Just a few days ago, to my big surprise, Vitalik released a blog post about
a topic critical to the functioning of Rug Pull Index. A blog post titled:
"Against overuse of the Gini
coefficient."

While I'm super interested to learn more about V's thoughts - I haven't had any
time to read it yet. Still, a friend of mine, and also ex-BigchainDBer, Ryan
Henderson, first made me aware of an inaccuracy
in RPI's Gini coefficient calculation for small and/or extreme case populations
as e.g. a population of two with incomes of 0 and 1. He, too, had recently used
the Gini coefficient in a paper on neural
networks.

Originally using Wikipedia's
formula,
for a maximally inequal populalation e.g. of 0 and 1, the Gini coefficient
calculation ended up being $G=\frac{1}{2}$, where as we'd expect it to be $G = 1$.

However, after having some more discussions with Ryan and my friend Jost
Arndt, we settled on a more intuitive derivation
of the Gini coefficient for Rug Pull Index that should also produce reasonable
results for small or extreme populations. For anyone interested, I updated the
/specification
page with the latest formula.