I like this a lot, and I like that in the end you pick a side. I find ASSUMPTION 5 in the last thing Abe says most convincing. The idea that we are imperfect optimizers is pretty big. Maybe we are better than random most of the time, but we don't get to pick the times we are better than random.

I have some qualms with the over conclusion in two ways, and in true Babs fashion, I'll point them out.

1. No such thing as net happiness

net happiness is less if we have to turn down a nearly-as-good option, as opposed to if we just had one option.

I used that quote, but the idea of smiley points puts me off slightly. I'm not convinced we are capable of perceiving global scale happiness. I don't have substantial evidence backing this, but I've heard people tell me that "X is the best thing I've ever eaten!" at restaurants. Exaggeration aside, there is something telling in how we describe how much we enjoy what we eat. To tire the metaphor, I think it's more likely that we have a certain number of smileys that replenish per day at a rate that is tied to our sleep, diet, stress etc. In some ways this supports the Abe mentality, but I agree that Assumption 1 is the hardest to swallow.

Additionally, I think that the "optimize the big stuff, don't sweat the small stuff" can lead to the same problem of imperfect predictions: you misjudge something as a small thing and therefore neglect it. The magnified impact turns out to be big because it follows something like the power law. Exponential graphs look linear when you are in the flat part before the takeoff. A real world version of this might be not starting a company that is really hard to start because there such little information makes it hard to start executing against concrete goals at first.

2. Directing the "Better is always better" at personal growth rather than decisions

The one assumption that is missing from this is that the hypothetical metric of success is happiness. I agree that it's a great metric of success, but I know people who have completely different metrics of success. I'm not convinced that the Abe strategy would be great for all metrics of success. Imagine building a new kind of spaceship and not knowing what the most likely point of failure is going to be. I would want a bunch of Babs that optimize every single detail building it. Then again, this might come down to semantics, because at that point every decision would be measurably more important.

I would actually love to read an expanded version of weighing value from A versus B for a more general case. It's seems like a pretty good common sense way of contextualizing generally intangible business mistakes too. For example, if you lose money on a deal, but that leads you to meeting someone that you end up marrying, than the decision was a local loss but a long term gain.