Here's something that ought to put my take on the field in context. It's from an interview with Stanford Economist Brian Arthur and his early direction of the economics research program at the complex systems-oriented Santa Fe Instutite:
[Arthur] made two key decisions early on, he says. The first had to do with topics. He was distinctly unenthused by the idea of applying chaos theory and nonlinear dynamics to economics, which seemed to be a big part of what [Nobel-winning economist Ken] Arrow had in mind. There were plenty of other groups doing that kind of thing already - and with very few worthwhile results, so far as he could tell. Nor was Arthur interested in having the program build some huge economic simulation of the whole world. "This may have been in [Citibank big shot and Satana Fe Institute benefactor] John Reed's mind," he says, "and it seems to be the first thing engineers or physicists want to do. But it's as if I said to you, 'You're an astrophysicist, why don't you build a model of the universe?' " Such a model would be just about as hard to understand as the real universe, he says, which is why astrophysicists don't do it that way. Instead, they have one set of models for quasars, another set for spiral galaxies, another set for star formation, and so on. They go in with a computational scalpel to dissect specific phenomena.
And that's exactly what Arthur wanted to do in the Sante Fe program... He also wanted people to learn how to walk before they tried to run. In particular, he wanted to see the program take some of the classical problems in economics, the hoary old chestnuts in the field, and see how they changed when you loked at them in terms of adaptation, evolution, learning, multiple equilibria, emergence, and complexity - all the Santa Fe themes...
That emphasis on the old chestnuts got the program in hot water later, says Arthur, when a number of people on the institute's science board accused them of being insufficiently innovative. "But we thought it was just good science, good politics, and good procedure to approach the standard problems," he says. "These are problems that economists recognize. Above all, if we could prove that changing the theoretical assumptions to be more realistic made major differences to the insights you got, maybe getting a feeling of more realism in those insights, then we could show the field that we had really contributed something."
From Complexity, Mitchell Waldrop, p. 244-245
Complex systems researchers need to follow this advice. The availability of petabytes of data and cheap computational power doesn't mean it's a wise idea to build huge models of everything, like say, the entire transcription network of a cell. Traditional biologists may not be that computer-savvy, but they have been very good at defining important biological questions. Most molecular biologists I know would love to understand, on a quantitative level, why a MAP kinase pathway or transcriptional cascade functions the way it does. They're sympathetic to systems-level questions, as long as those are questions with the promise to make an impact on how all biologists, computational and wet-lab, think about about transcription or signal transdusction.
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