Modularity — a questionable assumption

by
Lars Pålsson Syll
My articles My siteAbout meMy booksMy videos
Follow on:Twitter

Modularity — a questionable assumption

Modularity is the mark of a type of independence from context. The same functional relationship between variables will hold in a given component of the contributing mechanisms whether or not there is a change in a different component. The total effect may change when different components contribute, but the operation of the modular mechanism will not be changed nor change them. In situations where the presence or absence of other contributing factors changes the behavior of a component, and not just the total effect, modularity will fail …

Biological systems are complex and contingent. These features present challenges, both methodologically and conceptually, to our ability to export causal knowledge from one case to another. Recognizing that the causal functions discoverable in biology are for the most part not universal has led some in the past to doubt that there could be laws in biology and cast a correlated shadow on biology’s explanatory potential. Woodward has provided an alternative account of causal explanation that replaces the demand for universality with invariance, modularity, and insensitivity …

Some forms of complex organization and dynamics present problems even for Woodward’s more forgiving approach … Some complex structures harbor nonmodular, context-sensitive actual causes that can explain its behavior. On this view, exporting knowledge from one system to another will requires more than generalization from observed structure and simple instantiation in a new context.

Sandra D. Mitchell

Mitchell argues that we should not discard all (causal) theories building on modularity, stability, invariance, etc. But we also have to acknowledge that outside the systems that possibly fulfil these assumptions, they are of questionable substantial value.

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 based on ‘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.

Coming up with models that show how things may possibly be explained is not what we are looking for. It is not enough. As Mitchell notes in her article, many explanatory projects in biology “are directed not at what might be possible, but rather at what has actually evolved … Describing all the physically and chemically possible interactions does not zero in on the actual target of biological explanation.”

The same reasoning applies to economics. 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. 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 we can’t check if the ‘possible world’ models you come up with also hold for the one world we care about and in which we live — the real world.

The interventionist approach to causal inference has become increasingly popular in social sciences during the last two decades. But most social systems are complex, evolving, contingent, dynamic, emergent, and genuinely uncertain. As argued by Mitchell, the theories and methods that build on the interventionist approach are not viable for those systems. As an unsubstantiated general assumption guiding causal analysis in social sciences, modularity should be abandoned. Other — more pluralist — methods and theories of causal inference and explanation are needed.