In the latest issue of the Real World Economics Review Gustavo Marqués has an interesting paper entitled A Plea for Reorienting Philosophical Attention From Models to Applied Economics. In the paper Marqués examines some of the philosophical justifications that have been provided for the practice of economic modelling. In his survey he deals with three authors: Cartwright, Colander and Alexandrova. We will deal here with each in turn.
Cartwright, who I wrote about on this blog recently, provides a rather strange defence of abstract modelling. She claims that we should view models as ‘parables’. Marqués summarises her argument as such:
Parables, however, shed (or perhaps it would be better to say “suggest”) a lesson, that is not contained in the model itself, but must somehow be built from the outside taking into account relevant portions of available background knowledge. This means that models can have a “correct” lesson within them, but it must be partly construed out of the materials provided by the model on the basis of theoretical and extra theoretical knowledge. (p35)
Actually, I think that this is a rather common defence of modelling. Yes, a few philosophically unsophisticated modellers still today try to test their models against data using a variety of mathematical methods based on probability theories — whether Bayesian or frequentist. But these are usually those lower down in the intellectual pecking order. The more sophisticated theorists generally do assume something like a ‘parable’ defence of modelling — I think of Frank Hahn’s use of the general equilibrium framework to justify the use of Keynesian economics for real world policy.
This comes with various problems that Marqués points out which include the fact that the model might not deliver the right ‘abstract’ lesson; that there may be a variety of differing, even contradictory lessons from a given model and the modeller may pick up on the one that best suits their purpose; and, most damningly I think, that if models are just there to give out such parables why on earth need they be so mathematically bulky and precise if the lesson they impart is straightforward and imprecise.
But there is another serious objection to such an interpretation of models. That is, it sets them up as ‘myths’ in the anthropological sense. Anthropologists recognise that such myths provide the foundations upon which structuring principles for societies are built. They also recognise that those that interpret such myths — usually soothsayers, shamans or priests of some sort — are imbued with a strange sort of aura. If we start interpreting models in the same way as shaman interpret myths not only do we give the models a quasi-religious weight that seems rather primitivistic, but we also imbue economists with a mystical aura similar to that of a shaman or priest. I would imagine that, articulated in this way, many would be very cautious about turning economics into a sort of primitive religion.
Colander’s argument is more interesting. He points out that those using the dominant modelling framework prior to the crisis — that is, the DSGE framework — were actually blinded to the problems building up in the sub-prime mortgage market. The model, on this reading, acted as a sort of myopic filter that hid what was ultimately a very obvious reality — i.e. that mortgages were being handed out to less than creditworthy customers — that anyone not using the model would likely have picked up on.
While Colander’s criticism is interesting and important, his proposals for solutions are weak. First, he advocates that even more complex models are needed — a typical refrain heard from the modelling community. Secondly, he believes that economists should be trained better to pick the correct model given what is being observed. Colander, of course, misses entirely that these two goals may be completely in conflict with one another.
Contemporary models like the DSGE are unwieldy enough and require a lot of investment to get one’s head around (personally, I have never bothered with the intricacies). If we make the models even more complex, it seems to be pretty fantastic to assume that economists can both spend their time building and understanding such constructions while at the same time focusing on how to apply them.
Colander also doesn’t take into account the rather obvious fact that some people are just not suited to one or the other tasks. Some people cannot think in terms of highly abstract models, while others cannot think outside of this simply due to the way their mind works. Very few people can do both sufficiently well to take Colander’s approach — especially if we consider that many people must walk away with only an undergraduate or masters level training in economics to work in the world.
Colander’s rather fantastic and idealistic expectations of future economists hides what is a simple binary choice: do we want a profession dominated by abstract modellers whose ability to do applied work is seriously sub-par; or do we want a profession dominated with people who eschew precision and perfection in order to deal with the nitty-gritty of applied economics? We cannot, as Colander thinks, have it both ways and if we try the modellers will likely win out, as they become myth-makers carrying unwieldy contraptions that only they can ‘read’.
Alexandrova’s ideas suffer similar problems. She wants to use models as hypothesis-generating machines that we can then apply. But she provides no means by which we can make this transition. Thus, rather than being a solution, her paper merely provides a sort of taxonomy of what needs to be done — Step 1: build model to obtain an hypothesis; Step 2: make sure that the assumptions built in the model are applicable in the real-world… and so on.
Alexandrova’s approach suffers from what might be referred to as the “underpants gnomes profit plan problem” from the South Park cartoon. The problem is adequately outlined in the short clip below.
As Marqués writes,
Unless a clear connection between both programs can be exhibited (something that Alexandrova’s paper fails to show) to get busy in building models diverts resources from the technological approach of directly “building” in practice the desired result. This construction, it seems, does not need at all any of the solutions offered within the model. (p41)
It appears that Marqués runs into effectively the same problem over and over again. That is, each approach assumes some knowledge on the part of the users of the models that does not come (a) from the practice of modelling itself or (b) from the models once they are complete. This knowledge, which is identical to the ‘???’ in the underpants gnomes profit plan, is what I have referred to above as the ‘aura’ given to shamans and priests when they interpret myths. The myths here are, of course, the models and their interpretation is undertaken by the modeller.
The reason that this problem keeps arising is because each of the three authors surveyed by Marqués wants to keep the structure in which models become like myths and their interpretation carries with it a certain aura intact. Put more simply: none of the authors surveyed want to conclude that economics as a discipline should probably de-emphasise the role played by models in economics and instead train economists to do applied work in which they lay out clearly and explicitly their process of reasoning.
Such an approach would not only ensure that economics does not become a modern system of myth-making, but it would also allow economists to have rational arguments where instead today they just try to batter each other over the head with their models in a sort of a “my model is better than your model” display of silliness which rarely leads an argument to any conclusion. One is thus reminded of an old well-known quote from Keynes:
If economists could manage to get themselves thought of as humble, competent people on a level with dentists, that would be splendid.