Against Simplicity

The appeal of simplicity is understandable. The world is complex, information is overwhelming, and clear answers are restful. The desire for simple explanations of complex phenomena — a single cause for a complex event, a single policy for a multifaceted problem — is not a failure of intelligence. It is a response to the cognitive demands of living in a complicated world.

But simplicity, as an intellectual value, comes at a cost that is worth attending to. The simplified model is useful precisely because it excludes. It focuses attention on what it identifies as relevant and renders invisible what it treats as background. This exclusion is sometimes an acceptable price for clarity. It becomes a problem when the excluded factors are the ones that matter most, or when the model is treated as a description rather than a tool.

The historian of science Theodore Porter has written about how quantification functions in public life — how the reduction of complex social phenomena to single metrics (crime rates, educational attainment, GDP per capita) enables comparison and accountability but also systematically distorts what is being measured by excluding what cannot be measured. The metric becomes the thing, and the thing is lost.

This is not an argument for complexity for its own sake. It is an argument for being explicit about what simplifications are being made and why, and for maintaining awareness of what they exclude. The best analysts of complex problems are not the ones who find the simplest explanation, but the ones who know which simplifications are productive and which are misleading, and can tell the difference between them.

Knowing when to simplify and when not to is not a simple matter, which is, perhaps, the point.