Hi everyone. I've read a bit of the work here on Cluelessness, so I understand the core logic but admittedly am not an expert. It seems its point is that Cross-cause prioritization is almost impossible bc the world is so hard to model.
My reaction: How is this any different from the modeling of any other dynamical system? Dynamical systems are notoriously hard to model, much less modeling something like "total net good" in hypothetical future universes. Is there anything different about philanthropic modeling than, say, running a coffee shop? (i.e. everybody relies on forward-looking modeling, to some extent)
I assume the easier progress on Cluelessness lies more with the HCI side of the equation, rather than increasing the accuracy of our models. If the model is built well, it allows the user to understand its sensitivities so we can make an intuition call. Generally speaking, good risk managers/investors worry about their downside risk much more than their upside risk.
Is there something I'm missing here?
