Source: Quanta Magazine, Jul 2017
Flack’s focus is on information: specifically, on how groups of different, error-prone actors variously succeed and fail at processing information together. “When I look at biological systems, what I see is that they are collective,” she said. “They are all made up of interacting components with only partly overlapping interests, who are noisy information processors dealing with noisy signals.”
How did you get into research on problem solving in nature, and how did you wind up at the Santa Fe Institute?
I’ve always been interested in how nature solves problems and where patterns come from, and why everything seems so organized despite so many potential conflicts of interest.
Collective computation is about how adaptive systems solve problems. All systems are about extracting energy and doing work, and physical systems in particular are about that. When you move to adaptive systems, you’ve got the additional influence of information processing, which we think allows a system to extract energy more efficiently even though it has to expend a little extra energy to do the information processing. Components of adaptive systems look out at the world, and they try to discover the regularities. It’s a noisy process.
Unlike in computer science where you have a program you have written, which has to produce a desired output, in adaptive systems this is a process that is being refined over evolutionary or learning time. The system produces an output, and it might be a good output for the environment or it might not. And then over time it hopefully gets better and better.
We have this principle of collective computation that seems to involve these two phases. The neurons go out and semi-independently collect information about the noisy input, and that’s like neural crowdsourcing. Then they come together and come to some consensus about what the decision should be. And this principle of information accumulation and consensus applies to some monkey societies also. The monkeys figure out sort of semi-independently who is capable of winning fights, and then they consolidate this information by exchanging special signals. The network of these signals then encodes how much consensus there is in the group about any one individual’s capacity to use force in fights.
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?
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.
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?
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.
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.