Rethinking Heuristica

I've argued that more and more we seem to live in an Optiland, a computational utopia where through recent developments in optimization and learning we can solve the NP-problems that come up in practice and yet cryptography remains unscathed. We seem to simultaneously live in Heuristica ( and Cryptomania of Russell Impagliazzo's Five Worlds.

But we don't. Impagliazzo defined Heuristica as the world where P \(\ne\) NP but we can solve NP-complete problems easily on average. Since cryptography requires problems that are hard on average, if we are in Cryptomania we can't be in Heuristica. 

That definition made sense in 1995 but it didn't envision a world where we can solve many NP-problems in practice but not easily on average. Despite its name, Heuristica as defined does not capture solving real-world problems. To be fair, Impagliazzo entitled his paper "A Personal View of Average-Case Complexity," not "A Treatise on Solving Real World Problems". 

So we need to rethink Heuristica or create a new world (Practica?) that better captures real-world problems. How would we do so? 

When I talked with the SAT Solving researchers at Simons last spring, they had suggested that problems designed to be hard are the ones that are hard. But how can we mathematically capture that? Maybe it's connected to learning theory and TC⁰ (low depth circuits with threshold gates). Maybe it's connected to constraint-satisfaction problems. Maybe it's connected to time-bounded Kolmogorov complexity. 

As complexity theorists this is something we should think about. As we study the mathematics of efficient computation, we should develop and continue to revise models that attempt to capture what kinds of problems we can solve in practice.

But for now I don't have the answers, I don't even know the right questions.