When the model is the message – modern neoclassical economics

25 May, 2011 at 10:59 | Posted in Economics, Theory of Science & Methodology | Comments Off on When the model is the message – modern neoclassical economics

Most models in science are representations of something else. Models “stand for” or “depict” specific parts of a “target system” (usually the real world). A model that has neither surface nor deep resemblance to important characteristics of real economies ought to be treated with prima facie suspicion. How could we possibly learn about the real world if there are no parts or aspects of the model that have relevant and important counterparts in the real world target system? The burden of proof lays on the theoretical economists thinking they have contributed anything of scientific relevance without even hinting at any bridge enabling us to traverse from model to reality. All theories and models have to use sign vehicles to convey some kind of content that may be used for saying something of the target system. But purpose-built assumptions, like invariance, made solely to secure a way of reaching deductively validated results in mathematical models, are of little value if they cannot be validated outside of the model.

All empirical sciences use simplifying or unrealistic assumptions in their modeling activities. That is (no longer) the issue – as long as the assumptions made are not unrealistic in the wrong way or for the wrong reasons.

Theories are difficult to directly confront with reality. Economists therefore build models of their theories. Those models are representations that are directly examined and manipulated to indirectly say something about the target systems.

There are economic methodologists and philosophers that argue for a less demanding view on modeling and theorizing in economics. And to some theoretical economists it is deemed quite enough to consider economics as a mere “conceptual activity” where the model is not so much seen as an abstraction from reality, but rather a kind of “parallel reality”. By considering models as such constructions, the economist distances the model from the intended target, only demanding the models to be credible, thereby enabling him to make inductive inferences to the target systems.

But what gives license to this leap of faith, this “inductive inference”? Within-model inferences in formal-axiomatic models are usually deductive, but that does not come with a warrant of reliability for inferring conclusions about specific target systems. Since all models in a strict sense are false (necessarily building in part on false assumptions) deductive validity cannot guarantee epistemic truth about the target system. To argue otherwise would surely be an untenable overestimation of the epistemic reach of “surrogate models”.

Models do not only face theory. They also have to look to the world. But being able to model a credible world, a world that somehow could be considered real or similar to the real world, is not the same as investigating the real world. Even though all theories are false, since they simplify, they may still possibly serve our pursuit of truth. But then they cannot be unrealistic or false in any way. The falsehood or unrealisticness has to be qualified (in terms of resemblance, relevance etc). At the very least, the minimalist demand on models in terms of credibility has to give away to a stronger epistemic demand of “appropriate similarity and plausibility” (Pålsson Syll 2001:60). One could of course also ask for a sensitivity or robustness analysis, but the credible world, even after having tested it for sensitivity and robustness, can still be a far way from reality – and unfortunately often in ways we know are important. Robustness of claims in a model does not per se give a warrant for exporting the claims to real world target systems.

Anyway, robust theorems are exceedingly rare or non-existent in economics. Explanation, understanding and prediction of real world phenomena, relations and mechanisms therefore cannot be grounded (solely) on robustness analysis. Some of the standard assumptions made in neoclassical economic theory – on rationality, information-handling and types of uncertainty – are not possible to make more realistic by “de-idealization” or “successive approximations” without altering the theory and its models fundamentally.

If we cannot show that the mechanisms or causes we isolate and handle in our models are stable, in the sense that what when we export them from are models to our target systems they do not change from one situation to another, then they only hold under ceteris paribus conditions and a fortiori are of limited value for our understanding, explanation and prediction of our real world target system. If the world around us is heterogeneous and organic, mechanisms and causes do not follow the general law of composition. The analogy of vector addition in mechanics simply breaks down in typical economics cases.

The obvious ontological shortcoming of the epistemic approach is that “similarity” or “resemblance” tout court do not guarantee that the correspondence between model and target is interesting, relevant, revealing or somehow adequate in terms of mechanisms, causal powers, capacities or tendencies. No matter how many convoluted refinements of concepts made in the model, if the model is not similar in the appropriate respects (such as structure, isomorphism etc), the surrogate system becomes a substitute system that does not bridge to the world but rather misses its target.

To give up the quest for truth and to merely study the internal logic of credible worlds is not compatible with scientific realism. One has to – as Mäki (2009:41) argues

infer to conclusions about the world that are true or are likely to be true about the world … Justified model-to-world inference requires the model to be a credible surrogate system in being conceivable and perhaps plausible insofar as what it isolates – the mechanism – is concerned.

