Professor Laibson's suggestion that rational choice theory, behavioral economics, machine learning, and simple theory are all valuable and possible to combine in some way in designing models for and predicting economic behavior was very insightful. All of these ideas appear to point to a larger truth, but there are drawbacks to oversimplification, overcomplication, and lack of nuance. I am curious as to how these competing ideas are implemented in the behavioral economics field today. Professor Goodman's observation that perfect laboratory conditions are impossible when gathering economic data also serve to highlight that both oversimplified and overcomplicated models could lack some deeper insights about the phenomena being observed in real world markets where it is impossible to observe or include every variable and where oversimplification could prove disastrous when designing policy. It reminds me of the idea taught in Stat 110 that all models are wrong but some are useful. Combining these various and somewhat competing models helps us to get a fuller picture of the story behind why people make the economic choices that they make, and I appreciate stepping back and thinking about the merits and disadvantages of the lenses that we choose to view economic behavior through.