Home > Uncategorized > How the model became the message in economics

How the model became the message in economics

from Lars Syll

In The World in the Model Mary Morgan gives a historical account of how the model became the message in economics. On the question of how the models provide a method of enquiry where they can function both as “objects to enquire into” and as “objects to enquire with”, Morgan argues that model reasoning involves a kind of experiment. She writes:

It may help to clarify my account of modelling as a double method of enquiry in economics if we compare it with two of the other reasoning styles … the method of mathematical postulation and proof and the method of laboratory experiment.

If we portray mathematical modelling as a version of the method of mathematical postulation and proof … models can indeed be truth-makers about that restricted and mathematical small world … But whether they can come to valid conclusions about the behaviour of their actual economic universe is a much more difficult problem …

If we make the alternative comparison with laboratory experiments … the important question of whether the results of the experiment on the model can be transferred to the world that the model represents can be considered an inference problem …

Of course, model experiments in economics are usually pen-and-paper, calculator, or computer, experiments on a model world or an analogical world … not laboratory experiments on the real world …

The experiments made on models are different from the experiments made in the laboratory … because model experiments are less powerful as an epistemic genre. It does make a difference to the power and scope of inference that the model experiment is carried out on a pen-and-paper represenation, that is on the world in the model, not on the world itself.

Now, I think it is but fair to say that field experiments, model experiments and laboratory experiments, are basically facing the same problems in terms of generalizability and external validity. They all have the same basic problem — they are built on rather artificial conditions and have difficulties with the trade-off between internal and external validity. The more artificial conditions, the more internal validity, but also less external validity. The more we rig experiments/field studies/models to avoid confounding, the less the conditions are reminiscent of the real target system.

The nodal issue is how economists using different isolation strategies in different ‘nomological machines’ attempt to learn about causal relationships. By contrast with Morgan, I would more explicitly and forcefully argue that the generalizability of all these research strategies — because the probability is high that causal mechanisms are different in different contexts and the lack of homogeneity/stability/invariance — doesn’t give us warranted export licenses to the real target system.

If we mainly conceive of laboratory experiments, field studies and model experiments as heuristic tools, the dividing line is difficult to perceive. But if we see them as activities that ultimately aspire to say something about the real target system, then the problem of external validity is central. Let me elaborate a little on this point:

Assume that you have examined how the performance of A is affected by B (treatment). How can we extrapolate/generalize to new samples outside the original population? How do we know that any replication attempt succeeds? How do we know when these replicated experimental results can be said to justify inferences made in samples from the original population? If, for example, P(A|B) is the conditional density function for the original sample, and we are interested in doing an extrapolative prediction of E [P*(A|B)], how can we know that the new sample’s density function is identical with the original? Unless we can give some really good argument for this being the case, inferences built on P(A|B) is not really saying anything on that of the target system’s P*(A|B).

As I see it is, this is the heart of the matter. External validity/extrapolation/generalization is founded on the assumption that we could make inferences based on P(A|B) that is exportable to other populations for which P*(A|B) applies. Sure, if one can convincingly show that P and P* are similar enough, the problems are perhaps surmountable. But arbitrarily just introducing functional specification restrictions of the type invariance/stability/homogeneity, is, at least for an epistemological realist far from satisfactory. And often it is – unfortunately – exactly this that I see when I take part in mainstream economists’ models/laboratory experiments/field studies.

By this, I do not mean to say that empirical methods per se are so problematic that they can never be used. On the contrary, I am basically — though not without reservations — in favour of the increased use of laboratory experiments and field studies within economics. Not least as an alternative to completely barren ‘bridge-less’ axiomatic-deductive theory models. My criticism is more about aspiration levels and what we believe that we can achieve with our mediational epistemological tools and methods in the social sciences.

Many laboratory experimentalists claim that it is easy to replicate experiments under different conditions and therefore a fortiori easy to test the robustness of experimental results. But is it really that easy? If in the example given above, we run a test and find that our predictions were not correct – what can we conclude? That B works in X but not in Y? That B worked in the field study conducted in year Z but not in year W? Population selection is almost never simple. Had the problem of external validity only been about inference from sample to population, this would be no critical problem. But the really interesting inferences are those we try to make from specific laboratory experiments/fields to specific real-world situations/structures that we are interested in understanding or (causally) to explain. And then the population problem is more difficult to tackle.

