Last summer, Chris Anderson, the editor of Wired, wrote an excellent article entitled "The End of Theory: The Data Deluge Makes Scientific Method Obsolete” in which he convincingly argued that massive amounts of data, in combination with sophisticated algorithms and super powerful computers, offer 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 am open to the idea that he 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.
The implication for a field such as biology which, as Anderson points out is actually becoming more difficult to model as we learn more about it (due to our limited understanding of how genetics, microbes, personal behavior, the environment, and a host of other factors work in partnership to determine a person’s health), could be profound. More specifically, we will be able to analyze data without allowing hypotheses (which are, perhaps, wrong) to cloud our view of what the data is really showing us.