Will Scientific Modelling Soon Be Obsolete?

June 25 2008 / by juldrich / In association with Future Blogger.net
Category: Biotechnology   Year: General   Rating: 6 Hot

By Jack Uldrich

Cross-posted from www.jumpthecurve.net

Chris Anderson, the editor of Wired, has written an excellent article entitled “The End of Theory: The Data Deluge Makes Scientific Method Obsolete” in which he convincingly argues that massive amounts of data, in combination with sophisticated algorithms and super powerful computers, offers mankind a whole new way of understanding the world.

Anderson believes that our technological tools have now progressed to the point where the “old way” of doing science – hypothesize, model and test – is becoming obsolete. In its place, a new paradigm is now emerging whereby scientists, researchers and entrepreneurs simply allow statistical algorithms to find patterns where science cannot.

If Anderson is correct – and I believe he very well could be – this will take science in a whole new direction. In short, instead of modeling and waiting to find out if hypotheses are valid the scientific community can instead rely on intelligent algorithms to do the heavy lifting.

Before this vision can be achieved, however, it will require a great many brilliant scientists to unlearn the idea that their “model-based” method of trying to make sense of today’s increasingly complex world is the only way to search for new meaning. (cont.)

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The Centralization AND Decentralization of Science

January 14 2009 / by Alvis Brigis / In association with Future Blogger.net
Category: Science   Year: General   Rating: 2

In his first ever post on the NYTimes' The Wild Side blog, biologist Aaron Hirsch describes what he sees as the increasing centralization and decentralization of scienctific processes.  These new approaches, he argues, are driving larger and more complex efforts to generate more useful useful data in different ways.

Centralization: Across many different fields, new data are generated by a smaller and smaller number of bigger and bigger projects. And with this process of centralization come changes in what scientists measure — and even in what scientists are.Centralization of Information

Hirsch attributes this to the high cost of powerful machines and technologies that can quickly generate results that otherwise would take far longer to discover.  This new dependence on massive facilities or operations, he argues, is changing the nature of the scientist.

It’s not only scientific instruments, but also the scientists themselves who are transformed by centralization. If the 19th century was an age of far-flung investigators alone in the wilderness or the book-lined study, the 21st century is, so far, an age of scientists as administrators.

Decentralization: Simultaneously, we are are experiencing a huge decentralization of much of our scientific process through projects such as SETI that tap the distributed power of personal laptops.  Hirsch labels this "Citizen Science".

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