I do research on complex systems, and I love it. I think it is fascinating, and specially fun when I, as a physicist, get to play with data that comes from social, biological or economic systems. I enjoy approaching problems from those fields using the tools I was taught during my “traditional” training, and I think that, as a general rule, a fresh pair of eyes always helps.
Of course, you realize pretty soon that you don’t know nearly enough about those other fields to be able to make any relevant contribution. Hence, collaborations. And that is not only extremely useful thing, but also in principle, really fun: I believe that working together with other people can give rise to results way better that the sum of its parts. Learning from another person’s views and experiences will most certainly expand your mind and the set of tools you will be able to use from that point on. This applies to collaborations in general, but when it comes to interdisciplinary ones, I have found that things can get a little tricky, too.
A few years ago, I became part of a so called “econo-socio-physics” collaboration network. There were physicists, mathematicians, biologists, economists, anthropologists and sociologists, professors, grad students and post-docs, all of us already interested in Complexity. At the beginning, I was really excited about meeting with researchers from other areas (I could tell we all were, when we first got together). We started talking, presenting our current projects, and future ideas to try to brain storm, get feedback or even to establish some collaborations.
Then, I witnessed, astonished, how chaos took over.
People were not speaking the same language, we were not making the same assumptions, nor denoting the same thing by the same term (of course, that was only to be expected, but for some reason, it had never occurred to me that two scientists — as opposed to a scientist and an artist, for example — could not speak the same language at all).
For a while we were quite frustrated, annoyed or even angry at each other. There was some not-exactly-polite yelling involved. Even some scientific pissing contests took place.
Then, slowly, we realized that we just needed to take a step back, and explain things more clearly to each other, assume nothing, take nothing for granted. And most of all, avoid the temptation of judgment. And it worked. I know about a few very cool collaborative projects that were born there. Total success.
A few months ago, I found myself in a similar situation again: I started collaborating with a group from natural sciences with no experience in computation at all, and they had (still do) a hard time understanding even the most basic concepts, results, or methods we used regularly. Or course, I am sure the same would happen to me if the situation was reversed. But this time, I know better, and I am not only trying my best to explain things clearly, being aware of the lack of an overlap between our fields, but I also realize that it is a great opportunity to improve my communication skills, to learn how to explain things really well to the general public, to reach out instead of getting annoyed, and also, to fully understand my own work at a deeper level.
I only hope that, on top of all those wonders, we someday get a paper published out of our collaboration.
— Julia Poncela Casasnovas.