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.
or spherical cows.
Ludic Fallacy
61–70 of 80 posts
Re: Ludic Fallacy
#62Re: Ludic Fallacy
#63Seems 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.
"Map is not the territory." Straightforward, succinct, and no need to use latin or invoke Plato.
Re: Ludic Fallacy
#64Seems 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.
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.
Re: Ludic Fallacy
#65Seems 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.
"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.
Re: Ludic Fallacy
#66Seems 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.
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 a…
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Re: Ludic Fallacy
#67Seems 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.
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 a…
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.
Re: Ludic Fallacy
#68I haven't understood this as a fallacy. In the "suspicious coin" example, there is an opening assumption that the coin is fair, but it transpires clear evidence to the contrary. It isn't a fallacy to continue to treat this as a game: it has just become sensible to drop the assumption and treat it as a game where you are unsure about whether the coin is fair (as the second player in the example, in fact, does).
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Re: Ludic Fallacy
#69That's Taleb. Taleb ran a fund that bought options way out of the money. This loses money every year, unless there's a big crash. His fund happened to be active in 2008, and made a ton of money that year. He doesn't release the numbers for other years. Not clear if this is a net win over a full business cycle.
Taleb is sort of a counter to Black-Sholes option pricing. Black-Sholes assumes a Gaussian distribution. Given that assumption, a few numbers let you quantify risk. That's convenient, but somewhat unrealistic, and was taken way too seriously by the bond market from the 1980s to 2008. It provided a philosophical underpinning for the junk bond market. (They can't possibly all go bad at the same time, can they?)
Re: Ludic Fallacy
#70Earlier quoted context omitted.
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 a…
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.
You’re getting too hung up on the word “game.” In this context it just means the rules of the model.