What _possible_ justification could there be for using that as an accounting practice for a fund other than fraud?
How does volume correlate to the value of the insurance fund? Isnt value equal to number of tokens * price per token?
It basically takes the daily volume, multiplies it by 0.00075% (on average) and adds that to the insurance fund. FTX charges a % fee on every trade, so this is the equivalent of taking a fraction of their fee revenue and putting that into an insurance fund, which seems pretty reasonable.
There was an actual insurance fund and it had a tiny fraction (5% or less) of what this number in the database claimed. The random numbers generated by this code did not represent actual contributions to said insurance fund. They were not, in fact, taking a fraction of their fee revenue and putting it into an insurance fund.
They were not doing anything even remotely reasonable, this was a flat-out lie.
It’s still ridiculous to use a normal distribution to calculate how much money should go into an insurance fund that needs to cover sudden, severe losses.
I would normally say the latter is more random, because you're getting more entropy per call. In the same sense, a function drawing from a normal distribution is less random than one drawing from a uniform distribution.
(All of this depends on a computers view of the world, where values are in data types that have some number of bits. In a mathy view of the world where every value is effectively infinitely many bits you get just as much entropy sampling from a normal distribution as a uniform one.)
That's some mathematical nonsense.
A normal distribution is continuous from negative infinity to positive infinity, so even by your flawed judgement of what makes something "random", a sample from the normal distrubtion has far more possibilities.
The standard normal distribution is almost the definition of random.
But in that context, there is more entropy in a float32 drawn from a uniform distribution than a float32 drawn from a normal distribution, no?
It's not just the number of possibilities, it's how likely they are. For example, consider two functions:
A: 1% chance of 1, 1% chance of 2, 98% chance of 3
B: 50% chance of 1, 50% chance of 2
H = - sum over all possibilities of (p * log_2 p)
H_A = -(2 * 0.01 * log_2(0.01) + 0.98 * log_2(0.98))
= 0.16
H_B = -(2 * 0.5 * log_2(0.5))
= 1
For continuous distributions this is defined with an integral instead of a sum, but it's the same idea:
H = - integral from -inf to + inf of (p * log_2 p)
C: uniform distribution from -1000 to +1000
D: normal distribution centered on 0 with standard deviation 10
E: uniform distribution from -1 to 1
H_C = - log_2(2000) / log_2(2) = 10.97
H_D = 1/2 (log_2(2pi * 10^2) + 1) = 5.15
H_E = 1
Afterall, how can U(0,1) be considered to be "less random" than U(0,1000) when U(0,1000) = 1000*U(0,1). A constant factor can't change how "random" something is.
If you look at it from a math ("continuous is actually possible") perspective I have a much weaker handle on it, but I think the problem is that the entropy of 1000U(0,1) is not the same as the entropy of U(0,1)?
Let’s do a lottery together. We each throw in some money. Since they’re both just as random, I’m sure you won’t mind if I use a normal distribution, and choose each of our positions on the number line. :)
Your assertion is missing some needed qualifiers.
If given only mean and standard deviation, the normal distribution is the maximum entropy distribution i.e. maximally random distribution.
The non-zero mean and relatively small standard deviation (0.4 sigma) make all the difference here.
I'd say the ethics and legality are entirely determined by how this distribution was determined and if it's consistent with promises to customers.