Moshe Y. Vardi
Lost in Math?
WHEN I WAS 10 years old, my math
teacher started a Math Club. It was
not popular enough to last more than
a few weeks, but that was long enough
for me to learn about matrices and determinants. When I came home, my
mother asked me how the club had
been. “Beautiful,” I answered. “Do you
mean, ‘interesting’?” she inquired.
“No,” I said, “Beautiful!” While some
people find mathematics befuddling,
others find it elegant and beautiful.
The mathematician Paul Erdős often
referred to “The Book” in which God
keeps the most beautiful proofs of
each mathematical theorem. The philosopher Bertrand Russell said, “
Mathematics, rightly viewed, possesses not
only truth, but supreme beauty.” The
beauty can be compelling; something
so beautiful must be true!
But the seductive power of mathematical beauty has come under criticism lately. In Lost in Math, a book published earlier this year, the theoretical
physicist Sabine Hossenfelder asserts
that mathematical elegance led physics astray. Specifically, she argues that
several branches of physics, including string theory and quantum gravity, have come to view mathematical
beauty as a truth criterion, in the absence of experimental data to confirm
or refute these theories. The theoretical physics community, she argues, is
falling victim to group thinking and
cognitive bias, seduced by mathematical beauty. About 10 years ago, in the
wake of the 2008 financial crisis, the
Nobel Laureate economist Paul Krugman made the same point with respect to economics and mathematics
in an influential article titled “How
Did Economists Get It So Wrong?” His
main answer was: mistaking mathematical beauty for truth. “As I see
it,” wrote Krugman, “the economics
profession went astray because econo-
mists, as a group, mistook beauty, clad
in impressive-looking mathematics,
So both physics and economics
have, arguably, been lost in math.
What about computer science? Specifically, what about theoretical computer
science (TCS)? TCS is surely blessed
with mathematical beauty. As a graduate student a long time ago, it was
mathematical beauty that attracted
me to TCS, and continued to lead my
research for many years. I find computational complexity theory (or complexity theory, for short), with its theorems (for example, the time-hierarchy
and space-hierarchy theorems) and its
open questions (for example, P vs NP),
to be hauntingly beautiful. Beauty, yes;
but what about truth?
Physical theories describe the physical world, and by their “truth” we refer
to the fidelity in which they describe
this world. Economic theories describe
economic systems, but by their “truth”
we refer not only to the fidelity in which
they describe such systems but also to
the quality of the guidance they offer
to business people and policymakers.
I believe complexity theory is similar
to economic theories in that respect.
It should not only provide a theoretical framework in which we can study
the performance of algorithms, but it
should also offer sound guidance to algorithm designers and system developers who use algorithms. So how good a
theory is complexity theory from that
It is clear what it means to measure
the performance of a specific algorithm
A on a specific problem instance I. But
complexity theory aims at describing
the performance of A over the space of
all problem instances and it does so by
abstracting away from individual problem instances. The typical way in which
we do this abstraction is by considering
all problem instances of length n, and
asking for upper and lower bounds on
algorithmic performance (usually in
terms of time and memory utilization)
as a function of n. This approach is referred to as the worst-case approach,
as it focused on the most challenging
problem instance of each length n. If
the upper bound that we can prove is
one of a slow-growing function, for example, cn log n, for a small constant c,
then we have a guarantee of good performance on all problem instances.
But, in general, most upper and lower
bound are much less useful. For example, an exponential lower bound just
says some problem instances are hard,
but says nothing about the practical
significance of such instances.
In previous columns I have discussed this gap between theory and
practice in specific settings. As I pointed out, program termination may be
unsolvable in theory but solvable in
practice,a while Boolean satisfiability
may be intractable in theory but tractable in practice.b In both cases, the
worst-case approach is simply too pessimistic and tells us too little about
algorithmic performance in practice.
Beauty does not necessarily entail
truth. Going beyond worst-case complexity is a key challenge in complexity
theory and is the subject of much current research. (See Tim Roughgarden’s
Review Article on p. 88).
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Moshe Y. Vardi (email@example.com) is the Karen Ostrum
George Distinguished Service Professor in Computational
Engineering and Director of the Ken Kennedy Institute for
Information Technology at Rice University, Houston, TX, USA.
He is the former Editor-in-Chief of Communications.
Copyright held by author.