Nowadays many economists are in for randomized experiments. But just as most other methods used within neoclassical economics, randomization is basically a deductive method — or as Morgan calls it, “a deductive mode of manipulation”. Given the assumptions (such as manipulability, transitivity, separability, additivity, linearity etc) these methods deliver deductive inferences. The problem, of course, is that we will never completely know when the assumptions are right. Real target systems are seldom epistemically isomorphic to our axiomatic-deductive models/systems, and even if they were, we still have to argue for the external validity of the conclusions reached from within these epistemically convenient models/systems. Causal evidence generated by randomization procedures may be valid in laboratory models, but what we usually are interested in, is causal evidence in the real target system we happen to live in.

Ideally controlled laboratory experiments (still the benchmark even for natural and quasi-experiments) tell us with certainty what causes what effects — but only given the right ‘closures’. Making appropriate extrapolations from (ideal, accidental, natural or quasi) experiments to different settings, populations or target systems, is not easy. It works there is no evidence for it will work here. Causes deduced in a laboratory experiment still have to show that they come with an export-warrant to the target population/system. The causal background assumptions made have to be justified, and without licenses to export, the value of “rigorous” and “precise” methods is despairingly small.

Most mainstream economists want to have deductively automated answers to fundamental causal questions. But to apply ‘thin’ methods we have to have ‘thick’ background knowledge of what’s going on in the real world, and not in (ideally controlled randomized) laboratory experiments or ‘models experiment’. Conclusions can only be as certain as their premises – and that also goes for methods based on laboratory experiments.

Morgan’s book — and especially those carefully selected case studies presented — is an important contribution to the history of economics in general, and more specifically to our understanding of how mainstream economics has become a totally model-based discipline.

However, I haven’t read it without objections.

In her description of how economists have used — and are using — these ‘reasoning tools’ that we call models, Morgan puts too much emphasis ” at least for my taste –on modelling as an epistemic genre of “reasoning to gain knowledge about the economic world”. Even if epistemology is important and interesting in itself, it ought never to be anything but secondary in science, since the primary questions asked have to be ontological. First after having asked questions about ontology can we start thinking about what and how we can know anything about the world. If we do that, I think it is more or less necessary also to be more critical of the reasoning by  modelling that has come to be considered the only and right way to reason in mainstream economics for more than 50 years now.

In a way, it is rather symptomatic of the whole book that when Morgan gets into the all-important question of external validity in isolationist closed economic models, she most often halts at posing the question as “if those elements can be treated in isolation” and noting that this aspect of models is “much more difficult to characterize than the way economists use models to investigate their ideas and theories”. Absolutely! But this doesn’t make model reasonings as ‘objects to enquire’ into activities that from a scientific point of view are on a par with the much more important question if these models really have export-certificates or not. I think many readers of the book would have found it even more interesting to read if they would get more of argued and critical evaluations of the activities, and not just more or less analytical descriptions.

So, by all means, read Morgan’s book. It’s in many ways a superb book. As a detailed and well-informed case studies-based history it is definitely a proof of great scholarship. But to get more on the question if the economists’ models really give truthful and valid explanations on things happening in the real world, I would also recommend reading two other modern classics — Tony Lawson’s Economics and Reality (1997) and Nancy Cartwright’s Hunting Causes and Using Them  (2009).

  1. Ikonoclast
    May 18, 2020 at 11:38 pm

    In an earlier post Lars Syll mentioned or quoted the issue that scientists were “smart” because they restricted models to laboratories where conditions were controlled. Essentially, this leaves out the issue of the successes and failures of applied science. Science does not just work in the laboratory. It works in the real world as applied science. Of course, we have to further analyze how it works and how it does not work. Coal fired power stations work to generate some energy for useful work, while releasing a lot of waste energy and CO2. They do not work to preserve our benign Holocene climate. Indeed they work to destroy it. This raises the whole issue of unforeseen consequences in complex real systems but let us set that aside for now.

