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A true future predicting algorithm should be aware of its existence and the affect its prediction will have over the receiver / receivers.
Indeed, shouldn't that be the point?
Taleb's preachings come to mind
A true future predicting algorithm should be aware of its existence and the affect its prediction will have over the receiver / receivers.
A true future predicting algorithm should be aware of its existence and the affect its prediction will have over the receiver / receivers.
https://www.nobelprize.org/prizes/economic-sciences/1995/cer...
There's an old saying that goes something like there are countless way to fail, but only a few ways to succeed. Perhaps there's more complexity in modeling failure?
Taleb's preachings come to mind
How exactly?
Difficulties of Prediction Ultimately, our world and future are unknowable and unpredictable because of various factors, including:
• The role of chance and luck in inventions and game-changing discoveries
• The inherently unpredictable nature of dynamical systems and variables that behave in inconsistent or nonlinear ways
• We can’t make accurate predictions with flawed and incomplete information.
The combination of the 2 sets of factors above means that: humans are terrible at prediction, yet we keep making predictions without realizing how frequently we’re off the mark. Taleb calls this the “scandal of prediction”.
[0] - https://readingraphics.com/book-summary-the-black-swan/
A true future predicting algorithm should be aware of its existence and the affect its prediction will have over the receiver / receivers.
Hari Selden had a pretty developed concept what people can know about an algorithm that anticipates their behavior.
"Violence is the last refuge of the incompetent." - not actually Hari Seldon
Earlier quoted context omitted.
How exactly?
Excerpted summary from here [0] Difficulties of Prediction Ultimately, our world and future are unknowable and unpredictable because of various factors, including: • The role of chance and luck in inventions and game-changing discoveries • The inherently unpredictable nature of dynamical systems and variables that behave in inconsistent or nonlinear ways • We can’t make accurate predictions with flawed and incomplete…
People make predictions because they judge that it's better to have bad predictions than none at all. The probable error in prediction (e.g. the "cone of uncertainty" that becomes wider and wider as you go into the future in the simplest, best-performing econometric models) is somewhat undersold, but the general understanding is that this is still a Pareto improvement.
Bicycle helmets are also no good if you get hit by a truck.
There's an old saying that goes something like there are countless way to fail, but only a few ways to succeed. Perhaps there's more complexity in modeling failure?