AAR:
I modeled crabmonsters as triangular rather than exponential. Two ways I could have gotten this right: (1) GM-modeling; I was pretty sure all of these were dice rolls but forgot that for crabmonsters, or (2) uncertainty hedging; if there was an obvious next encounter after merpeople to extend further than modeled, it was crabmonsters, not e.g. pirates or water elementals.
Demon whales were spot-on. I said 76.4% fatality rate, actual was 78%.
My notes on merpeople were correct, imprecise, and as follows:
SUPER SUS, there is a little blip right near the highest damage mark. Very high uncertainty here—they may kill lots?
Nessie I gave slightly more mean and slightly less variance than I should have, guessing 60+4d12 (86, 6.90^2) when the truth was 40+10d8 (85, 7.25^2). Still, I said 2.39% chance of fatality and actual fatality was “~2%”.
I did much better on this one than the last. :) Relatedly, the primary difficulty at my “how do I get money” work is that when we tell an e-commerce merchant “this particular order? we’re not going to insure this order against fraud, it’s too risky”, they don’t ship it, and we never find out much info in that part of the distribution.
I thought this was less speculative than its predecessors, not more. All of the distributions were reasonable. While there could have been unknown unknowns like another type of water elemental or pirates that either succeed or don’t do much damage, there in fact weren’t. There were known unknowns that were pretty much called out explicitly. This was all signaled by the title which I read as “explicitly not a black swan scenario”. The somewhat hidden second society of merpeople was strongly clued by having a very obvious two-humped pirate distribution. Altogether this felt like a good meld of data science and fair puzzle.
The one “GM modeling” that I think would probably be important for doing well on your subsequent puzzles is “usually the underlying model is decently simple; also usually it involves dice rolls like 10+2d6 or 1d8*1d8″.