My plan of “when out of time, do enough to capture pairwise stuff and combine with an adhoc heuristic” works surprisingly well in these. :D
Which SCPs are available for capture each quarter, how do we know how many teams of which types were available to capture them, and how were teams allocated in the past?
Feedback: my gut reaction is that it would have been extremely difficult to divine the underlying-source nature of the data. Do you have a suggested method or technique that would have been likely to work? I think that noticing the success rates of teams per locations should have been possible, then ignoring that going forward. But how to get from profits, past the sales/security noise, past the tags noise? Seems like maybe looking at the full matrix of P(tag A | tag B) could have revealed an underlying structure? I’ll have to think on that one.
Also, my favorite part of this was tracking down SCP-1182, but I don’t think a similar thing should be present in most D&D.Sci challenges. :D
Which SCPs are available for capture each quarter, how do we know how many teams of which types were available to capture them, and how were teams allocated in the past?
I’ve also edited the doc to add this: Aside from their dependence on tags, your predecessors’ actions were almost entirely random, sending 2 teams of each type plus 1-5 additional random teams (1d3 at first, up to 1d4 in 1950 and 1d5 in 2000) to target random SCP objects.
Feedback: my gut reaction is that it would have been extremely difficult to divine the underlying-source nature of the data. Do you have a suggested method or technique that would have been likely to work?
I did not intend for it to be realistically possible for players to fully capture the underlying nature of the data. The goal was to have a tag-to-profit mapping that followed reasonably simple rules, but exhibited complicated behaviors that provided a lot of depth for players to analyze. My hoped-for pattern of ‘how to solve this problem’ took the form of:
Sanity-checking the data. What is up with these weird rows? Get rid of them!
Safe/Euclid objects are much more profitable on average. Let’s just try a bunch of random Safe objects with no Keter ones.
Chasing down tags to do better than random at identifying profitable Safe objects. (‘Mechanical’, ‘Mobile’ and ‘Virtual’ are probably your best friends here.)
Keter objects have the highest profit numbers. Is there a way we can get those?
Chasing down tags to do better than random at identifying profitable Keter objects. (This would require a lot more effort, including a lot of multi-tag effects like the ‘Organic AND Mechanical’ one, before it could make your payoff from this beat just pursuing Safe objects. You do get a bonus ending though.)
Overall most people just did steps #1-3, which is not unexpected. In your ‘escape’ plan you actually scored nearly as high as your ‘invaluable’ plan in expectation, going after SCP-2797, SCP-3688 and SCP-4004 (all three Keter objects that showed up in the max-payoff scenario), but losing some payoffs on Euclid-class objects.
My plan of “when out of time, do enough to capture pairwise stuff and combine with an adhoc heuristic” works surprisingly well in these. :D
Which SCPs are available for capture each quarter, how do we know how many teams of which types were available to capture them, and how were teams allocated in the past?
Feedback: my gut reaction is that it would have been extremely difficult to divine the underlying-source nature of the data. Do you have a suggested method or technique that would have been likely to work? I think that noticing the success rates of teams per locations should have been possible, then ignoring that going forward. But how to get from profits, past the sales/security noise, past the tags noise? Seems like maybe looking at the full matrix of P(tag A | tag B) could have revealed an underlying structure? I’ll have to think on that one.
Also, my favorite part of this was tracking down SCP-1182, but I don’t think a similar thing should be present in most D&D.Sci challenges. :D
I’ve also edited the doc to add this: Aside from their dependence on tags, your predecessors’ actions were almost entirely random, sending 2 teams of each type plus 1-5 additional random teams (1d3 at first, up to 1d4 in 1950 and 1d5 in 2000) to target random SCP objects.
I did not intend for it to be realistically possible for players to fully capture the underlying nature of the data. The goal was to have a tag-to-profit mapping that followed reasonably simple rules, but exhibited complicated behaviors that provided a lot of depth for players to analyze. My hoped-for pattern of ‘how to solve this problem’ took the form of:
Sanity-checking the data. What is up with these weird rows? Get rid of them!
Safe/Euclid objects are much more profitable on average. Let’s just try a bunch of random Safe objects with no Keter ones.
Chasing down tags to do better than random at identifying profitable Safe objects. (‘Mechanical’, ‘Mobile’ and ‘Virtual’ are probably your best friends here.)
Keter objects have the highest profit numbers. Is there a way we can get those?
Chasing down tags to do better than random at identifying profitable Keter objects. (This would require a lot more effort, including a lot of multi-tag effects like the ‘Organic AND Mechanical’ one, before it could make your payoff from this beat just pursuing Safe objects. You do get a bonus ending though.)
Overall most people just did steps #1-3, which is not unexpected. In your ‘escape’ plan you actually scored nearly as high as your ‘invaluable’ plan in expectation, going after SCP-2797, SCP-3688 and SCP-4004 (all three Keter objects that showed up in the max-payoff scenario), but losing some payoffs on Euclid-class objects.