers just need to fix the actions of the individual of their analyses to render them atomistic, i.e., to fix their responses to given conditions. It is this implausible fixing of actions that tends to be expressed though, or is the task of, any rationality axiom. But in truth any old specification will do, including fixed rule or algorithm following as in, say, agent based modelling; the precise assumption used to achieve this matters little. Once some such axiom or assumption-fixing behaviour is made economists can predict/deduce what the factor in question will do if stimulated. Finally the specification in this way of what any such atom does in given conditions allows the prediction activities of economists ONLY if nothing is allowed to counteract the actions of the atoms of analysis. Hence these atoms must additionally be assumed to act in isolation. It is easy to show that this ontology of closed systems of isolated atoms characterises all of the substantive theorising of mainstream economists.
It is also easy enough to show that the real world, the social reality in which we actually live, is of a nature that is anything but a set of closed systems of isolated atoms (see Lawson, [Economics and Reality, London and New York: Routledge] 1997, [Reorienting Economics, London and New York: Routledge] 2003).
Mathematical-statistical descriptions of economic phenomena are either faithful (if selective) depictions of one-off events (which are unlikely to recur) or highly stylized renditions of complex chains of events (which almost certainly won’t recur). As Arnold Kling says in his review of Richard Bookstaber’s The End of Theory,
people are assumed to know, now and for the indefinite future, the entire range of possibilities, and the likelihood of each. The alternative assumption, that the future has aspects that are not foreseeable today, goes by the name of “radical uncertainty.” But we might just call it the human condition. Bookstaber writes that radical uncertainty “leads the world to go in directions we had never imagined…. The world could be changing right now in ways that will blindside you down the road.”
I’m picking on economics because it’s an easy target. But the “hard sciences” have their problems, too. See, for example, my work in progress about Einstein’s special theory of relativity.
John Cochrane, “Mallaby, the Fed, and Technocratic Illusions“, The Grumpy Economist, July 5, 2017
Vincent Randall: “The Uncertainty Monster: Lessons from Non-Orthodox Economics“, Climate Etc., July 5, 2017
Modeling Is Not Science
Microeconomics and Macroeconomics
Why the “Stimulus” Failed to Stimulate
Baseball Statistics and the Consumer Price Index
The Keynesian Multiplier: Phony Math
Further Thoughts about the Keynesian Multiplier
The Wages of Simplistic Economics
The Essence of Economics
Economics and Science
Economists As Scientists
Economic Modeling: A Case of Unrewarded Complexity
Economics from the Bottom Up
Unorthodox Economics: 1. What Is Economics?
Unorthodox Economics: 2. Pitfalls
Unorthodox Economics: 3. What Is Scientific about Economics?
Unorthodox Economics 4: A Parable of Political Economy