More on Human Randomness

In a post a few months ago I asked whether there is a way for a human to reliably generate truly random numbers. I got a lot of great responses and I think it’s worth summarizing them here!

Randomness in poker strategies

Robert Anderson noted that poker players sometimes use the second hand of a watch to introduce some randomness into their strategy. I assumed this would be something like getting a random bit based on whether the number of seconds is even or odd, but Pete McAllister chimed in to say that it is usually something more like dividing a minute into chunks, and making a decision based on which chunk the current second is in. For example, if you want to make one choice 20 percent of the time and another choice 80 percent of the time, you could just make the first choice if the second hand is between 0–12 seconds, and the other choice otherwise.

In game theory this is called a “mixed” strategy, and this kind of strategy can arise naturally as the Nash equilibrium of certain kinds of games, so it’s not surprising to me that it would show up in high-level poker. I found conflicting advice about this online; some people were claiming that you should not use randomness when playing poker, but I did find a website that talked about implementing this kind of mixed strategy using the second hand of a watch, and it seemed to be a website with pretty high-level poker advice.

In any case, if you have a phone or a watch with you, this does suggest some strategies for generating random numbers: for example, look at the last digit of the seconds to get a random number from 0–9, or whether it is even or odd to get a bit. Or you could just take the number of seconds directly as a random number between 0–59. Of course this only works once and then you have to wait a while before you can do it again. Also, it turns out that my phone doesn’t show seconds by default. Taking the ones digit of the minutes as a random number from 0–9 should work too, but the tens digit of the minutes seems like it’s “not random enough”, in the sense that it might be correlated with whatever it is that I’m doing.

Of course, a phone or watch counts as an “aid”, but most people tend to carry around something like this all the time, so it’s relatively practical. On the other hand, if you’re going to use a phone anyway, you should just use an app for generating random numbers.

Bodies and clothing

  • Naren Sundar commented that hair is pretty random, but admitted that it would be hard to measure.

  • Frederik suggested spitting, or throwing your shoe in the air and seeing which way the toe points when it lands. I like the shoe idea, but on the other hand it’s somewhat obtrusive to take your shoe off and throw it in the air every time you want a random bit! And what if you’re not wearing shoes? I’m also afraid I might throw my shoe in the same way every time; I’m not sure how random it would be in practice.

Minds and memorization

Kaligule suggested taking whatever song is currently running through your head, stopping it at a random point, and getting a random bit by seeing whether the number of consonants in the next word is even or odd.

This is a cool idea, and is the only proposal that really meets my criterion of generating randomness “without aids”. I think for some people it could work quite well. “Stopping at a random point” is somewhat problematic—you might be biased to stop at certain points more than others—but it’s pretty hard to know how many consonants are in a word before you count, so I’m not sure this would really bias the results that much.

Unfortunately, however, it won’t work for me because, although I do always have some kind of music running through my head, it often has no lyrics! Kaligule suggested using whether the melody goes up or down, but this is obvious (unlike number of consonants in a word) and too easy to “cheat”, i.e. pick a stopping point that gives me the bit I “want”.

This suggested another idea to me, however: just pre-generate some random data and put some up-front effort into memorizing it. Whenever you need some randomness, use the next part of the random sequence you memorized. When you use it up, generate another and memorize that instead. This leaves a number of questions:

  • How do you reliably keep track of where you are in the sequence? I don’t actually have a good answer to this. I think in practice I would get confused and forget whether I had already used a certain part or not. Though maybe this doesn’t really matter that much.

  • What format would be most effective, and how do you go about memorizing it? Some ideas:

    • My first idea is to generate a sequence of random bits, and then write a story where sequential words have even or odd numbers of letters corresponding to the bits in your sequence. Unfortunately, this seems like a relatively inefficient way to memorize data, but writing a story that corresponds to a given sequence of bits does sound like a fun exercise in constrained writing.

    • Alternatively, one could simply generate a random sequence of digits (or hexadecimal digits) and memorize them using whatever sort of memorization technique you like (e.g. a memory palace). This is less fun but probably more effective. Memorizing a story sounds like it would be easier, but I don’t think it is, especially since you would have to memorize it word-for-word and you only get one bit per word memorized, as opposed to something like e.g. four bits per hexadecimal digit.

I have generated some random hexadecimal digits but haven’t gotten around to trying to memorize them yet. If I do I will definitely report on the experience. In the meantime, I’m also open to more ideas!

About Brent

Associate Professor of Computer Science at Hendrix College. Functional programmer, mathematician, teacher, pianist, follower of Jesus.
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2 Responses to More on Human Randomness

  1. Curtis says:

    As another strategy, if we assume that there’s some true randomness to how humans choose bounded numbers (even if it isn’t uniform) and that a sequence of these random numbers are independently distributed, then we can use the central limit theorem to get a better distribution of numbers.

    The procedure works like this: to get a number which is approximately uniform in \{0, 1, \dots n-1\}, generate a (relatively long) sequence of “random” numbers in \{0, 1, \dots n-1\}, and then compute their sum mod n. According to this answer on the Mathematics StackExchange ( ), the result of this process should approach the uniform distribution. From experimental evidence, this appears to be true.

    This relies on the assumption that each number in the sequence is chosen independently of all others, which may not be true. Another drawback is that it could be easy to cheat if you’re good at tracking partial sums mod n. For n=2, this might be really easy for some people.

    • Curtis says:

      This may also be a hard calculation to do without paper, since you have to remember either the entire sequence of numbers or their current sum. Remembering the sum seems more reasonable, but then it’s much easier to cheat.

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