were a way to do science that was not
previously available. Wilson’s Nobel
Prize was based on breakthroughs he
achieved in creating computational
models whose simulations produced
radical new understandings of phase
changes in materials. In the early 1980s,
Wilson joined with other leading scientists in many fields to advocate that the
grand challenges of science could be
cracked by computation and that the
government could accelerate the process by supporting a network of supercomputing centers.
7 They argued that
computation had become a third leg
of science, joining the traditional legs
of theory and experiment. The term
“computational thinking” was common in their discussions.
The computational sciences movement eventually grew into a huge
interagency initiative in high-performance computing, and culminated in
the U.S. Congress passing a law funding a high-performance computing
initiative in 1991.
This movement validated the notion
that computation (and computational
thinking) is essential to the advancement of science. It generated a powerful political movement that codified
this notion into a U.S. federal law.
It is important to notice that this
movement originated with the leaders
of the physical and life sciences. Com-
Computation is
unavoidable not
only in the method
of study, but in
what is studied.
puter science was present but was not a
key player. Computer scientists, in fact,
resisted participation until NSF CISE
and DARPA set up research programs
open only to those collaborating with
other sciences.
In the middle 1980s, Ken Wilson advocated the formation of departments of
computational science in universities.
He carefully distinguished them from
computer science. The term “
computational science” was chosen to avoid confusion with computer science.
Thus, computational science is seen
in the other sciences not as a notion
that flows out of computer science, but
as a notion that flows from science itself. Computational thinking is seen as
a characteristic of this way of science.
It is not seen as a distinctive feature of
computer science.
Therefore, it is unwise to pin our
hopes on computational thinking as a
way of telling people about the unique
character of computer science. We
need some other way to do that.
The sentiment that computational
thinking is a recent insight into the true
nature of computer science ignores the
venerable history of computational
thinking in computer science and in
all the sciences. Computer science is
a science in its own right (see the sidebar “Computer Science as Science”).
is Computational thinking
adequate for Computer science?
In 1936 Alan Turing defined what it
means to compute a number. He offered a model of a computing machine
and showed that the machines were
universal (one could simulate another). He then used his theory to settle
a century-old “decision problem” of
mathematics, whether there is a by-inspection method to tell if a set of decision rules can terminate with a decision in a finite number of moves. He
showed that the “decision problem” was
not computable and argued that the very
act of inspecting is inherently computational: not even inspectors can avoid
computation. Computation is universal
and unavoidable. His paper truly was the
birth of computer science.
The modern formulations of science
Computer Science as Science
since its beginnings in the late
1930s, computer science has
been a unique combination of
math, engineering, and science.
It is not one, but all three. major
subsets form legitimate fields of
math, engineering, or science.
But if you focus on a single
subset, you cannot express the
uniqueness of the field.
the term “computer
science” traces back to the
writings of John von neumann,
who believed that the
architecture of machines and
applications could be put on a
rigorous scientific basis.
until about 1990, the
emphasis within the field was
developing and advancing
the technology. Building
reliable computers within a
networking infrastructure was
a grand challenge that took
many years. now that this has
been accomplished, we are
increasingly able to emphasize
the experimental method and
reinvigorate our image as a
science. our many partnerships
with other sciences including
biology, physics, astronomy,
materials science, economics,
cognitive science, and
sociology, have led to amazing
innovations.
these collaborations
have uncovered questions
in the other fields about
whether computer science is
legitimately science. many see
computer people as engineers
implementing principles they
did not discover rather than
equal partners in the search for
new principles. so it matters
whether computer science
qualifies as a full-fledged
science. Whether a field is seen
as a science depends on its
satisfying six criteria:
5
• has an organized body of
knowledge
• results are reproducible
• has well developed experimental methods
• Enables predictions, including surprises
• offers hypotheses open to
falsification
• Deals with natural objects
Computer science easily
passes the first five of these
tests, so the debate has tended
to center on the last. During
the past decade, prominent
scientists in other fields have
discovered natural information
processes—affirming the
sixth criterion.
3 the older
definition of computer science
as “the study of phenomena
surrounding computers,”
which dates back to alan
Perlis, George Forsythe, and
allen newell around 1970,
is giving way to “the study
of information processes,
natural and artificial.” the
shift from computer as object
of study to computer as tool is
enabling us to revisit the deep
questions of our field in the
new light of computation as a
lens through which to see the
world. the most fundamental
of these questions is: What is
computation?
6, 9