Re: Serial Number For I Mind Map 8 Serial Number Generator
Agathe Thies
Neurons fire depending on the impulses they get from other neurons. This seems to be 'deterministic'. However, sometimes it might be useful to use random processes instead. Does the human brain have built-in `random number generators' which influence its output?
Comparing this with a computer: when I make an algorithm, the computer will do exactly what it is told. But my computer also has a built-in hardware random number generator. I can use these random numbers for, say, a Monte Carlo simulation. Therefore, the computer is a combination of a `deterministic' machine and random number generators.
Finally, it seems that the only 'true' random number generators are quantum random number generators. However, a hardware number generator such as the one in my laptop is based on a chaotic system. Therefore, a very small change of a very small detail could already give a different result. Now there might be some random quantum process hidden somewhere in my laptop; the very small influence this has is then amplified by the chaotic system. Could this also happen in the brain?
Are you aware of, or have you devised, any practical, simple-to-learn "in-head" algorithms that let humans generate (somewhat "true") random numbers? By "in-head" I mean.. preferably without any external tools or devices. Also, a high output (many random numbers per minute) is desirable.
Check out this article on Geomancy. Specifically the section on generating Geomantic charts. It involves a pseudo-random number generating technique using binary digits and some simple recursive calculation. It seems like you could do this in your head fairly easily (though a piece of paper would help).
Then you have to select a fairly random part of it eliminating unconscious bias as much as possible. A way to do this for example would be to select a song, pick a number k between 10 and 20 and then find the kth letter in its lyrics.
Update: By the way, when people are asked to write a random sequence of say coin tosses, the most common mistake by far is to make your sequence "too random": runs of identical results will be too short, which a simple run length analysis will reveal. This method is mainly aimed at avoiding this trap. Of course other anomalies might arise from the shadow of this run length bias, but you'd need proper experiments to find them. Somewhat ironically, an algorithm for generating random numbers by thinking alone cannot be found by thinking alone.
The simplest pseudorandom number generator would be something like Dilbert's famous "9.. 9.. 9.." algorithm. But intuitively it doesn't seem very good (which of course is the joke). Statisticians have developed a whole host of tests to say whether a sequence of purportedly random outputs are "good". Start with the wikipedia page for "chi squared test" and you could spend an afternoon just reading about these tests.
The next step up in "goodness" is "cryptographically strong randomness" which means that given a sequence a1, a2, ... you cannot predict the next number in the sequence with "reasonable probability" unless you use a lot of computation. These numbers are sometimes called "computationally pseudorandom." One common way to obtain such a sequence is via a "hash chain" like this: a1 = SHA512(a2), a2=SHA512(a3), ... Since we believe (based on experience, not mathematical proof) that SHA512 is computationally hard-to-invert, we believe that a2 is "impossible" to predict given just a1.
Fortunately portable random number generators - including coins for the flopping, dice (with various numbers of facets) for rolling, cards for the picking and straws for drawing - are relatively easy to obtain at low cost. Moreover, for the technophiles amongst us, there are some rather good simulations of these tools available for most mobile platforms.
While it's not a mechanism for generating random numbers in your head, the Solitaire algorithm (as portrayed in Neal Stephenson's Cryptonomicon) demonstrates how difficult it is to use random numbers for cryptographic purposes. It requires only a pack of ordinary playing cards to create a reasonably secure output but the method to do is quite intricate.
A security conscious friend of mine was attempting to generate entropy using random dice rolls to generate a random password, and I became curious about the security of random number generators and whether or not she could do the same by simply thinking of random dice rolls. Would it compromise the security of password/key generation to use human-generated random numbers instead of random numbers from a dice or a secure random number generator?
I can see how the standard random number generators included in many programming languages might not be cryptographically secure in known and exploitable ways, but how about the random numbers generated by the brain? When I asked her, she stated that humans were terrible at generating entropy, but I'm not sure that this is the case. How do humans rank in the generation of key entropy and would it be possible for a human mind to take the place of random number generator for key/password generation without being exploitable?
