When I watched David Laibson's interview, there were two main questions in the back of my head that I wish I could ask him. First, it would be if behavioral economic models introduce any layer of complexity beyond a traditional economical model. This could be both through the addition of inputs, or having the models be nonlinear, instead of linear (these are only examples, as I have little to no idea how any models beyond the supply-demand curves work), or any other modelling change that introduces some added difficulty in using the model. Then, supposing there are indeed some drawbacks of using a behavioral economic model, when does it make sense to use them, and when does it not? If we are modelling climate change and want an economic input, which might have some feedback with both the climate itself, and the output of our model (prediction), would it be better to use a simpler, traditional model and account for the uncertainty, or would it be better to go "all out" with a behavioral model, and likely have less uncertainty but more complexity? Of course, that will depend on what the model is being used for (as a model being adequate or not depends on its objective), but is there some clear "transition" point? If there are economics concentrators who could answer my questions (at least in a conjecture level) or think that they do not make any sense, please say so.