Milton Friedman’s Methodological Mistake|Matthew Tanous

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In 1966, well known Chicago School financial expert Milton Friedman wrote an extremely prominent essay on the method of economics entitled “The Methodology of Favorable Economics” (included in the volume Essays in Positive Economics). In identifying economics as a “favorable science”, Friedman focuses on the use of empirical examination where the “ultimate objective … is the development of a “theory” or, “hypothesis” that yields valid and meaningful (i.e., not truistic) predictions about phenomena not yet observed.” Focusing on prediction instead of explanation of observation provides the primary step wrong, however as can be seen later in his essay, Friedman does not even adhere to this requirement for prediction, stating that the theorist’s function remains in part “to define the scenarios under which the formula works or, more precisely, the basic magnitude of the mistake in its predictions under various situations.” He even goes so far as to mark down possible theories that can offer more accurate predictions, stating “it does not always pay to use the more general theory since the additional accuracy it yields may not justify the extra expense.”

In other words, Friedman is arguing that the fact of a theory’s statements does not matter. All that matters is whether the theory makes “excellent” (i.e., precise enough) predictions. The case Friedman would make versus, for instance, the phlogiston theory of combustion is not that the theory of elements is easier, explanatory of phenomena unassociated to combustion, nor that nobody has actually ever discerned a phlogiston’s existence or presented any way to do so. Instead, Friedman would merely argue that the forecasts of phlogiston theory were not accurate enough. Enough for what? I don’t know that he might even say so.

These methodological mistakes come to a head when Friedman presents an example to a “theory” that anticipates the shots of billiards players. Here Friedman informs us that we ought to

[c] onsider the issue of predicting the shots made by a specialist billiard player. It seems not unreasonable that exceptional predictions would be yielded by the hypothesis that the billiard gamer made his shots as if he understood the complex mathematical solutions that would provide the optimum instructions of travel, might approximate properly by eye the angles, and so on, explaining the location of the balls, could make lightning calculations from the formulas, and might then make the balls take a trip in the direction indicated by the solutions. Our confidence in this hypothesis is not based on the belief that billiard gamers, even professional ones, can or do go through the process explained; it derives rather from the belief that, unless in some way or other they can reaching essentially the very same outcome, they would not in fact be expert billiard gamers.

So here we have a “theory” of billiards. We can, apparently, presume that “specialist” billiards players operate in an idealistic world where they perform their shots completely utilizing exact mathematical computations and estimations. Here we can– in our theory– assume away all trouble of the video game, all inaccuracy of estimate, and all imperfection of human ability. Rather we can deal with the gamers as if they were best billiards-shooting robotics and proceed to make “reasonable” (even “outstanding”!) forecasts from there.

However it is simple to see that if we in fact tried to forecast the result of a video game of billiards– even one played by experts– in such a fashion, we would rapidly run into problems.

The first problem we would face is … what is billiards anyway? None of the complexities of the math of a billiards shot can assist you determine what the objective of the video game is: not the angles of deflection, the velocity of the cue as it strikes the cue ball, the geometry of the balls spread on the table. We should include in our theory the “rules of the game”. And here we hence introduce, by necessity, a teleological component. The billiards gamer is not making a chance at random; each and every action taken in the game is directed at a set goal (within set restrictions), and these objectives are defined by the objective and restrictions of the game as played. Whether these players are playing 8-ball or 9-ball pool, or perhaps something completely different like carom or creative billiards, is an entirely essential part to forecasting what shots a gamer will make.

Having added that in, we are still no closer to making any sort of predictions about the result of the game, however. Now we must account for the gamer’s capability to select their shot. Basically, we are presented with a praxeological aspect of action to the problem. The player, as a totally free star, can select in between different shots. Do they pick the much easier shot that might get a target ball in a pocket, however not set up the next shot well, or some harder shot that would easily lead into future shots? The assumption Friedman’s toy theory makes of important perfection may incline us to assume the most hard shots will be made each time (though it isn’t too hard to see how this would not conform to any genuine game of billiards), however even there we run into issues: two shots might be of basically equivalent mathematical “trouble”, but the player would still have to choose just one. This element of action also combines with the teleological element: the player’s objective might not match the explained role of the game, as they could be playing for another reason, such as to flaunt their skill (even if they could make more tactical shots) or make a friend feel better (state by losing deliberately). These reasons can not be divined by others, however exist only in the mind of the gamer.

Do we throw our hands up and state we can just offer likelihoods at this moment? Numerous would most likely assume this is the response that gets us out of this dilemma: a “sensible” assumption that the player makes the “best” shot (according to some mathematical criteria) with possibilities included to represent cases where there are multiple “best” shots. As natural as this escape hatch may be at first blush, we can see on more reflection that it does not actually address the issue at hand. Various players will have various determination to take risks, understandings of what it means to “line up a shot”, play choices: no mathematical model can properly represent the varying potentialities here, even when factoring in possibilities.

And all this is to state absolutely nothing of the fundamental distinctions in truth that would naturally exist: flaws in gamer capability, the table, cues, or billiard balls. Or any of the various mental and emotional pressures on a competitive gamer going up against a well-skilled challenger. These are no little facet of the outcome of numerous shots and the billiards video game itself, but a rather large element of it. Competitive billiards games can and do rest on one gamer losing focus and missing out on a shot he should have made, or mistakenly selecting one shot when they might have selected another, possibly exceptional choice. They can rest on the angle of deflection off among the table’s side boards being just slightly off– and whether gamers can adapt to these differences quickly. Even anticipating what shots players would make offered a certain table setup, which has currently proven difficult, might not permit forecasting how the video game would progress from there.

What might really be forecasted about billiards is thus rather little, and practically totally insignificant: assuming an ideal environment, the trajectory of a shot as soon as the balls are in motion can be approximately forecasted by the laws of physics. Physicists frequently use such idealizations to try to factor about separated results, using the typically accepted fact that physical forces are additive in predictable good manners to describe (however generally not anticipate) larger-scale phenomenon. Nevertheless, even the most studied physicist will acknowledge that forecasting any “real world” phenomena outside of heavily separated experiments is a task for supercomputers at finest and essentially difficult at worst. (There is a reason that quantum mechanics class workouts mainly include at many a handful of particles “in a box” and not macroscopic items comprised of billions of such particles in complicated interactions: performing the mathematical estimations would be nigh difficult with even the very best computer systems.) As the old physics student joke goes, “first, presume the horse is a sphere …”

These conclusions can just be drawn more highly when, instead of unthinking and naturally responsive items like subatomic particles or billiard balls, we present humans with objectives and choices of their own into the mix. Prediction becomes basically difficult except by rough chance, and supplies no guide towards any explanation of how things are or why. Instead we need to turn to logical reduction from fundamental facts like praxeology’s action axiom (“humans act”) and teleological constructs like imputed goals to understand what is going on. This is much more true of the big complex of human interaction that produces a national economy as it is about anything as fairly basic and intuitive as a video game of billiards.

Initially published Disinthrallment.

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