Sokol: Human society also seems a little chaotic recently. Are you ever tempted to apply this kind of thinking in that direction?
Flack: Absolutely. With the help of some friends in finance and economics, we are moving a little bit into financial markets in our research. I think that’s an amazing model system for asking these kinds of collective computation questions. My next meeting today is about how to apply our criticality approach, coupled to new machine-learning results that are able to find phases of matter for physical systems, to either political data or market data. Our goals are to address whether there is evidence for phase transitions or critical phenomena in financial data and to understand the behavioral processes that might move markets closer to critical points.
Sokol: Now that you can follow up on these kinds of questions to your heart’s content, what would you say if you could visit yourself back at Cornell, in the stacks of the library?
Flack: Jorge Luis Borges is one of my favorite writers, and he wrote something along the lines of “the worst labyrinth is not that intricate form that can trap us forever, but a single and precise straight line.” My path is not a straight line. It has been a quite interesting, labyrinthine path, and I guess I would say not to be afraid of that. You don’t know what you’re going to need, what tools or concepts you’re going to need. The thing is to read broadly and always keep learning.
Sokol: Can you talk a bit about what it’s like to start with a table of raw data and pull these sorts of grand patterns out of it? Is there a single eureka moment, or just a slow realization?
Flack: Typically what happens is, we have some ideas, and our group discusses them, and then over months or years in our group meetings we sort of hash out these issues. We are ok with slow, thoughtful science. We tend to work on problems that are a little bit on the edge of science, and what we are doing is formalizing them. A lot of the discussion is: “What is the core problem, how do we simplify, what are the right measurements, what are the right variables, what is the right way to represent this problem mathematically?” It’s always a combination of the data, these discussions, and the math on the board that leads us to a representation of the problem that gives us traction.
We have this argument at the Santa Fe Institute a lot. Some people will say, “Well, at the end of the day it’s all math.” And I just don’t believe that. I believe that science sits at the intersection of these three things—the data, the discussions and the math. It is that triangulation—that’s what science is. And true understanding, if there is such a thing, comes only when we can do the translation between these three ways of representing the world.
This article appears courtesy of Quanta Magazine.
Excellent story from Google News..