ACM Journal on Computing and Cultural Heritage

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information processes—natural or constructed processes that transform information. They can be discrete or continuous.

Computing represents information processes as “expressions that do work.” An expression is a description of the steps of a process in the form of an (often large) accumulation of instructions. Expressions can be artifacts, such as programs designed and created by people, or descriptions of natural occurrences, such as DNA and DNA transcription in biology. Expressions are not only representational, they are generative: they create actions when interpreted (executed) by appropriate machines.

Since expressions are not directly constrained by natural laws, we have evolved various methods that enable us to have confidence that the behaviors generated do useful work and do not create unwanted side effects. Some of these methods rely on formal mathematics to prove that the actions generated by an expression meet specifications. Many more rely on experiments to validate hypotheses about the behavior of actions and discover the limits of their reliable operation.

Table 2 summarizes the computing paradigm with this focus. While it contains echoes of engineering, science, and mathematics, it is distinctively different because of its central focus on information processes. 5 It allows engineering and science to be present together without having to choose.

There is an interesting distinction between computational expressions and the normal language of engineering, science, and mathematics. Engineers, scientists, and mathematicians endeavor to position themselves as outside observers of the objects or systems they build or study. Outside observers are purely representational. Thus, traditional blueprints, scientific models, and mathematical models are not executable. (However, when combined with computational systems, they give automatic fabricators, simulators of models, and mathematical software libraries.) Computational expressions are not constrained to be outside the systems they represent. The possibility of self-reference makes for very powerful computational schemes based on recursive designs and executions, and also for very powerful limitations on comput-

ing, such as the noncomputability of halting problems. Self-reference is common in natural information processes; the cell, for example, contains its own blueprint.

The interpretation “computational thinking” 12 embeds nicely into this paradigm. The paradigm describes not only a way of thinking, but a system of practice.

Conclusion

The distinctions discussed here offer a distinctive and coherent higher-level description of what we do, permitting us to better understand and improve our work and better interact with people in other fields. The engineering-science debates present a confusing picture that adversely affects policies on innovation, science, and technology, the flow of funds into various fields for education and research, the public perception of computing, and the choices young people make about careers.

We are well aware that the computing paradigm statement needs to be discussed widely. We offer this as an opening statement in a very important and much needed discussion.

 

References

1. Arden, B. W. What Can Be Automated: Computer Science and Engineering Research Study (COSERS). Mi T Press, 1983.

2. Denning, P. Computing is a natural science. Commun. ACM 50, 7 (July 2007), 15–18.

3. Denning, P. Who are we? Commun. ACM 44, 2 (Feb. 2001), 15–19.

4. Denning, P. et al. Computing as a discipline. Commun. ACM 32, 1 (Jan. 1989), 9–23.

5. Denning, P. and P.S. Rosenbloom. Computing: The fourth great domain of science. Commun. ACM 52, 9 (Sept. 2009), 27–29.

6. Freeman, P. Public talk “i T Trends: impact, expansion, Opportunity,” 4th frame; www.cc.gatech. edu/staff/f/freeman/Thessaloniki

7. Freeman, P. and Aspray, W. The Supply of Information Technology Workers in the United States. Computing Research Association, 1999.

8. Newell, A., Perlis, A.J., and Simon, H.A. Computer science, letter in Science 157, 3795 (Sept. 1967), 1373–1374.

9. Perlis, A.J. The computer in the university. in Computers and the World of the Future, M. Greenberger, ed. Mi T Press, 1962, 180–219.

10. Rosenbloom, P.S. A new framework for computer science and engineering. IEEE Computer (Nov. 2004), 31–36.

11. Simon, H. The Sciences of the Artificial. Mi T Press (1st ed. 1969, 3rd ed. 1996).

12. Wing, J. Computational thinking. Commun. ACM 49, 3 (Mar. 2006), 33–35.

 

Peter J. Denning ( [e-mail]) is the director of the Cebrowski institute for information innovation and Superiority at the Naval Postgraduate School in Monterey, CA, and is a past president of ACM.

Peter A. Freeman ( [e-mail]) is emeritus Founding Dean and Professor at Georgia Tech and Former Assistant Director of NSF for CiSe.