    We need an ontological explanation for good modelling results versus poor modelling results. Mary S. Morgan may or may not provide this explanation. I don’t know as I have not read the book in question. The ontological explanation must relate to the correspondence theory of truth. There really is no other candidate for explanation. The model must possess some consistent homomorphic correspondences between the model system and the real system. It was Bertrand Russel who pointed out the need for homomorphic correspondence in this context.

    Science models a real system with a notional system (often the notation of mathematics). There is a clear demarcation between the real system and the notional model and there is a well-founded assumption that the hard-science-discovered fundamental laws of real systems are consistent across real systems in at least our “local” time and place in the cosmos.

    Economics models a real system / notional system hybrid with notional models. There is not a clear demarcation between the real and the notional. The real economy is real but the money/financial economy and all the legal laws and regulations which support it are notional. “Notional” here also contains the meaning that they are social fictive creations, that is to say they are human notions. Every legal law and regulation made one way could be made another way. These legal laws and regulations are not fundamental in the sense that the fundamental laws discovered by science are fundamental.

    Economics in this sense equates to attempting to make a science of the purely notional, the fictive and the imaginary, including the social and political. It is a nonsense proposition. Instead, we must take recourse in ethics and in the hard sciences of real systems. Economic decisions must be made on ethical grounds first and then subjected to scientific analysis to find what is possible. Economic decisions must not be made on notional money calculations based on elite private property rights.

    The first decisions to be made are what is ethically and morally correct to do according to our belief systems and as decided democratically in a pluralistic society. Is it moral to have widespread poverty and homelessness (as an example)? The answer for the majority of people is clearly “no”. The next question is this. Is it physically possible to alleviate and even abolish poverty and homelessness in a developed country (as the first and easiest example)? The answer clearly is “yes”. We have enough resources and productive capacity. Since it is physically possible it should be done. The only argument against this comes from the argument for the privilege of great possession of private property and riches. According to this ethic, some should be permitted to keep excessive property and riches despite its corollary of poverty and homelessness.

    The answer is simple in moral, scientific and logical terms though apparently difficult in realpolitik terms due to the power of rich privilege. Abolish the privilege of great private property holdings, abolish the privilege of riches. Do this to alleviate poverty and homelessness and to help all humans achieve their full potential. It is simple moral philosophy. Economics is or should be the mere administrative allocation of resources after the moral decisions and according to what is scientifically/tewchnically and ecologically/sustainably possible.

    Don’t make a big deal of economics. It’s a third or fourth order concern as John Ralston Saul has correctly identified.

  2. Robert Locke
    May 19, 2020 at 9:59 am

    Listen to the historiansm.MARY morgan had a handle on this already when i worked with her in the business history unir at the London School of Economics and Political Science in the 1980s,Lars is still messing around.

    • May 19, 2020 at 2:00 pm

      On the whole I agree with Ike, but Bertrand Russell could only justify his correspondence theory of truth by postulating his theory of types: in other words what Kant argued, Frank Salter’s dimensional analysis and the possibility other philosophers avoid as “category confusion”. That’s what the scientific language Algol68 was all about.

      I’m beginning to feel sorry for Lars. We do what we can, and if you are an academic it is not surprising if what you do is a bit academic.

  3. Jorge Buzaglo
    May 19, 2020 at 2:08 pm

    An interesting, and I think, generalizable distinction is often made in physics between reality and our knowledge of reality. “Reality,” as a something existing outside and independent of human consciousness is a metaphysical assumption, an unfalsifiable hypothesis. In the context of the physics distinction, models do not represent reality, but our knowledge of reality. Our knowledge of economic reality is, for sociopolitical and other reasons, much more divided and contested than our knowledge of physical reality.

    • May 19, 2020 at 6:32 pm

      What Jorge does not seem willing to acknowledge is that the assumption made by some physicists that reality only exists in human consciousness is a metaphysical assumption no less falsifiable in the logical positive sense than the realist one, but totally insane when one examines the historical and archaeological record.

