by The ultimate methodological issue in economics If scientific progress in economics — as Robert Lucas and other latter-day followers of Milton Friedman seem to think — lies in our ability to tell ‘better and better stories’ one would of course expect economics journals to be filled with articles supporting the stories with empirical evidence. However, the journals still show a striking and embarrassing paucity of empirical studies that (try to) substantiate these stories and their predictive claims. Equally amazing is how little one has to say about the ultimate methodological issue — the relationship between the model and real-world target systems. It is as though explicit discussion, argumentation and justification on the subject aren’t considered
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Lars Pålsson Syll considers the following as important: Economics
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The ultimate methodological issue in economics
If scientific progress in economics — as Robert Lucas and other latter-day followers of Milton Friedman seem to think — lies in our ability to tell ‘better and better stories’ one would of course expect economics journals to be filled with articles supporting the stories with empirical evidence. However, the journals still show a striking and embarrassing paucity of empirical studies that (try to) substantiate these stories and their predictive claims. Equally amazing is how little one has to say about the ultimate methodological issue — the relationship between the model and real-world target systems. It is as though explicit discussion, argumentation and justification on the subject aren’t considered required.
If the ultimate criteria of success of a model is to what extent it predicts and coheres with (parts of) reality, modern mainstream economics seems to be a hopeless misallocation of scientific resources. To focus scientific endeavours on proving things in models is a gross misapprehension of what an economic theory ought to be about. Deductivist models and methods disconnected from reality are not relevant to predicting, explaining or understanding real-world economies.
If macroeconomic models – no matter what ilk – build on microfoundational assumptions of 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 hypotheses of causally relevant mechanisms or regularities can be bridged, are obviously non-justifiable. Incompatibility between actual behaviour and the behaviour in macroeconomic models building on representative actors and rational expectations microfoundations is not a symptom of “irrationality”. It rather shows the futility of trying to represent real-world target systems with models flagrantly at odds with reality. A gadget is just a gadget – and no matter how brilliantly silly models you come up with, they do not help us work with the fundamental issues of modern economies.
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 from 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 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 to 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 hypotheses 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.
Science and policy proposals should never be based on making heroically unreal tractability assumptions in the pursuit of ‘rigour’. Models and research designs building on such assumptions should make us naturally suspicious about their relevance and definitely weaken our degree of confidence in the solutions and proposals they produce.