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computational science and the discovery of natural information processes. I will review each of these periods.

The pioneers who planned and built the first electronic computers were strongly motivated by visions of computers advancing science. The two most obvious ways were the numerical solution of mathematical models of physical processes, and the analysis of large datasets compiled from experiments. Computer science became a recognized academic field of study in 1962 with the founding of computer science departments at Purdue and Stanford. These departments maintained strong faculties in mathematical software, which directly supported science.

In 1967, Newell, Perlis, and Simon
argued that the new field was a science
concerned with all aspects of “phe-
nomena surrounding computers.” 12
However, many traditional scientists
disagreed with the science claim; they
held that true science deals with phe-
nomena that occur in nature (“natural
processes”) whereas computers are
man-made artifacts. Simon, a Nobel
Laureate in economics, so strongly
disagreed with the “natural interpreta-
tion” that he published a book The Sci-
ences of the Artificial (MIT Press, 1969).
He argued that economics and com-
puter science met all the traditional
criteria for science, and deserved to
be called sciences even if, said Simon,
their focal phenomena are “man-made
as opposed to natural.”
In the initial years of the field, most
computing people devoted their energy
to building the systems that could real-
ize the visionary dreams of the found-
ers. By the late 1970s, the computing in-
dustry was recruiting system people so
vigorously that university departments
were experiencing a “brain drain” of
systems-oriented faculty. ACM leader-
ship was very concerned: this trend
threatened experimental computer
science. I was deeply involved as ACM
president in arguing the importance
of experimental methods for comput-
ing and in assisting the U.S. National
Science Foundation (NSF) to support
experimental computer scientists. I
wrote in 1980 that the experimental
method (that is, science) is essential in
computer science, 6 and in 1981 I cited
the subfield of performance model-
ing and prediction as an exemplar of