If they did a transformation based on a singleton target distribution, it would be PPS. What they are doing is something in between.
Can you expand on this a little?
I'm assuming their methodology is as follows (and the fact they misreport block finding times suggests this may not be their methodology):
1. When a block is found, determine X, the number of shares since the last block (in multiples of the difficulty).
2. Report there were f(X)*D shares in the last round, and pay (50 BTC / (D*f(X))) per each share since the last block, for a total of (50BTC * X / (D*f(X))).
3. f is chosen so that f(X) will follow a specific distribution with CDF F2(X).
X follows the exponential distribution so the real CDF is F1(X) = 1 - exp(-X). To have a CDF F2 for the reported lengths, the transformation they need is f(X) = F2^{-1}(F1(X)).
If they take F2(X)=F1(X), then f(X) = X and they are not doing any transformation. This results in normal proportional payments.
If they make it so f(X) is always 1 (corresponding to number of shares per round precisely equal to the difficulty) - that is, the target distribution has no variance, it's a "random" variable that always takes the same value - they will always pay (50 BTC / D) per share, that is, they are handing out PPS payments which has high variance for them, and no variance for miners.
If they make the target distribution something in between, with variance less than exponential but more than a singleton, they will have some variance, but less than PPS (and for miners, less than exponential). But this is moot unless they choose the distribution so that E[X/f(X)] = 1 (the expectation is over the real exponential distribution of X) so that they pay 50 BTC per block on average.
Edit:What method did you use to calculate Beta? I ended up going with a brute force algorithm which was quick to write but not to run. Is there a better way - some regression technique maybe?
There are several ways to go about it but they all require a good numerical root-finding/optimization function (which should take less than 10 iterations to converge). I copped out by simply fitting your reported mean and variance exactly. Expressing alpha in terms of beta is easy from one equation, and then you solve the second equation numerically for beta. A more accurate result can be obtained by taking some statistics (such as moments or quantiles) and building a loss function for any given alpha/beta combination based on their difference from the real values of the distribution, with a weight that depends on each statistic's estimate variance. Then it's just a matter of numerically minimizing the loss function over alpha and beta.
The best method would be Bayesian inference, but if we don't want to commit to any particular prior, the Maximum Likelihood Estimator is the next best thing. For any given alpha and beta, calculate the logarithm of the pdf at each datapoint, and add. Maximize this function with respect to alpha and beta, again with whatever numerical optimization function you have available.
PS if you can give me the complete list of values of round length divided by difficulty, I can try to run an MLE.
I'm not that much interested in these politics
Politics or consumer advocacy? I haven't mined since October so I don't have a vested interest anymore. But dishonesty is bad for bitcoin - even if it was originally done with good intentions.
If it involves pointing fingers, it's politics in the wide sense of the word, it being a noble pursuit notwithstanding.