Thanks aphyer for making this scenario and congrats to James Camacho (and Unnamed) for their better solutions.
The underlying mechanics here are not that complicated, but uncovering details of the mechanics seemed deceptively difficult, I guess due to the non-monotonic effects, and randomness entering the model before the non-monotonicity.
It wasn't that hard though to come up with answers that would do OK, just to decipher the mechanics. I guess this is good in some ways (one doesn't just insta-solve it), but I do like to be able to come up with legible understanding while solving these, and it felt pretty hard to do so in this case, so maybe I'd prefer if there were more lower hanging fruit mechanics wise. (So maybe I don't actually prefer simple mechanics as long as some of them are easier to figure out and can be built off of?)
Regarding my own attempt to figure it out:
Thanks to abstractapplic for the comment that tipped me off to the characteristics being quantitative not just a classification, as well as to there possibly being a total food amount characteristic not just spicy/sweet. Multicore's comment on Roc possibly being in a "meaty" category also helped me in regard to the latter.
Ironically though, the model change actually lowered my performance (from 16.30) presumably due to the improved model not taking as much into account penalties for variance that were implicitly present in the earlier interactions + spicy/sweet dish numbers model. I'm still pleased that the "improved" model had more structural resemblance to the actual reality, even if the answer was worse. (the second change in my answer also lowered my expected performance (from 16.13), but I think this was basically coincidental).
I actually did consider the possibility that the characteristics might have variability, including explicitly considering a uniform distribution between bounds, but Claude did an initial probe and dismissed it. I guess I should have pushed Claude on this! But also I'd been refining imperfect models for a while and and didn't want to spend the time/effort required to develop a new model at that time if it didn't instantly seem promising.
I heavily used AI for this scenario. It helps a lot to quickly do stuff that would take a lot more effort without, and in principle should also help with exploring, but I feel like its tendency to focus on what's right in front of it can also distract me from switching approaches. Also I wish I had done more exploration of the data before asking AI to come up with a solution. As it was Claude (4.5 Opus) found the spicy/sweet categories without me having previously done so, which robbed me of having the pleasure of finding them on my own, as other commenters who mentioned them likely did.
I'm not sure if anyone appreciated my insanely long AI-generated comments, I could avoid doing that in the future if people were annoyed by it.
This is a follow-up to last week's D&D.Sci scenario: if you intend to play that, and haven't done so yet, you should do so now before spoiling yourself.
There is a web interactive here you can use to test your answer, and generation code available here if you're interested, or you can read on for the ruleset and scores.
A dish has three properties: Size, Spiciness and Sweetness.
| Dish | Size | Spiciness | Sweetness |
| Ambrosial Applesauce | 0 | 0 | 2 (±1) |
| BBQ Basilisk Brisket | 2 (±1) | 1 (±1) | 1 (±1) |
| Chili Con Chimera | 2 (±1) | 2 (±1) | 0 |
| Displacer Dumplings[1] | 0 | 0 | 0 |
| Ettin Eye Eclairs | 1 (±1) | 0 | 4 (±1) |
| Fiery Formian Fritters | 1 (±1) | 2 (±1) | 0 |
| Geometric Gelatinous Gateau | 1 (±1) | 0 | 3 (±1) |
| Honeyed Hydra Hearts | 1 (±1) | 0 | 2 (±1) |
| Killer Kraken Kebabs | 3 (±1) | 3 (±1) | 0 |
| Mighty Minotaur Meatballs | 3 (±1) | 0 | 0 |
| Opulent Owlbear Omelette | 2 (±1) | 0 | 0 |
| Pegasus Pinion Pudding | 1 (±1) | 0 | 1 (±1) |
| Roc Roasted Rare | 4 (±1) | 0 | 0 |
| Scorching Salamander Stew | 2 (±1) | 3 (±1) | 0 |
| Troll Tenderloin Tartare | 1 (±1) | 0 | 0 |
| Vicious Vampire Vindaloo | 2 (±1) | 4 (±1) | 0 |
| Wyvern Wing Wraps | 1 (±1) | 1 (±1) | 0 |
Cooking is not an exact science, and there's a ±1 variation in each dish (effectively adding 1d3-2 to each of its stats). For example, if you cook Chili Con Chimera (usually Size 2 and Spiciness 2), you might end up with a large mild batch (Size 3 Spiciness 1), or a small spicy batch (Size 1 Spiciness 3), etc. etc. However, any stat that is at 0 will stay there: your Chili will never end up sweet, for example, and your Eclairs will never end up spicy.
The ideal Feast has in total Size 10, Spiciness 5, and Sweetness 5.
Each of these stats increases your score up to this ideal, and then decreases it thereafter. For example, a total Size of 6 will score you 6 points, Size 9 will score you 9 points, Size 12 will score you 8 points (10 minus 2), and Size 15 will score you 5 points (10 minus 5).
The first order of strategy was to select dishes that tended to generate the correct overall amount of size/spiciness/sweetness.
The subtler element of strategy was to minimize variance by using as few dishes as possible for each stat: reaching e.g. 5 Sweetness using 3 Sweet dishes is worse than reaching 5 Sweetness using 2 Sweet dishes, because there's more variance away from your ideal 5.
The perfectly optimal feast that used as few dishes as possible for each stat was actually very tightly constrained:
However, there were many other options that would get extremely close in score, just e.g. having very slightly more variance in one stat or another, or accepting a Spiciness/Sweetness of 4 off a single dish (4±1 scores only a tiny bit worse on average than 5±2).
| Player | Dishes | Size | Spiciness | Sweetness | Average Score |
| Optimal Play | AC(D?)GKR) | 10 (±4) | 5 (±2) | 5 (±2) | 16.94 |
| James Camacho | AGORTV | 10 (±5) | 4 (±1) | 5 (±2) | 16.67 |
| Unnamed | ABDMOPV | 10 (±5) | 5 (±2) | 4 (±3) | 16.30 |
| simon | BDGKOPT | 10 (±6) | 4 (±2) | 5 (±3) | 16.09 |
| Multicore | ABDGRS | 9 (±4) | 4 (±2) | 6 (±3) | 15.89 |
| abstractapplic | ABDHKO | 8 (±4) | 4 (±2) | 5 (±3) | 15.52 |
| Yonge | ABFOP | 6 (±4) | 3 (±2) | 4 (±3) | 12.63 |
| Entirely Random Play | ?? | ?? | ?? | ?? | 9.88 |
Congratulations to all players, especially James Camacho.
The Isamandan feasts in your dataset were generated as follows:
As usual, I'm interested to hear any other feedback on what people thought of this scenario. If you played it, what did you like and what did you not like? If you might have played it but decided not to, what drove you away? What would you like to see more of/less of in future? Do you think the scenario was more complicated than you would have liked? Or too simple to have anything interesting/realistic to uncover? Or both at once? Did you like/dislike the story/fluff/theme parts? What complexity/quality scores should I give this scenario in the index?
These dumplings Displace themselves out of your stomach after being eaten.