I find constructing “minimal economic models” or using models as “stylized facts” or “stylized pictures” somehow “approximating” reality, rather unimpressive attempts at legitimizing using fictitious idealizations for reasons more to do with model tractability than with a genuine interest of understanding and explaining features of real economies. Many of the model-assumptions standardly made by neoclassical economics are restrictive rather than harmless and could a fortiori anyway not in any sensible meaning be considered approximations at all.

Why should we be concerned with economic models that are purely hypothetical constructions? Even if a constructionist approach should be able to accommodate the way we learn from models, it is of little avail to treat models as some kind “artefacts” or “heuristic devices” that produce claims, if they do not also connect to real world target systems.

The final court of appeal for economic models is the real world, and as long as no convincing justification is put forward for how the inferential bridging de facto is made, credible counterfactual worlds is little more than “hand waving” that give us rather little warrant for making inductive inferences from models to real world target systems. Inspection of the models shows that they have features that strongly influence the results obtained in them and that will not be shared by the real world target systems. Economics becomes exact but exceedingly narrow.

If substantive questions about the real world are being posed, it is the formalistic-mathematical representations utilized to analyze them that have to match reality, not the other way around. As Keynes (1971-89:296) has it:

Economics is a science of thinking in terms of models joined to the art of choosing models which are relevant to the contemporary world. It is compelled to be this, because, unlike the natural science, the material to which it is applied is, in too many respects, not homogeneous through time.

The theories and models that economists construct describe imaginary worlds using a combination of formal sign systems such as mathematics and ordinary language. The descriptions made are extremely thin and to a large degree disconnected to the specific contexts of the targeted system than one (usually) wants to (partially) represent. This is not by chance. These closed formalistic-mathematical theories and models are constructed for the purpose of being able to deliver purportedly rigorous deductions that may somehow by be exportable to the target system. By analyzing a few causal factors in their “laboratories” they hope they can perform “thought experiments” and observe how these factors operate on their own and without impediments or confounders.

Unfortunately, this is not so. The reason for this is that economic causes never act in a socio-economic vacuum. Causes have to be set in a contextual structure to be able to operate. This structure has to take some form or other, but instead of incorporating structures that are true to the target system, the settings made in economic models are rather based on formalistic mathematical tractability. In the models they appear as unrealistic assumptions, usually playing a decisive role in getting the deductive machinery deliver “precise” and “rigorous” results. This, of course, makes exporting to real world target systems problematic, since these models – as part of a deductivist covering-law tradition in economics – are thought to deliver general and far-reaching conclusions that are externally valid. But how can we be sure the lessons learned in these theories and models have external validity, when based on highly specific unrealistic assumptions? As a rule, the more specific and concrete the structures, the less generalizable the results. Admitting that we in principle can move from (partial) falsehoods in theories and models to truth in real world target systems does not take us very far, unless a thorough explication of the relation between theory, model and the real world target system is made. If models assume representative actors, rational expectations, market clearing and equilibrium, and we know that real people and markets cannot be expected to obey these assumptions, the warrants for supposing that conclusions or hypothesis of causally relevant mechanisms or regularities can be bridged, are obviously non-justifiable. To have a deductive warrant for things happening in a closed model is no guarantee for them being preserved when applied to an open real world target system.

Economic theorists ought to do some ontological reflection and heed Keynes’ (1964 [1936]:297) warnings on using laboratory thought-models in economics:

The object of our analysis is, not to provide a machine, or method of blind manipulation, which will furnish an infallible answer, but to provide ourselves with an organized and orderly method of thinking out particular problems; and, after we have reached a provisional conclusion by isolating the complicating factors one by one, we then have to go back on ourselves and allow, as well as we can, for the probable interactions of the factors amongst themselves. This is the nature of economic thinking. Any other way of applying our formal principles of thought (without which, however, we shall be lost in the wood) will lead us into error.

If not, we will have to keep on wondering on what planet the neoclassical economists are living.

 

References

Keynes, John Maynard. (1964 [1936]). The General Theory of Employment, Interest, and Money. London: Harcourt Brace Jovanovich.

Keynes, John Maynard. (1971-89). The Collected Writings of John Maynard Keynes, vol. I-XXX, D E Moggridge & E A G Robinson (eds), London: Macmillan.

Keynes, John Maynard. (1973 [1921]). A Treatise on Probability. Volume VIII of The Collected Writings of John Maynard Keynes. London: Macmillan.

Mäki, Uskali. (2009). MISSing the World. Models as Isolations and Credible Surrogate Systems. Erkenntnis 70:29-43.

Pålsson Syll, Lars. (2001). Den dystra vetenskapen (”The dismal science”). Stockholm: Atlas.