Moshe Y. Vardi
science has only two Legs
Science has been growing new legs of late.
The traditional “legs” (or “pillars”) of the scientific
method were theory and experimentation.
That was then. In 2005, for example, the U.S.
Presidential Information Technology
Advisory Committee issued a report,
“Computational Science: Ensuring
America’s Competitiveness,” stating:
“Together with theory and experimentation, computational science now
constitutes the ‘third pillar’ of scientific inquiry, enabling researchers to
build and test models of complex phenomena.” The report offered examples
such as multi-century climate shifts,
multidimensional flight stresses on
aircraft, and stellar explosions.
This “third leg” of science has become a standard coin (run a Web search
on this phrase!). However, this leg
has been recently augmented by yet a
“fourth paradigm” (or “leg”) that refers
to the usage of advanced computing
capabilities to manipulate and explore
massive datasets. For example, the decoding of the human genome in 2001
was a triumph of large-scale data analysis. Now science allegedly has four legs,
and two of them are computational!
I find myself uncomfortable with
science sprouting a new leg every few
years. In fact, I believe that science still
has only two legs—theory and experimentation. The “four legs” viewpoint
seems to imply the scientific method
has changed in a fundamental way. I
contend it is not the scientific method
that has changed, but rather how it is
being carried out. Does it matter how
many legs science has? I believe it does!
It is as important as ever to explain science to the lay public, and it becomes
more difficult to explain when it grows
a new leg every few years.
Let us consider the first leg: theory.
A scientific theory is an explanatory
framework for a body of natural phenomena. A theory can be thought of as
a model of reality at a certain level of
abstraction. For a theory to be useful,
it should explain existing observations
as well as generate predictions, that
is, suggest new observations. In the
physical sciences, theories are typically
mathematical in nature, for example,
the classical theory of electromagnetism in the form of Maxwell’s Equations. What is often ignored is the fact
that any application of a mathematical
theory requires computation. To make
use of Maxwell’s Equations, for example, we need to solve them in some
concrete setting, and that requires
computation—symbolic or numeric.
Thus, computation has always been an
integral part of theory in science.
What has changed is the scale of
computation. While once carried out
by hand, computation has required
over time more advanced machinery.
“Doing” theory today requires highly
techniques carried out on cutting-edge
The nature of the theories has also
changed. Maxwell’s Equations constitute an elegantly simple model of reality. There is no analogue, however,
of Maxwell’s Equations in climate science. The theory in climate science is a
highly complex computational model.
The only way to apply the theory is via
computation. While previous scientific
theories were typically framed as math-
ematical models, today’s theories are
often framed as computational mod-
els. In system biology, for example, one
often encounters computational mod-
els such as Petri Nets and Statecharts,
which were developed originally in the
context of computer science.
Moshe Y. Vardi, EDITOR-In-CHIEf