Sir Edmund

• Sir Edmund 3 Oct 2023 18:36 UTC

  1 point

  0

  on: Fifty Flips

• Sir Edmund 16 Aug 2023 22:01 UTC

  4 points

  0

  on: Red Pill vs Blue Pill, Bayes style

  “That means that for our posterior from above, if you choose blue, the probability of the majority choosing red is about 0.1817. If you choose red, it’s the opposite: the probability that the majority will choose red is 1-0.1817 = 0.8183, over 4 times higher.”

  It may well be that I don’t understand Beta priors, or maybe I just see myself as unique or non-representative. But intuitively, I would not think that my own choice can justify anywhere close to that large of a swing in my estimate of the probabilities of the majority outcome. Maybe I am subconsciously conflating this in my mind with the objective probability that I actually cast the tiebreaking vote, which is very small.

• Sir Edmund 16 Nov 2021 15:46 UTC

  6 points

  0

  on: D&D.Sci Dun­geon­crawl­ing: The Crown of Com­mand Eval­u­a­tion & Ruleset

  Thank you for making this. I have enjoyed following the D&D.Sci series, even though I don’t get around to posting full solutions.
  
  One thing that the series has given me is a better awareness of the limits of data science. Given enough time and effort, you can parse a data set along as many dimensions as you please, but the amount of time and effort needed grows exponentially based on the number of possible variables. If this scenario were a video game, I imagine that just controlling a party through a few different dungeons, and thereby seeing which enemies did high damage against which parties, would quickly give an intuitive sense and at minimum would help a strategist to avoid a lot of clear mistakes. The same goes for the League of Defenders of the Storm scenario—an actual player of the game would quickly learn that level 1′s beat their corresponding level 6′s in play, whereas that fact wasn’t as obvious from observing the overall data set.

  So both on-the-ground experience and data science have their uses. It’s valuable to practice what can be done with data science alone, but one key takeaway from this series is that if I’m ever making a bet with my own life ( (or just a lot of money) on the line, I pray I’ll get a chance to practice my strategy as well as to observe the relevant data.

• Sir Edmund 9 Nov 2021 16:57 UTC

  3 points

  in reply to: Sir Edmund’s comment on: D&D.Sci Dun­geon­crawl­ing: The Crown of Command

  Regarding the overall levels of successful teams: I was surprised to see that the total average level of adventurers on victorious teams was 3.69474, and on defeated teams it was 3.477. That’s not as big a difference as might be guessed, and it suggests that the precise composition of the team (mix of types of adventurers) matters a lot more than the overall level. This is very helpful, because levels are the primary constraint on our resources.
  
  To get possible insight into the various roles, I calculated the victory rates of teams that lacked a specific type of adventurer. Here they are:
  
  No Fighters: 740/​1247
  No Rangers: 845/​1308
  No Mages: 766/​1235
  No Clerics: 654/​1230
  No Druids: 821/​1215
  No Rogues: 832/​1247
  
  Overall, missing a Cleric seems to hurt the most, and missing a Druid seems the easiest to overcome. But again, the success/​failure rates still seem to be tied more to tailoring the party to the specific threats it will face, rather than finding a strong-across-the-board team.

• Sir Edmund 8 Nov 2021 21:08 UTC

  3 points

  on: D&D.Sci Dun­geon­crawl­ing: The Crown of Command

  If we filter the data based on the encounter which ended an unsuccessful Adventuring party, I notice that the groups foiled by Poison Needle Traps disproportionately had no Rogues.
  
  Filtering by adventurer type, I notice that groups with no Rogues did not do well against dungeons with PNTs.
  
  Thus, I think including at least one Rogue on the team heading for the Lost Temple of Lemarchand is wise.