Scientific progress is often viewed as an inexorable march toward increasing knowledge. We'll never know everything about the universe, but we've gotten used to the idea that we keep knowing ever more, at an ever-increasing rate.
However, as we discussed some time ago, human beings are creatures of finite complexity. There is only a finite amount we can do, and, more relevant to the present discussion, there is only a finite amount we can know. It's very likely that the human brain holds less pure information than the average hard drive. So while we humans as a collective might be able to increase our knowledge indefinitely, our knowledge as individuals has a definite limit.
What does this limit mean for the study and practice of science? For one thing, it limits the knowledge that a single scientist can apply to a particular problem. A researcher studying a virus can't apply all of science, or all of molecular biology, or all of virology to his study. Even just the scientific knowledge about this particular virus might be too much to fit into this researcher's brain. As scientists, we attack our problems using whatever knowledge we've gained from coursework, reading, and conversations with others--a tiny fraction of the wealth of potentially relevant knowledge out there.
Worse, as the frontier of knowledge keeps expanding, the amount of background knowledge needed to comprehend a single patch of this frontier increases steadily. I started my math career in differential geometry/topology: a beautiful subject, but one that requires years of graduate coursework to understand current research questions even on a superficial level. Since we have finite brainpower, no individual can maintain this kind of expertise in more than a few subjects. So we become specialists, unable to discuss our research with anyone outside our narrowly defined field. Before I switched to complex systems, I was continually frustrated by the isolation that came with specialized research. And I hear this same frustration from many of the other math/science grad students I talk to.
The danger is that science will keep branching into smaller, more arcane, and more isolated subsubdisciplines. This would make interdisciplinary research increasingly difficult, and the prospect of a science career ever more daunting and unappealing for students. And it would not get us any closer to solving some of our biggest problems in science, which lie not at the fringes of some highly specialized discipline, but in the synthesis of results from all branches of science.
What is needed is a sustained push for big-picture thinking. Whereas small-picture science focuses on the complex and the narrowly defined, big-picture sceince seeks the broad and the simple. It combines the many complex discoveries made by small-picture scientists, and distills them into ideas that can fit in a single human's head.
Here's a useful example, stolen from the website eigenfactor.org and based on this paper:
The above is a diagram of a yeast protein interaction network. It represents the cumulative work of many scientists who investigated whether and how certain proteins interact with each other. A remarkable achievement, certainly.
But the sheer volume of information makes this diagram useless to anyone but a specialist, and probably not very helpful for the specialists either. Trying to draw conclusions from a diagram like this would be like trying to navigate cross country using a map that shows every side street and alley in the US. It's just too much information for one brain to handle.
The authors go on to describe an algorithm that can transform complex networks like this:
into simplified ones like this:
that represent simple, understandable relationships.
I don't mean to belittle the work done by small-picture scientists; without them the big picture thinkers would have nothing to talk about. But I think the scientific establishment is so structured around the small-picturists that big picture thinking often gets squeezed out, which only impedes our understanding of science in general.
Idiosyncratic Thinking: a computer heuristics lecture
8 hours ago in Doc Madhattan