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Is science a good thing, or is it a bad thing? This question was debated among physicists at the end of World War II, for the obvious reasons, but it's being debated now among those in the biological sciences as well as by those with no special knowledge of science, yet who have opinions about how scientific research should be conducted. In the discussion that occurred more than 60 years ago, some physicists felt that scientists should have kept from the world the knowledge of nuclear weapons (assuming such a thing was possible), while others felt that it is always the responsibility of the scientist to probe the secrets of the universe, no matter where that probing might lead. Some claimed that even asking such questions is not the business of the scientist when doing science. Naturally, I'm oversimplifying and ignoring the many shades of opinion among "it's good," "it's bad," and "it's neither." My purpose is not to add my two cents to the debates, but rather to point out what I think is one significant way in which computational science enters the fray.
There are those among today's scientists who are not inclined to engage in such debates and, in fact, are more likely to fall into the "not the business of scientists" category. I don't mean that these scientists aren't informed about or concerned over the state of the world—far from it. But the connections between professional activities and larger issues are difficult to make if one spends most waking hours mining the deepest veins of a sub-sub-specialty of a very narrow and arcane topic. Computational scientists, however, don't have this luxury, because so many scientific issues of major import can be addressed via computations, and computation is never narrow. Some current examples: Is global warming real? Is it the result of human activity? What about AIDS? Is it an epidemic in the strictest sense of that term? Can electronic voting be trusted? In these and many other cases, part of the response is to see what the computational models say.
A good computational science project can involve many disciplines—numerical analysis, computer architecture, logic, programming languages, visualization, error-correcting codes, probability theory, and, of course, the basic scientific questions under investigation. Working on such a project feels a little like the quest in The Rule of Four. As Publisher's Weekly said about that book, "This debut novel's range of topics almost rivals the Hypnerotomachia's itself, including etymology, Renaissance art and architecture, Princeton eating clubs, friendship, steganography (riddles) and self-interpreting manuscripts."
If it is to continue to thrive, computational science must be broad, and its practitioners must be broadly educated. This sometimes generates questions about where computational science belongs in the usual academic curriculum, but that's because the curriculum itself needs to be revised. Two of the most important things computational scientists can do are to learn new things and to teach new things. Interestingly, the columns and features of CiSE that are the most explicitly educational are also the most popular, as least when judged by the number of downloads. Education in computational science has been the main theme of CiSE from its beginning and is likely to remain so for quite some time.
This is my last EIC message for CiSE: the next issue will be in the capable hands of incoming EIC Norman Chonacky. I am sure that all readers, editors, authors, and staff will give him the same superb support that I've had. Working with the CiSE staff, editorial board, and authors, and especially writing the EIC messages have been among the joys of my working life for the past several years. Thanks everybody!