  4. João Pedro
    May 20, 2020 at 10:15 am

    Being this dismissive of economics is non sensical. Economics is not only a tool to alleviate problems, It’s also descriptive of human behaviour and It’s consequences. Even if a goal hasn’t been defined, economics is still there and affects society to a great extent. Economical problems won’t just not exist if a moral and ethical goal for hasn’t been set, and the allocation of resources leads to significant consequences indenpendently from a major centralized goal. A lot of economics describes more espontaneous reactions, so relegating It to a “third or forth order corncern” and subject It exclusively to the use of other disciplines would be harmful in many ways

  5. Jorge Buzaglo
    May 20, 2020 at 10:38 am

    More about reality. Reality could be conceived as an infinite-dimensional, unbounded manifold. We clearly can observe the three dimensions of Euclidean space, plus the time dimension. All the past, present and future events of the universe are inscribed in the infinite-dimensional unbounded manifold of reality. (As a corollary, everything, included you and me, are eternal in reality.) But because of the many infinities in dimensions and boundaries, the reality manifold is not knowable in any detail.

  6. ghholtham
    May 20, 2020 at 1:04 pm

    I knew an economist whose fate was to work in the financial sector and to make forecasts, on which his pay, if not his job, depended. He was generally successful. He had a model, which like all economic models had behavioural equations and identities, i.e definitions linking variables. Most large macro models contain as many identities as behavioural equations. His practice was to look at the behavioural equations (derived, obviously from past observations of the variable in question) then throw them away and impose his own view on where that variable was going. Yet he claimed it was impossible to forecast without a model. Firstly extrapolation of the past was a useful jumping off point when considering the future. Secondly all those identities kept him consistent and ensured that his views permeated the whole forecast in a coherent way. Sometimes he found himself forecasting inconsistent things and the model made him think again. Models are tools, not truth machines. And he was right. Perhaps you can’t forecast well anyway, but you can’t forecast sensibly without a model.

    • May 20, 2020 at 4:21 pm

      Jorge, did your infinity of dimensions exist at the Big Bang? From the behaviour of radio waves, no.

      Gerald, excellent story and I agree with your conclusions. You cannot see in advance how traffic flows will split up, but a good map can show you where to look.

  7. Jorge Buzaglo
    May 20, 2020 at 5:09 pm

    A non-existing universe (before the “big bang”) is as difficult to conceive as an infinite universe. Personally, I prefer to think about the necessarily existing infinite universe/reality in which all lives. By the way, I must confess that I find a bizarre arrogance of physicists to postulate what happened within the first nanoseconds of the existence of the world 14 billion years ago. This is much less poetical, but as mythical as the religious creation stories. Less arrogant, but much more useful would be of physicists to predict the weather tomorrow, which they still cannot. But “big bang” sounds like a formidable explosion, and boys like explosions.

    • May 21, 2020 at 9:51 am

      I agree about your “What was there before the Big Bang?” problem. That story is at least consistent with the conservation of energy, but Pascal’s Wager seems to be the logical answer. But why make it? On holiday in Turkey recently we were shown how the early Christians lived in tunnels to avoid slavery or worse. I’m not sure they would have “preferred to think about the necessarily existing infinite universe in which [they] all lived”. They’d have preferred something a bit more hopeful.

  8. May 25, 2020 at 3:48 am

    It is just part of the establishment gaslighting tendency whereby what was once seen as bad becomes the accepted way. It might have started with ‘sophisticate’:

    ” attested about 1400 in the sense “make impure by admixture”, from Medieval Latin sophisticatus, past participle of sophisticare (see sophistication). From about 1600 as “corrupt, delude by sophistry”; from 1796 as “deprive of simplicity”. Related: sophisticated, sophisticating. As a noun meaning “sophisticated person” from 1921″

    (I wonder if PR wizard Edward Bernays had any hand in that ‘bad is good’ reversal of meaning?)

    All these ‘models’ are just smokescreening ‘perpetual motion machines’ to keep the ordinary populace from appreciating just how badly they’re being exploited. The illusion is even given fake respectability by pseudo ‘Nobel Prizes’ that are nothing to do with the real ones, but treated just the same in the establishment media.

    The CoViD-19 ‘shutdown’ experience, will have opened some eyes: one can only hope.

    https://en.m.wiktionary.org/wiki/sophisticate#English

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