The fundamental problem is the inverted-U shape of payoffs to “optimizing” any one factor (or a small subset of key focus areas). Beyond a point, you’re just “o

The fundamental problem is the inverted-U shape of payoffs to “optimizing” any one factor (or a small subset of key focus areas). Beyond a point, you’re just “overfitting” to the incorrect metric and underperforming in the truer sense. Yeah, you might not have a metric for the “deeper value”... c’est la vie! Any decision making approach that tries to ignore that problem/limitation is simply stupid. The other problem with prizing efficiency is that we often optimize for efficiency under an incorrect model of the situation (model-reality-mismatch) — underweighting the likelihood of upsetting possibilities. That’s essentially what the idea of “black swan”/“fat tails” is about. It’s not really about statistics, unless you’re using a flawed and over-simplified statistical model to ground your metric of efficiency. IMHO the same problem underlies the approach/framework of behavioral economics. In many situations, observed human behavior might be “irrational” only because your model of reality is naively simplistic. It shouldn’t be surprising that a satisficing approach works better in reality than an optimizing approach; if it does sound surprising, consider that your intuitions might be biased by an incorrect model of reality!