The day began with Pieter van Dokkum (Yale) and me arguing about what's important and how to achieve it. In my view, the biggest issue with everything is that we don't believe any of the models, and yet we have to do science with them. How does that work? I don't think anyone has a clear answer. You can say that you are only getting answers subject to very strong asusmptions—that's very true—but that doesn't tell you what to believe and what not to believe. Like, given that the line lists going into 1-D models are wrong in such-and-such ways, what conclusions about stars are in danger of being wrong? In some sense every model-based result we have is mixed with some probability of some kind of ill-specified null model that things are very wrong and anything (within reason, whatever that means) could be happening.
In my research, this really comes up in the question of whether we have true parameters for stars. That is, are we correct about log-g and T-eff and various chemical abundances? At some level it is not just that we can't be right fundamentally (stars don't even have steady-state values for these!) and not just that we can't be right in practice (our models give different answers depending on the data at hand, etc.), it is that we can't know that we are right even when we are right as we can be. All we can really know is whether we do a good job of predicting new data. Compare models in the space of the data! I emphasized that today.
In response to all these issues, I said one thing that made people uneasy: I said we should focus our attention on problems that can be solved with the tools at hand. We should try to re-cast our projects in terms of things for which our models produce stable predictions (like relative measurements of various kinds). I don't think we should choose our scientific questions based on an abstract concept of “what's interesting”. I think we should choose on the concrete concept of what's possible. I not only think this is true, I think it how it has always been in the history of science.