I think 0th-order, 2nd-order and 3rd-order forecasting should be called threecasting, fivecasting and sixcasting respectively. This easily lets speakers differentiate between layers; also, imo, names which are bad puns tend to stick.
abstractapplic
Karma: 1,707
Second link is broken.
D&D.Sci (Easy Mode): On The Construction Of Impossible Structures [Evaluation and Ruleset]
Response to clarifying question:
Yes. The Duke has learned the hard way that his architects’ guesses as to how much their projects will end up costing are consistently worse than useless; if you want to optimize on cost as well as impossibility, that’s another thing you’ll have to deduce from the record of finished projects.
D&D.Sci (Easy Mode): On The Construction Of Impossible Structures
Reflections on my performance:
I’m pleasantly surprised by the effectiveness of my reasoning, and of my meta-reasoning. Not only did my loadout do well, but my calibration was impressively close: the final decision I pegged at a “~95%” success rate got 94.4%, and most of the alternative strategies I mentioned in my post were similarly on-the-nose.
(Unfortunately, my meta-meta-reasoning could still use some work. I figured out that this was a “linear-ish logistic success model with some interactions on top” kind of problem, took this as an opportunity to test that library I made, created a good predictor with a bunch of pretty/informative graphs . . . and then found myself thinking “only need one minigun? doesn’t sound right to me”, “why would Tyrants/Artillery and Scarabs/Minigun-or-Flamethrowers be so much stronger than every other potential feature interaction?”, and “I’m totally gonna turn out to have screwed up and wish I’d handled this with XGBoost, better not even mention how I built my model”. If I’d been more calibrated about how calibrated I ended up being, this could have been a really good chance to show off by calling in advance that my unconventional ML approach would succeed here.)
Reflections on the challenge:
This was the 2D performance thing I tried to pull off in Boojumologist, but with better conceptual underpinning and flawless execution. I’m proud, gladdened and envious: can’t think of a single way to improve this scenario.
(I, uh, may be biased by how well I happened to do: please take this feedback with a grain of salt.)
By imitating other players
As Jay Bailey mentioned, you can look at how other players approached challenges, and copy the approaches that worked. Pablo Repetto’s playthroughs of three early .scis seem particularly worthwhile given your situation, both because of how comprehensive & well-written they are, and because they were made by someone in the process of learning to use code on data science problems (the first playthrough was done in pure Excel, the other two were handled in Python).
By following a sensible strategy
Below is my standard plan for investigating a dataset, synthetic or otherwise (cribbed from an otherwise-mediocre Udacity course I took most of a decade ago, and still worth following).
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Univariate Analysis: How is each feature distributed when considered in isolation? You should probably make a histogram for each column.
Bivariate Analysis: Construct and check the correlation matrix between all features. Are there clusters? Create scatterplots (or equivalent) for any pair of features which correlate unusually strongly, any pair of features where at least one is a response variable, and any pair of features you find yourself curious about.
Feature Derivation: Based on what you’ve seen so far – and/or common sense – are there meaningful features you can create from what you’ve been provided? (i.e., if you’re given “Number of Wizards”, “Number of Sorcerors” and “Number of Druids” for each row, it might be worth creating a “Total Number of Magic Users” column.) Investigate how these features interact with others.
ML Modelling: If you can, and it seems like a good idea, build an ML model predicting the important/unknown features from those you have. If constructed successfully, this becomes an oracle you can ask about the outcome of any possible choice you could make. (XGBoost and similar tools are extremely versatile, and have pretty good performance on most problems.)
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(The above is just a rough guide for what to do when you don’t know what to do. If you follow it, you should pretty quickly find yourself with a list of rabbitholes to fall down; you should probably err on the side of dropping everything and deviating from the path as soon as you find something interesting.)
By playing easier D&D.Scis
Difficulty of D&D.Sci games tends to be both high and high-variance; it’s usually assumed that players will have both data-manipulation and model-building skills. For what it’s worth, I can confirm that two relatively-approachable scenarios where not-using-ML won’t put you at a disadvantage are (spoilered because this technically leaks information about them):
If I’m following your notation right, it looks like you mixed up Flamethrowers and Miniguns.
I’m glad you liked it!
(. . . could you spoiler your strategy and win chance? I know this challenge is three years old, and what you mention here isn’t game-breaking info, but I want to keep it possible for people looking/playing through the archives to seek clarifications in the comments without unwittingly/unwillingly learning anything else about the scenario.)
Unintentionally Creating Value
An Unintentional Compliment
That makes sense, ty.
The leaderboard will track how well you’ve done relative to random/best play at the # of soldiers you chose to bring.
Could you elaborate on this? I think I’d do better relative to best play with
high numbers of soldiers,
and do better relative to random play with
low numbers of soldiers,
so it’s not clear which way I should lean; also, I don’t know how you plan to quantify “relative to”.
