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}