This post is by MSU faculty Arend Hintze
While I was listening to the many interesting talks of this year’s BEACON congress (2016), I was pondering about the journey that we took together to get here. Fortunately, I was around to not only witness the first BEACON congress, but all others since then, except for 2015. Amazing, exciting, controversial, and interdisciplinary were only some of the words that popped into my mind, and I deeply enjoyed reflecting on that ride. But why? What is the thing I liked most, what made this so special?
That was the moment I had the idea for the title of this blog post, because it characterizes so absurdly what makes working with the people in BEACON so exciting and rewarding. It is the misunderstanding between the different disciplines. When I talk to biologists, for example about mate selection, or navigation, or foraging then this person has a specific animal with a specific repertoire of behavior and methods to study it in mind – the “cow” – and also the limitations of said model system. I can make our computational model systems without constraints, but also, most often, I have no experience or knowledge about the animal my collaborators are talking about. I know what it means to have the “feeling for the animal”[1,2], but that’s it, I don’t have that about model organisms in general (with the exception of C. elegans maybe). I have a feeling about abstract systems, selection pressures, and how to design experiments in the computer, and I code worlds and environments that are loose enough analogies to animal system to get things to evolve – the “milk”.
This necessarily leads to misunderstanding, and in the process of picking up the pieces, we typically both learn things. I understand the animal better, you understand the modeling process, and together we find the right abstractions, and are able to form the exact hypotheses and experiments to do in the future. It is enlightening and rewarding, and we haven’t even conducted experiment yet, but at least we think we know what is going on, until the results come in.
In many cases, these results shake both of our understandings, not because we again communicated poorly, but because neither of us understood what was going on in the first place; or I go and fix a bug and we meet next week, hoping for new and surprising results. However, it is exactly this dialog, where we try to explain to each other what we do and how our system works that allows us to be creative. When I was listening to many talks, I could see how well we now understand each other. Having results from computational systems right between results from organismal systems, and the audience doesn’t even flinch, is amazing. We reached a state where we talk each others language, and appreciate what everyone can bring to the table, without hearing “milk” or “cow” but knowing that we talk about bovine evolution.
I also had the feeling that we might start to lose out on exactly this quality that made us strong. The presentations were great, but also much more focused on each topic, without giving the broader context we all add when we know that the audience isn’t too familiar with what we are working on. It is a sign that the past made a difference, and we indeed learned from each other, and maybe we just rose to a new level? In one of our workshops about AVIDA and Markov Brains/MABE we tried to go back to the basics and explain things from scratch. While that might have been preaching to the choir, I also had the feeling that we could do this more often. The strength of our computational model systems doesn’t come from what we did with them in the past. Their strength comes from what we can do with them in the future. In summary, I think we came very far, we should just make sure that we keep misunderstanding each other in the familiar productive way I learned to love!
Cheers Arend
[1] Holmberg, T. (2008). A feeling for the animal: On becoming an experimentalist. Society & Animals, 16(4), 316-335.
[2] Keller, E.F. (1983) A feeling for the organism. The life and work of Barbara McClintock. New York: Owl Books
