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The algorithms probably aren't that great, and more of them would likely have diminishing returns. Adding substantially more false positives could actually be a bad thing.

As someone else mentioned. The 35k parameters is skeptical. Taleb and Tversky and Kahneman have good evidence that most algorithms are better with less parameters. The more parameters, the more noise.




> The more parameters, the more noise.

Interesting observation.

Do you have more specific references? Those three are so popular it’s hard to narrow to commentary on parameters and noise.


In my opinion, it's a large part of Thinking, Fast and Slow and The Black Swan.

Basically, as your signals approach infinity, your chance of finding correlation approaches 1. Taleb argues your chance of finding causation is likely to decrease.




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