About 2 years ago, I was home sick with Covid wondering how to spend my time while I waited to get back into the lab. We had some experiments slated, which we had proposed for my NSF fellowship in which we had proposed the idea that Bayesian updating was responsible for the winner/effects. We had pitched it as a simple conceptual model, but since I had a few days at home, I thought I could code it up as a proper agent-based simulation, to see if our intuitions were correct. 10 days later, I had a solid simulation with some cool preliminary results. Let’s write it up as a paper, we said. It will be fun, we said.
Well, careful modeling and writing about said modeling are not trivial, as it turns out, but a few months later, we made the first submission to AmNat (where it was rejected). One of the reviewers had many detailed and constructive comments, so we went back to work, responding to their comments, and a few months later resubmitted to Animal Behaviour. We were very fortunate to get the same helpful reviewer, who was pleased to see we had taken their advice to heart and had many more constructive suggestions. (The other reviewer seems to have had a lot of strong feelings about our work, but that is a blog post for a different day). In any case, 7 months after initial submission, it’s accepted!
I’ve described some of the ideas over on the project page, since this forms the basis for a lot of our ongoing work, but in brief, Bayesian updating does seem to be a pretty good model for winner/loser effects, and it makes some specific predictions that we are excited to try out. Stay tuned for what’s to come.