Do unrealistic economic models explain real-world phenomena?

21 October, 2017 at 11:48 | Posted in Economics | 2 Comments

When applying deductivist thinking to economics, neoclassical economists usually set up ‘as if’ models based on a set of tight axiomatic assumptions from which consistent and precise inferences are made. The beauty of this procedure is, of course, that if the axiomatic premises are true, the conclusions necessarily follow. idealization-in-cognitive-and-generative-linguistics-6-728The snag is that if the models are to be relevant, we also have to argue that their precision and rigour still holds when they are applied to real-world situations. They often don’t. When addressing real economies, the idealizations and abstractions necessary for the deductivist machinery to work simply don’t hold.

If the real world is fuzzy, vague and indeterminate, then why should our models build upon a desire to describe it as precise and predictable? The logic of idealization is a marvellous tool in mathematics and axiomatic-deductivist systems, but a poor guide for action in real-world systems, in which concepts and entities are without clear boundaries and continually interact and overlap.

As Hans Albert has it:

Clearly, it is possible to interpret the ‘presuppositions’ of a theoretical system … not as hypotheses, but simply as limitations to the area of application of the system in question. Since a relationship to reality is usually ensured by the language used in economic statements, in this case the impression is generated that a content-laden statement about reality is being made, although the system is fully immunized and thus without content. In my view that is often a source of self-deception in pure economic thought …

200px-Hans_Albert_2005-2A further possibility for immunizing theories consists in simply leaving open the area of application of the constructed model so that it is impossible to refute it with counter examples. This of course is usually done without a complete knowledge of the fatal consequences of such methodological strategies for the usefulness of the theoretical conception in question, but with the view that this is a characteristic of especially highly developed economic procedures: the thinking in models, which, however, among those theoreticians who cultivate neoclassical thought, in essence amounts to a new form of Platonism.

Seen from a deductive-nomological perspective, typical economic models (M) usually consist of a theory (T) – a set of more or less general (typically universal) law-like hypotheses (H) – and a set of (typically spatio-temporal) auxiliary assumptions (A). The auxiliary assumptions give ‘boundary’ descriptions such that it is possible to deduce logically (meeting the standard of validity) a conclusion (explanandum) from the premises T & A. Using this kind of model economists are (portrayed as) trying to explain (predict) facts by subsuming them under T, given A.

An obvious problem with the formal-logical requirements of what counts as H is the often severely restricted reach of the ‘law.’ In the worst case, it may not be applicable to any real, empirical, relevant situation at all. And if A is not true, then M doesn’t really explain (although it may predict) at all. Deductive arguments should be sound – valid and with true premises – so that we are assured of having true conclusions. Constructing, e.g., models assuming ‘rational’ expectations, says nothing of situations where expectations are ‘non-rational.’

Economic theories and models have to be compared to the situations they are supposed to represent/explain/predict. There is no way of getting around questions of realism and real-world relevance. Building theories and models that are ‘true’ in their own very limited ‘idealized’ domain is of limited value if we can’t supply bridges to the real world. Economic ‘laws’ that only apply in specific ‘idealized’ circumstances —  in ‘nomological machines’ — are not the stuff that real science is built of. Results derived in mainstream economic models

Results deduced in a ‘closed world’ mainstream economic model is obtained only because the model (machine) was built for that purpose. Outside the machine — in the real world — we know that most of the assumptions, including the typical ceteris paribus condition, do not apply.

Most mainstream economic models are abstract, unrealistic and presenting mostly non-testable hypotheses. How then are they supposed to tell us anything about the world we live in?

When confronted with the massive empirical refutations of almost every theory and model they have set up, mainstream economists usually react by saying that these refutations only hit A (the Lakatosian ‘protective belt’), and that by ‘successive approximations’ it is possible to make the theories and models less abstract and more realistic, and – eventually — more readily testable and predictably accurate. Even if T & A1 doesn’t have much of empirical content, if by successive approximation we reach, say, T & A25, we are to believe that we can finally reach robust and true predictions and explanations.

There are grave problems with this modelling view. What Hans Albert most forcefully is arguing with his ‘Model Platonism’ critique of mainstream economics, is that there is a strong tendency for modellers to use the method of successive approximations as a kind of ‘immunization,’ taking for granted that there can never be any faults with the theory. Explanatory and predictive failures hinge solely on the auxiliary assumptions. That the kind of theories and models used by mainstream economics should all be held non-defeasibly corroborated, seems, however — to say the least — rather unwarranted.

Confronted with the massive empirical failures of their models and theories, mainstream economists often retreat into looking upon their models and theories as some kind of ‘conceptual exploration,’ and give up any hopes/pretences whatsoever of relating their theories and models to the real world. Instead of trying to bridge the gap between models and the world, one decides to look the other way.

To me, this kind of scientific defeatism is equivalent to surrendering our search for understanding the world we live in. It can’t be enough to prove or deduce things in a model world. If theories and models do not directly or indirectly tell us anything of the world we live in – then why should we waste any of our precious time on them?



  1. I have seen it so many times in these kinds of writings–the inability to separate micro-from macro-economics.

    Micro- must naturally be fussy and vague because here we need to look at so many similar situations as independent happenings, How is it possible to understand this, unless we begin to take aggregates, which has the happy result of converting micro- into macro-. However the real-world situations of doing this cannot exactly cover each separate micro- issue, and this leads to a degree of dissatisfaction, but its the micro-economists fault for thinking that his/her methods can be made sufficiently generally applicable about small things.

    There is an analogy here with the gas laws of physics and molecular Brownian movements. In a gaseous fluid, the molecules are independently moving in many directions and their collisions and the results of their changes in temperature, distribution within the available space (density) and velocity all seem to be random. Yet when we consider the average effects we can derive the straight-forward properties (laws) of the gas as a whole, in terms of its density, pressure and temperature.

    Another way of expressing this idea is to claim that the would be explanations are attempting to do too much, and its is necessary for them to focus their thoughts into more easily resolved issues.

  2. I think financial firms can synthetically construct sections of markets where they can use math to hedge everything and still win. It’s like a bookmaker: if he calculates his odds correctly, he takes all bets and payouts cancel and he makes money from the vigorish. The “vig” is like interest and the odds are like a matched book dealer. The dealer doesn’t care who wins or loses just as the bookie doesn’t; they calculate the odds in such a way that they win on interest at least, no matter what happens in the event (or the market, for dealers).

    Uncertainty for dealers takes the form of funding liquidity: will they be able to roll over funding? Mortgage-backed assets were suddenly devalued by fearful, labile traders; that was the “shock” that precipitated the 2008 financial crisis. So the uncertainty is really psychological: how likely is it that traders will get emotional? I put that risk at 100% and thus I would insure everything so I’m like a bookie with odds, making money off the vig no matter who wins the bets … and if I’m betting I would also insure against losing the bet … that’s how big firms use math models to make money. With the Fed there as backstop in case of a crisis …

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