The use of quantum-based RNGs, so-called true random number generators (tRNGs), as an optimal source of randomness has become the standard in micro-PK research (e.g., Jahn et al., 1980). In meta-analyses of 100s of studies performed using tRNGs, a small but significant effect of the human mind on non-random deviations from chance was found (Bösch et al., 2006; see also Radin and Nelson, 1989, 2003). Despite this, micro-PK is not a generally accepted phenomenon in science. This is because meta-analysis has several flaws, including the ability to be biased by the inclusion of successful studies. In addition, high-powered attempts to replicate positive micro-PK tests have not been successful (e.g., Jahn et al., 2000; Maier and Dechamps, in press). The goal of this study was to conduct a decisive scientific test for micro-PK. A large-scale assessment was performed that used Bayesian techniques to consolidate data until clear evidence for or against the existence of micro-PK was found.
Next the observer effects on picture selection will be reported. The data were analyzed on average every week by the experimenter, the second author, depending on the number of participants tested during the preceding days (see Bayesian approach described above).
I was wondering if anyone had a better solution.
I did look at some similar questions but they have details about random number generation generally. I'm looking for a solution to this specific type of random number generation, either an algorithm or a library that provides it.
Generate a Random number, say x, between [0,n) and then generate another Random floating point number, say y, between [0,1]. Then raise x to the power of y and use floor function, you'll get your number.
odds are good that smaller element will be taken.
I dont' know exactly what do you need. You can also define your own distribution or maybe use some random number generation libraries. But suggested approach is simple and easy to configure.
UPDATE2: You don't have to generate array explicitly. For array of size 1000, you can generate random number from interval: [0,1000000] and then configure your own distribution of selected values: say, intervals of length 1200 for smaller values (0-500) and intervals of length 800 for larger (500-1000). The main point that this way you can easily configure the probability and you don't have to re-implement random number generator.
Last night I was discussing the nature of reality with a friend and she said there were repeatable and respected experiments where random number generators were apparently manipulated by thought. If this is true my concept of reality is significantly challenged.
Are there are any pseudo-random number generators that are easy enough to do with mental arithmetic, or mental arithmetic plus counting on your fingers. Obviously this limits to fairly simple math - it needs to be something someone of average mathematical ability can do, or maybe average ability for a programmer, not a math prodigy.
If the only way to do this is by limiting the range, like maybe it only can output 8 bit numbers, that is fine. I suspect one of the standard PRNG algorithms would be simple enough in an 8 bit version, but I don't know enough to simplify any of them from the 32 bit version to an 8 bit version. (All the ones I looked at depend on specially picked seed numbers that are different depending how many bits you are working with, and usually only 32 and 64 bit examples are given.)
How about Blum Blum Shub, but with prime numbers too small for secure use? Used securely it's slow, but it involves operations that we're used to dealing with, so you might be able to get to a manageable speed without too much practice, maybe with M = 437 or moderately bigger.
A comment points out that this is wrong. Months later I still haven't found the time to revisit how I came up with the magic numbers and where I went wrong, so I'm adding this note at the top in the interim.
Yes I know of one that can possibly be done in your head , and if modified further can result in truly random numbers take a list of numbers , an ordered list of numbers in base ten cause that would be the easiest to calculate in. Add them up together , the keep only the ones digit place number of that resulting number and then place that on the end of the list and drop off the first digit , and then repeat , this will not produce true random numbers but random enough and depending on the size of the list of numbers that you choose to use , will eventually repeat but for a large initial list will not repeat for a sufficiently large amount of time.
for example if I used just 5 numbers in a list 12345 then the next list would be 2345 and the rightmost digit of 1+2+3+4+5ie 15 or 5 so the list would be 23455 now the one has dropped off and is not used anymore so the next sum adds up to 20 -1 (15+5 minus the one that dropped off) so the next list would be 34559 then 45596 then 55969 then 59694 now here we stop , because we have generated a full seeds worth of digits so initially we had 12345.
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