by Isn’t it the mark of a successful theory of a range of phenomena that it unites and embraces the causally relevant parameters and state variables within a single theoretical perspective? This question suggests that if our theories are successful, then they should produce descriptions of systems according to which the systems are interactionally simple. I think that this would be to put the conceptual cart before the phenomenal horse. As the criterion (one of many) for the adequacy of a theory of a system, this statement seems correct but it is hardly sufficient. Also, one should not automatically assume that our existing theories are adequate theories of complex systems. The belief that they are is based largely on a still unfilled reductionist promise. William Wimsatt
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Lars Pålsson Syll considers the following as important: Theory of Science & Methodology
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Isn’t it the mark of a successful theory of a range of phenomena that it unites and embraces the causally relevant parameters and state variables within a single theoretical perspective? This question suggests that if our theories are successful, then they should produce descriptions of systems according to which the systems are interactionally simple. I think that this would be to put the conceptual cart before the phenomenal horse. As the criterion (one of many) for the adequacy of a theory of a system, this statement seems correct but it is hardly sufficient. Also, one should not automatically assume that our existing theories are adequate theories of complex systems. The belief that they are is based largely on a still unfilled reductionist promise.
Experiments are hard to carry out in economics, and the theoretical ‘analogue’ models economists construct and in which they perform their ‘thought experiments’ build on assumptions that are far away from the kind of idealized conditions under which natural scientists perform their experiments. The ‘nomological machines’ that natural scientists have been able to construct have no real analogues in economics. The stability, autonomy, modularity, and interventional invariance, that we may find between entities in nature, simply are not there in real-world economies. That’s a fact, and contrary to the beliefs of most mainstream economists, it won’t go away simply by applying deductive-axiomatic economic theory with tons of more or less unsubstantiated assumptions.
By this, I do not mean to say that we have to discard all (causal) theories building on modularity, stability, invariance, etc. But we have to acknowledge the fact that outside the systems that possibly fulfil these assumptions, they are of little substantial value.
Take the modularity assumption for example. Modularity refers to the possibility of independent manipulability of causal relationships in a system. Trying to identify causal relations most economists today — especially when performing experiments — assume some kind of invariance or modularity, meaning basically that you can make an intervention on a part of a model without changing other dependencies in that model.
Modularity makes causal inferences made on the basis of ‘interventions’ stable. But although making causal inferences is not possible without making some kind of assumptions, you always have to argue why it is reasonable to make those assumptions. In the case of modularity that means you have to show that for the target system you are analyzing — the economy — it is possible to make ‘surgical interventions,’ ‘wiggle,’ or manipulate parts of the system without changing other parts of the system. Since economies basically are interactionally complex open systems, it is de facto hard to find causes that are separately manipulable and show such invariance under intervention. Most social mechanisms and relations are not modular. Extraordinary claims require extraordinary evidence. So if economists want to continue to use models that presuppose modularity they have to start arguing for the reasonableness of it. As scientists, we should not merely accept what is standardly assumed. When is modularity a reasonable assumption and when is it not? That modularity makes it possible to identify causality in ‘epistemically convenient systems’ is no argument for assuming it to apply to real-world economies.
Running paper and pen experiments on artificial ‘analogue’ model economies is a sure way of ‘establishing’ (causal) economic laws or solving intricate econometric problems of autonomy, identification, invariance and structural stability — 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. Setting up convenient circumstances for conducting 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.
Coming up with models that show how things may possibly be explained is not what we are looking for. It is not enough. We want to have models that build on assumptions that are not in conflict with known facts and that show how things actually are to be explained. Our aspirations have to be more far-reaching than just constructing coherent and ‘credible’ models about ‘possible worlds’. We want to understand and explain ‘difference-making’ in the real world and not just in some made-up fantasy world. No matter how many mechanisms or coherent relations you represent in your model, you still have to show that these mechanisms and relations are at work and exist in society if we are to do real science. Science has to be something more than just more or less realistic storytelling or ‘explanatory fictionalism.’ You have to provide decisive empirical evidence that what you can infer in your model also helps us to uncover what actually goes on in the real world. It is not enough to present epistemically informative insights about logically possible models. You also, and more importantly, have to have a world-linking argumentation and show how those models explain or teach us something about real-world economies. If you fail to support your models in that way, why should we care about them? And if you do not inform us about what are the real-world intended target systems of your modelling, how are we going to be able to value or test them? Without giving that kind of information it is impossible for us to check if the ‘possible world’ models you come up with also hold for the one world in which we live — the real world.
A ‘tractable’ model is of course great since it usually means you can solve it. But — using ‘simplifying’ tractability assumptions like modularity, because otherwise they cannot ‘manipulate’ their models or come up with ‘rigorous ‘ and ‘precise’ predictions and explanations, does not exempt scientists from having to justify their modelling choices. Being able to ‘manipulate’ things in models cannot per se be enough to warrant a methodological choice. Suppose economists do not really think their tractability assumptions — such as modularity — make for good and realist models. In that case, it is certainly a just question to ask for clarification of the ultimate goal of the whole modelling endeavour.