What we’re facing:
A horrifying number of Tyrants,
A large quantity of Scarabs and Abominations, and
A below-par-given-they-showed-up-at-all-but-still-significantly-above-zero count of Crawlers and Venompedes.
Relevant Weapons:
Artillery is the optimal counter for Tyrants.
Miniguns are very good at handling Scarabs (to the point that bringing more than one would likely be overkill), and pretty useless at most handling most other xenos (to the point that bringing more than one would likely harm our chances).
Lances are good counters for anything which isn’t a Tyrant or a Scarab. (And also not-terrible vs Tyrants)
Torpedos are slightly better than Lances when facing Abominations, and only slightly worse than Artillery when facing Tyrants.
(As far as I can tell, the other four weapons aren’t worth considering.)
Current strategies per number of soldiers:
8 Soldiers: 3 Artillery, 2 Lances, 1 Minigun, 2 Torpedos.
(My model says this gives me >99% chance of survival, but also says that about just bringing one of every weapon. We can be more daring!)
7 Soldiers: 3 Artillery, 2 Lances, 1 Minigun, 1 Torpedo.
(My model says this gives me ~95% chance of survival.)
6 Soldiers: 2 Artillery, 2 Lances, 1 Minigun, 1 Torpedo.
(My model says this gives me about a 2⁄3 chance of waking up the next morning.)
5 Soldiers: 2 Artillery, 1 Lance, 1 Minigun, 1 Torpedo.
(My model says this has slightly worse odds than a game of Russian Roulette with five bullets loaded.)
4 Soldiers: 1 Artillery, 1 Lance, 1 Minigun, 1 Torpedo.
(My model says this almost gives me an entire 1% survival chance.)
If I have to pick one strategy:
7 Soldiers: 3 Artillery, 2 Lances, 1 Minigun, 1 Torpedo.
Description of an investigative cul-de-sac:
I notice that
Duels between a Tyrant and an Artilleryman always end well.
Duels between a Tyrant and a Minigunner, Phaser or Flamethrower always end badly.
Tyrant vs Artilleryman 2v2s . . . don’t happen, ever. (Turns out the quartermasters do display some nonrandom behaviors, and one of these is a bias towards weapon variety.)
2v2s involving two Tyrants, an Artilleryman, and someone who’d lose a 1v1 against a Tyrant . . . end well pretty much exactly half the time, regardless of which [MPF] is used.
I reason that
This is what we’d see in a turn-based fight where humans
aggressivelyheroically always take the first move, and the xenos move randomly. The Artilleryman caps a Tyrant every time; the remaining Tyrant then picks a random human to squish; they pick the dud half the time; we get the coinflip we see.But then
I find out that there are 2v1 fights between two Tyrants and a lone Artilleryman, and these have the exact same 50% win chance; the dud isn’t even useful as a decoy; my hypothesis is falsified.
From all this I conclude
Absolutely nothing.
Like, conceptually it’s absolutely unpredictable
That’s exactly what I was going for; I wanted phenomena which couldn’t have been predicted without using the dataset.
Misc. prelim notes:
There’s a random element. (Existence proof: 16079 and 17759 were the same fight but we only lost 17759.)
There’s an implicit chrono effect: It looks like this war has been developing not necessarily to our advantage. (Luckily it seems like this is probably ‘just’ enemies outnumbering our troops more frequently in later rows, and not anyone actually getting better/worse at their job.)
The number of troops sent scales with the size of the enemy forces, making inference trickier; however, I haven’t seen anything contradicting the hypothesis that loadouts are decided by throwing darts at a board.
Specific weapons counter specific enemies: in particular, the Minigun is usually pretty lousy, but drops Scarabs like flies.
I expected to find synergies between weapons, and didn’t. I did, however, find some antisynergies: Miniguns and Flamethrowers are hella redundant (presumably because they’re both anti-Scarab bugspray), and the [MPR] set all clash with each other (“Why do you need Gun? You already have Gun!”)
Guaranteed victories seem possible. (A single soldier with a minigun can perfectly-reliably survive 5 Scarabs, but not 6.)
Thanks for running this when my one was going to be late, and thanks for checking with me beforehand.
(Also, thanks for the scenario, like, in general: it looks like a fun one!)
I (to my own surprise) got an “above average” score when I took this test a few years back, which I attribute mostly to the lack of emotional and circumstantial ‘noise’ in the images. I don’t think being able to tell what is being emoted by a professional actor told to display exactly one (1) emotion, with no mediating factors, has much connection with being able to read actual people.
(. . . though a level-2 version with tags like “excited but hesitant” or “proud and angry” or “cheerful; unrelatedly, lowkey seasick” could actually be extremely useful, now I think on it.)
This has connotations of being angry, which I don’t think is what you’re going for. (Unless Peter is getting mad at Oskar for potentially revealing his scheme to his bosses by doing something too similar, or he’s irritated that a kindred spirit isn’t recognising him fast enough, or unless I’ve completely misunderstood the implication here.)