the ideals of science. 4 Despite these efforts, many university departments lost their experimentalists and the science vision faded into the background.

In the 1980s, science visionaries from many fields saw ways to employ high-performance computers to solve “grand challenge” problems in science. They said computing is not only a tool for science, but also a new method of thought and discovery in science. (Aha! Computational thinking!) They defined computational science as a new branch of science imbued with this idea. The leaders of biology, epitomized by 1975 Nobel Laureate David Baltimore, went further, saying biology had become an information science and that DNA translation is a natural information process. Another biologist, Roseanne Sension, attributed the efficiency of photosynthesis to a quantum algorithm embedded in the cellular structure of plant leaves (Nature, April 2007). Biologists have thus been leaders in driving nails into the coffin of the “natural science” argument about computing. Many other scientists have reached similar conclusions. They include physicists working with quantum computation and quantum cryptography, chemists working with materials, cognitive scientists working with brain processes, economists working with economic systems, and social scientists working with networks. 9 All claimed to work with natural information processes. Stephen Wolfram went further, arguing that information processes underlie every natural process in the universe. 13

Those two external factors—rise of computational science and discovery of natural information processes—have spawned a science renaissance in computing. Experimental methods have regained their stature because they are the only way to understand very complex systems and to discover the limits of heuristic problem solution methods.

Here is an example of an advance in algorithms obtained through an empirical approach. In May 2004, an international research group announced it had computed an optimal tour of 24,978 cities in Sweden (see http://tsp.gatech.edu/ sweden). By iterating back and forth among several heuristic methods, they homed in on a provably optimal solution. Their computation took about one year on a bank of 96 parallel Intel Xeon

2.8GHz processors. With classical tour-enumeration algorithms, which are of order O(n!), the running time would be well beyond the remaining age of the universe. With experimental methods, algorithm scientists quickly found optimal or near-optimal solutions.

New fields heavily based in experimental methods have opened up— network science, social network science, design science, data mining, and Bayesian inference, to name a few. The widening claims that information processes occur in nature have refuted the notion that computer science is not “natural” and have complemented Simon’s arguments that computing is a science of the artificial.

When is a field a Science?

This brief history suggests that computing began as science, morphed into engineering for 30 years while it developed technology, and then entered a science renaissance about 20 years ago. Although computing had subfields that demonstrated the ideals of science, computing as a whole has only recently begun to embrace those ideals. Some new subfields such as network science, network social science, design science, and Web science, are still struggling to establish their credibility as sciences.

What are the criteria for credibility as science? A few years ago I compiled a list that included all the traditional ideals of science: 1, 3

˲ Organized to understand, exploit, and cope with a pervasive phenomenon.

˲ Encompasses natural and artificial

although computing
had subfields that
demonstrated the
ideals of science,
computing as a whole
has only recently
begun to embrace
those ideals.

processes of the phenomenon.

˲Codified structured body of knowledge.

˲Commitment to experimental methods for discovery and validation.

˲ Reproducibility of results.

˲Falsifiability of hypotheses and models.

˲Ability to make reliable predictions, some of which are surprising.

Computing’s original focal phenomenon was information processes generated by hardware and software. As computing discovered more and more natural information processes, the focus broadened to include “ natural computation.” 9 We can now say “computing is the study of information processes, artificial and natural.” 1

Computing is not alone in dealing with both natural and artificial processes. Biologists, for example, study artifacts including computational models of DNA translation, the design of organic memories, and genetically modified organisms (GMOs). All fields of science constantly face questions about whether knowledge gained from their artifacts carries over to their natural processes. Computing people face similar questions—for example, does studying a software model of a brain yield useful insights into brain processes? A great deal of careful experimental work is needed to answer such questions.

The question of “scienceness” of computing has always been complicated because of the strong presence of science, mathematics, and engineering in the roots and practice of the field. 8, 11 The science perspective focuses on increasing understanding through experimental methods. The engineering perspective focuses on designing and constructing ever-improved computing systems. The mathematics perspective focuses on what can be deduced from accepted statements.

The term “theory” illustrates the different interpretations that arise in computing because of these three perspectives. In pure math, theory means the set of valid deductions from a set of axioms. In computing, theory more often means the use of formalism to advance understanding or design.

 

effects on the education System Unfortunately, our education system for young people has not caught