The best advice you will get this year1 January, 2017 at 17:16 | Posted in Theory of Science & Methodology | Leave a comment
Getting it right about the causal structure of a real system in front of us is often a matter of great importance. It is not appropriate to offer the authority of formalism over serious consideration of what are the best assumptions to make about the structure at hand …
Where we don’t know, we don’t know. When we have to proceed with little information we should make the best evaluation we can for the case at hand — and hedge our bets heavily; we should not proceed with false confidence having plumped either for or against some specific hypothesis … for how the given system works when we really have no idea.
Trying to get around this lack of knowledge, mainstream economists in their quest for deductive certainty in their models, standardly assume things like ‘independence,’ ‘linearity,’ ‘additivity,’ ‘stability,’ ‘manipulability,’ ‘variation free variables,’ ‘faithfulness,’ ‘invariance,’ ‘implementation neutrality,’ ‘superexogeneity,’ etc., etc.
This can’t be the right way to tackle real-world problems. If those conditions do not hold, almost everything in those models is lost. The price paid for deductively is an exceedingly narrow scope. By this I do not mean to say that we have to discard all (causal) theories/laws building on ‘stability,’ ‘invariance,’ etc. But we have to acknowledge the fact that outside the systems that possibly fullfil these assumptions, they are of little substantial value. Running paper and pen experiments on artificial ‘analogue’ model economies is a sure way of ‘establishing’ (causal) economic laws or solving intricate problems — in the model-world. But they are pure substitutes for the real thing and they don’t have much bearing on what goes on in real-world open social systems. Deductive systems are powerful. But one single false premise and all power is gone. Setting up convenient circumstances for conducting thought-experiments may tell us a lot about what happens under those kinds of circumstances. But — few, if any, real-world social systems are ‘convenient.’ So most of those systems, theories and models, are irrelevant for letting us know what we really want to know.
Limiting model assumptions in economic science always have to be closely examined. The results we get in models are only as sure as the assumptions on which they build — and if the economist doesn’t give any guidance on how to apply his models to real-world systems he doesn’t deserve our attention. Of course one can always say — as James Heckman — that it is relatively straightforward to define causality “when the causes can be independently varied.” But what good does that do when we know for a fact that real-world causes almost never can be independently varied?
Building models can’t be a goal in itself. Good models are means that makes it possible for us to infer things about the real-world systems they ‘represent.’ If we can’t show that the mechanisms or causes that we isolate and handle in our models are ‘exportable’ to the real-world, they are of limited value to our understanding, explanations or predictions of real economic systems.
The kind of fundamental assumption about the character of material laws, on which scientists appear commonly to act, seems to me to be much less simple than the bare principle of uniformity. They appear to assume something much more like what mathematicians call the principle of the superposition of small effects, or, as I prefer to call it, in this connection, the atomic character of natural law. The system of the material universe must consist, if this kind of assumption is warranted, of bodies which we may term (without any implication as to their size being conveyed thereby) legal atoms, such that each of them exercises its own separate, independent, and invariable effect, a change of the total state being compounded of a number of separate changes each of which is solely due to a separate portion of the preceding state. We do not have an invariable relation between particular bodies, but nevertheless each has on the others its own separate and invariable effect, which does not change with changing circumstances, although, of course, the total effect may be changed to almost any extent if all the other accompanying causes are different. Each atom can, according to this theory, be treated as a separate cause and does not enter into different organic combinations in each of which it is regulated by different laws …
The scientist wishes, in fact, to assume that the occurrence of a phenomenon which has appeared as part of a more complex phenomenon, may be some reason for expecting it to be associated on another occasion with part of the same complex. Yet if different wholes were subject to laws qua wholes and not simply on account of and in proportion to the differences of their parts, knowledge of a part could not lead, it would seem, even to presumptive or probable knowledge as to its association with other parts. Given, on the other hand, a number of legally atomic units and the laws connecting them, it would be possible to deduce their effects pro tanto without an exhaustive knowledge of all the coexisting circumstances.
Real-world social systems are usually not governed by stable causal mechanisms or capacities. The kinds of ‘laws’ and relations that e. g. econometrics has established, are laws and relations about entities in models that presuppose causal mechanisms being invariant and atomistic. But — when causal mechanisms operate in the real world they only do it in ever-changing and unstable combinations where the whole is more than a mechanical sum of parts. If economic regularities obtain they do it as a rule only because we engineered them for that purpose. Outside man-made ‘nomological machines’ they are rare, or even non-existant.
Since there is no absolutely certain knowledge at hand in social sciences — including economics — explicit argumentation and justification ought to play an extremely important role if the purported knowledge claims are to be sustainably warranted. Without careful supporting arguments, building ‘convenient’ analogue models of real-world phenomena accomplishes absolutely nothing.
So we better follow Cartwright’s advice:
Where we don’t know, we don’t know. When we have to proceed with little information we should make the best evaluation we can for the case at hand — and hedge our bets heavily.