You can tell a lot about a field by how researchers motivate their
results in papers and talks. Pure mathematics often gives little or no
motivation starting a paper or talk with something like "Let
λ be an inaccessible cardinal…" In economics, even
in theoretical papers, considerable time is spent in coming up with
stories to justify a given model. More discussion is spent in
economics talks about the model than the particular proofs and results
that derive from that model.
In theoretical computer science and in particular computational
complexity we straddle between these two worlds. Our goal is to
understand the power of efficient computation so we have complexity
classes like NC, P, P/poly, BPP and BQP that try to approximate
real-world models of computation. We have classes like NP, #P and
PSPACE that capture a large number of interesting problems that we
would like to solve. We have models like Probabilistically Checkable
Proof Systems (PCPs) whose parameters help us understand the
limitations of approximation. We have combinatorial tools like
expanders and extractors that have wide applications in many areas of
complexity and beyond.
But all these classes, models and tools have very nice theoretical
properties as well. We tend to focus more on the theoretical
foundations judging papers more for their theorems and the difficulty
of the proofs than the importance of the underlying problem. In the
end we reduce the amount of motivation in the paper often to a single
sentence of the introduction and a theory audience only rarely questions
the importance of a model during a talk.
Once we deemphasize the motivation of a model, then others, in an
attempt to find open problems, look at variations of the model. Often
these variations are motivated solely by the importance of the
original model, even if the variations have little relevance with the
original motivation of the model. Researcher then consider variations
on the variations deviating quite far from the original model and its
motivations.
Other computer scientists often complain, rightly or wrongly, that
theoretical computer science and computational complexity have lost
touch with real computational issues. We must be careful to not
focus too much on presentations that don't express or don't even
have some reasonable motivation beyond the difficulty of the proofs.