I can't speak for aphyer, but I tend not to tag my own posts (mostly out of a vague "authors don't get to decide what their works are" sentiment). If it's impeding people from playing I'll make a point of tagging my D&D.Sci scenarios as D&D.Sci (when it's part of a very specific genre and also literally has the name of that genre in the post title there's no point in me being ontologically coy); hopefully that will help.

This is my best lesswrong post. If you haven't read the comments section, you ought to, there's gold in there.

I think this was some of my best fiction-qua-fiction. I don't know how well it communicated anything, or to what extent what it communicated was right.

I hope more people on LW talk more about the potential downsides and edge cases associated with prediction markets, because I think it's an important and underdiscussed topic, and because I don't think I understand them well enough to do that (outside intentionally pathological caricatures in intentionally silly stories).

Fwiw, I really liked seeing someone take an AI-heavy approach to one of these.

Reflections on my performance

I took this game as an opportunity to demonstrate how my modelling library works. This was in its own way a resounding success: I can’t think of a better demonstration of my methodology’s strengths and weaknesses than getting a uniquely deep & justifiable view of the underlying systems - I really wasn’t expecting to be right about everything in this post (minus arguably the last part) - and then significantly underperforming more mainstream approaches. Still, I think I did acceptably, and a combination of decent judgement and excellent luck happened to let my character get a Perfect Feast, so I’m content.

(I really shouldn’t have skipped the “. . . and then use all the insight you got from playing with interpretable models to make best use of uninterpretable treebased models” step^[1] at the end. I’ll know better next time.)

Reflections on the challenge

I know I liked this one, but I’m uncertain as to how much: there were two major aspects I find myself deeply ambivalent about.

First: the relatively low number of rows, high number of columns and nonzero randomness in output made this look like a data starvation problem, but there turned out to be a pretty tight linkage between predictors and responses, such that it actually was more-or-less fair; in other words, the scenario pretended to be harder and jankier than it was. The effect of this for me was kind of videogamey: I’m not sure whether to consider this impeccable design (“good job mechanically playing into the scrappy-underdog-who-keeps-winning-anyway fantasy!”), lowkey disquieting (“should a game about epistemology be doing that, though?”), or a legitimate extra layer of challenge (“I need to git gud at knowing how gud I need to git.”^[2]).

Second: the challenge was in retrospect pretty cheeseable, but none of the actual players actually cheesed it. I can net a low-variance high-Quality Feast just by ordering historical Feasts by Quality and mimicking one of the ones that managed to get Quality=20^[3]; that said, afaict the only person to make use of an approach like this was me, and I still didn’t lean on it anywhere as hard as I could have^[4]. (This could have been fixed with a trivial extension of the existing ruleset by having one or more foods with high and high-variance Sweetness and/or Spiciness, such that they could sometimes fill one or more of the quotas by themselves; these would be disproportionately represented in the highest rows, but in expectation a terrible choice for players.)

There were also plenty of things I just straightforwardly enjoyed. The writing and the premise were both fun, and underlying mechanics were conceptually beautiful and impeccably implemented. Also, I like a game which lets me show off, and a game which kicks my ass; this one did the former qualitatively and the latter quantitatively, which imo counts as two reasons to think it's good. All told, I’d award this a conflicted-but-approving [almost-certainly-at-least-three-and-plausibly-more-than-that-but-I-don’t-know-how-much]/5 for Quality.

1. ^{^}

   Or, potentially, the “construct an entire modelling paradigm around the shape of the problem” step.

2. ^{^}

   I’d have been much less likely to skip that final step if I hadn’t thought I had too little data to justify higher model complexity.

3. ^{^}

   Two of the four dish combinations with Quality=20 had >16 Quality in expectation.

4. ^{^}

   If a tree falls in the forest, and the only person to hear it is over a mile away, is the sound it makes loud?

I look back on this challenge with great fondness, and not just because of how handily I happened to beat it. More than most of our data science scenarios, this one tested the ability to ask the right questions, find the right answers, and then apply them well (LWers tend to have trouble with that last part in particular; I think we could use more things like this).

I continue to think that this is plausibly the best ever installment of that genre I invented, and I continue to think it expands said genre in interesting ways. I also continue to think said genre is a valuable addition to LW, because it provides (limitedly) messy and (tolerably) complicated inferential problems with definitive answers; in other words, it gives us a chance to fail and know we failed.

(This challenge successfully fooled me, and I love it for that. Maybe you'll do better, dear reader?)

Speaking of which, calling in advance that I used my homebaked modelling library for this one, and found it helpful. With its techniques, I discovered that:

A minor bugfix to my investigation code suggests that

My revised revised feast plan is now

On the basis that

And

I think the game Wildermyth is too easy and too janky - unless you crank the difficulty to max, at which point it becomes too hard and too janky - but I keep coming back to it simply because it lets your characters keep Adventuring even as they age (complete with graying hair and lowered movement-per-turn) and have children (who frequently join the party).