something fishy - rng is not rng

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MasterAxe wrote:

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Xavderion wrote:

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MasterAxe wrote:

This has been pretty obvious to me for quite some time. i can literally go weeks maybe even a month or more and NVER see a Multistrike gem drop, Yet whenever i do see one drop, i always see it drop 2-3 more times through out the day.

You just described RNG. Not sure why people expect evenly distributed drops in a random system.

No, what it is, is horrible randomness, whatever system they use is very very streaky. That being said, im not complaining, just stating its obvious. No random generator is truly accurate, its just the one GGG uses is not even close, LOL.

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iiell wrote:

Can a computer generate a truly random number?
It depends what you mean by random…



“One thing that traditional computer systems aren’t good at is coin flipping,” says Steve Ward, Professor of Computer Science and Engineering at MIT’s Computer Science and Artificial Intelligence Laboratory. “They’re deterministic, which means that if you ask the same question you’ll get the same answer every time. In fact, such machines are specifically and carefully programmed toeliminate randomness in results. They do this by following rules and relying on algorithms when they compute.”

You can program a machine to generate what can be called “random” numbers, but the machine is always at the mercy of its programming. “On a completely deterministic machine you can’t generate anything you could really call a random sequence of numbers,” says Ward, “because the machine is following the same algorithm to generate them. Typically, that means it starts with a common ‘seed’ number and then follows a pattern.” The results may be sufficiently complex to make the pattern difficult to identify, but because it is ruled by a carefully defined and consistently repeated algorithm, the numbers it produces are not truly random. “They are what we call ‘pseudo-random’ numbers,” Ward says.

For most applications, a pseudo-random number is sufficient, he adds. “For example, if you want to do a random sampling of a large set of data, you’ll need numbers to feed into the program so that the samples are more or less evenly distributed. Using pseudo-random numbers is perfectly acceptable in this case because there’s no quantitative advantage in the degree of randomness.” Similarly, a CD player in “random” mode is probably really playing in pseudo-random mode, with a pattern that is discernible if you listen carefully enough.

Not all randomness is pseudo, however, says Ward. There are ways that machines can generate truly random numbers. And the importance of true randomness is not to be underestimated, he adds. “If you go to an online poker site, for example, and you know the algorithm and seed, you can write a program that will predict the cards that are going to be dealt.” Truly random numbers make such reverse engineering impossible, he adds. There are devices that generate numbers that claim to be truly random. They rely on unpredictable processes like thermal or atmospheric noise rather than human-defined patterns. The results might still be slightly biased towards higher numbers or even numbers, but they’re not generated by a deterministic algorithm. (A similar online solution is available at random.org.)—Jason M. Rubin

https://engineering.mit.edu/ask/can-computer-generate-truly-random-number

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johnKeys wrote:

code is code.
and code tends to have bugs.

in my case though, code and RNG are working properly and as intended: I get nothing.

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MasterAxe wrote:

No, what it is, is horrible randomness, whatever system they use is very very streaky. That being said, im not complaining, just stating its obvious. No random generator is truly accurate, its just the one GGG uses is not even close, LOL.

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Gobla wrote:

RNG is streaky by it's very nature. An evenly spread result is the very definition of not-random.

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johnKeys wrote:

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Gobla wrote:

RNG is streaky by it's very nature. An evenly spread result is the very definition of not-random.

true RNG is random by nature.
which means given a set of outcomes of size n, the probability for it to be a streak equals the probability for it to be evenly distributed, equals the probability for it to be anything in between.

tendency to create streaks is a property of a PRNG, and the higher the probability of a streak, compared to other possible outcomes - the bigger sign it is that this PRNG is poorly implemented.