4 ms·

Seems like a long winded and sanctimonious way of saying that models are simpler than the reality that they mimic. Bears in woods and catholic popes come to mi

Seems like a long winded and sanctimonious way of saying that models are simpler than the reality that they mimic. Bears in woods and catholic popes come to mind.

karmakaze 6y ago
or spherical cows.
- deleted 6y ago
  [deleted]
briankelly 6y ago
"Map is not the territory." Straightforward, succinct, and no need to use latin or invoke Plato.
ajross 6y ago
It sounds obvious, but in practice it's not. It's an election year, so take polling as a great example: Every poll of a race comes with two numbers, one a fraction of support for whatever issue or candidate is being measured, and the other a "margin of error" (which in the industry is a 95% confidence interval, I believe). This is just a statistical measure based on sample size. It's backed by two centuries of math and no one interestingly disputes the way the number is calculated or whate it means. And there are a LOT of these polls taken. So take all those polls, check their variance, and you'll find that it's much higher than the value you'd expect given the margins of error they reported. This is a routine effect. So square that: why are the MoE numbers "wrong", given that multiple measurements of the same number are giving values with more noise than expected? The answer is exactly this paradox. The model, that all these polls are measuring the same thing, is wrong. They aren't measuring the same thing. The different polls use different sampling methods, they weight their data using different algorithms, with opinion polls they phrase the questions slightly differently (or just ask the same questions in a different order), all of which affect the results. And this happens everywhere in science. It's a frightenly easy mistake to make, especially as data sets get large.
- deleted 6y ago
  [deleted]
- jayd16 6y ago
  Hmm this is a surely a common mistake to make but what does it have to do with games? If you sampled poker plays with similar differences you'd get similar errors.
  - garmaine 6y ago
    You’re getting too hung up on the word “game.” In this context it just means the rules of the model.
gmuslera 6y ago
"different", but not in decimal points. Sometimes what you interpret as noise could give you vastly different results. And it can be bigger than just positive feedback loops.

Seems like a long winded and sanctimonious way of saying that models are simpler than the reality that they mimic. Bears in woods and catholic popes come to mind.

karmakaze 6y ago

or spherical cows.

deleted 6y ago
[deleted]

briankelly 6y ago

"Map is not the territory." Straightforward, succinct, and no need to use latin or invoke Plato.

ajross 6y ago

It sounds obvious, but in practice it's not. It's an election year, so take polling as a great example: Every poll of a race comes with two numbers, one a fraction of support for whatever issue or candidate is being measured, and the other a "margin of error" (which in the industry is a 95% confidence interval, I believe). This is just a statistical measure based on sample size. It's backed by two centuries of math and no one interestingly disputes the way the number is calculated or whate it means. And there are a LOT of these polls taken. So take all those polls, check their variance, and you'll find that it's much higher than the value you'd expect given the margins of error they reported. This is a routine effect. So square that: why are the MoE numbers "wrong", given that multiple measurements of the same number are giving values with more noise than expected? The answer is exactly this paradox. The model, that all these polls are measuring the same thing, is wrong. They aren't measuring the same thing. The different polls use different sampling methods, they weight their data using different algorithms, with opinion polls they phrase the questions slightly differently (or just ask the same questions in a different order), all of which affect the results. And this happens everywhere in science. It's a frightenly easy mistake to make, especially as data sets get large.

deleted 6y ago
[deleted]
jayd16 6y ago
Hmm this is a surely a common mistake to make but what does it have to do with games? If you sampled poker plays with similar differences you'd get similar errors.
- garmaine 6y ago
  You’re getting too hung up on the word “game.” In this context it just means the rules of the model.

gmuslera 6y ago

"different", but not in decimal points. Sometimes what you interpret as noise could give you vastly different results. And it can be bigger than just positive feedback loops.