I second that. I see it as something to build intuition for "complex" problems. To make this more concrete: If you want to study the brain you can go the "biolo

I second that. I see it as something to build intuition for "complex" problems. To make this more concrete: If you want to study the brain you can go the "biology" approach and describe neurons really well and build mathematical models for all the neuron types. Or you could do it the other way around with the "psychology" approach and put people/monkey/rats in an MRT. Both ways you learn important stuff but it will be hard to connect both worlds because simulation of enough neurons to predictive power over the outcome of an MRT is probably far fetched (although there are the human brain project or its US counterpart, the brain activity map project which attempt to do something like this). Complexity theory might help to learn how to close this gap. Things like synchronization (http://www.scholarpedia.org/article/Synchronization#Chaotic_systems http://www.scholarpedia.org/article/Synchronization#Chaotic_...) or Self organized criticality (form the critical brain theory) could help distinguishing which parts of neuronal dynamics are due to biological restrictions and which form the function of the brain. With this knowledge one might be able to "dumb down" neuronal models enough to make large scale simulations without loosing to much of the processing dynamics. You might still not have predictive power then, but then again, complexity theory might help you to understand what the limitations of your approach are. The same intuitions could be applied to other things. Large scale power grids are also often hard to predict when not moving into a sure fail state. Being able to analyze how you stabilize these systems without basically dumping a lot of money on them is the way to go (Looking in the past, the money will probably not be spend). You could study the behavior of crowds and maybe make estimations on the safety large conventions build a "panic index" that calculates the risk of having something like at the Loveparade https://en.wikipedia.org/wiki/Love_Parade_disaster https://en.wikipedia.org/wiki/Love_Parade_disaster . Again - you would not be able to make a precise prediction of whats gonna happen but I'd say it'd even knowing if you have like a 0.5% Chance of a disaster would be worth knowing. (Of course, there are effective methods taught to prevent this disasters anyway. But sometimes you have new configurations that didn't occur in the past and you might catch these things with a simulation. I could be an additional approach)