You mention economics. Macro-economic theories are based on high-level relationships that economists have observed in reality, and that have some theoretical b

You mention economics. Macro-economic theories are based on high-level relationships that economists have observed in reality, and that have some theoretical basis. For example, supply and demand can be represented as curves (aggregates of individual positions), and that in a perfect market the value of a good will converge at the intercept of supply and demand. There are a considerable number of assumptions required to make a statement like this, most of which are made to 'manage away' complexity. Complexity theory helps us find ways to address and model complexity, such that we can reduce the number of assumptions required. For example, if we can build an agent-based simulation of supply and demand, and we model these agents as realistically as possible (few assumptions), we can then see if the same realisation of a good's value occurs. The value in this is that we can then test the assumptions directly (what happens if 20% of people make irrational decisions?) and see how it affects the emergent behaviour. Complexity theory can help in properly building these kinds of fine-grained simulations in a smart way, and can also help in understanding and debugging such simulations when the wrong behaviour emerges. In my field (power systems) complexity theory can help us investigate and understand why large-scale blackouts have a fat tail distribution. That is, to understand why our macro-level statistical models aren't accurate in the edge cases. Primarily it is because these outliers occur 'emergently' due to the complexity of power systems, in ways that we overlook by applying old-school approaches to grid development and system operation. In short: complexity theory helps us look beyond our 'mostly right' high-level statistical models and theories.