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#Avalanche: tax the rich (validators), subsidize the poor (validators). $AVAX! NFA & DYOR
10 Jun
State of #Avalanche: a) number of validators at 967, πŸ₯³ b) TVL at 211.6M $AVAX, πŸ€‘ c) GINI at 85.3%:
State of #Avalanche: a) number of validator *groups* (by reward address) at 909, 😎 b) TVL at 211.5M $AVAX, πŸ€‘ c) GINI at 89.6%:
Read 4 tweets
8 Jun
State of #Avalanche: a) number of validators at 947, 😎 b) TVL at 211.7M $AVAX, πŸ€‘ c) GINI at 85.1%, d) min. number of approvers at 947 - 858 = 89, e) min. number of rejectors at 947 - 925 = 22: Image
..where the GINI inequality coefficient is defined as the sum of total stake difference *over* the sum of max. possible stake difference among validators. It's 0% for total equality (everybody having the same stake) & 100% for total inequality (a single one having all the stake).
..where the *approvers* control 70% of the stake and _need_ to collectively sign-off a TX. It's a minimum number, because one of the mega-validators among the *approvers* might get replaced by two or more smaller validators.
Read 6 tweets
6 Jun
State of #Avalanche: a) number of validators at 944, 😎 b) TVL at 213.0M $AVAX, c) GINI at 84.8%:
1/ My long terms followers will have noticed, that I've added a 2nd vertical bar to the graph. The 1st *left-hand-side* vertical bar is the 30%-vs-70% split, and the 2nd *right-hand-side* vertical bar is the 70%-vs-30% split w.r.t. to stakes.
2/ Why did I do that? Well, the LHS 30%-vs-70% split tells us that 853 out of 944 validators control 30% of the stakes, and the remaining 944 - 853 = 91 validators control 70% of the stakes.
Read 32 tweets
4 Jun
State of #Avalanche: a) number of validators at 965, πŸ₯³ b) TVL at 220.5M $AVAX, πŸ€‘ c) GINI at 84.6%: Image
1/ GINI is a measure for *inequality*: A min. value of 0% would be very good for decentralization, and would imply perfect equality among validator stakes. A max. value of 100% would be very bad for decentralization, and would imply perfect inequality among validator stakes.
2/ So, a GINI of 0% would mean all validators have the same stake, and a GINI of 100% would mean a single validator has all the stake.
Read 14 tweets
30 May
1/ I've been looking to poke holes into #Avalanche for *two* years, writing an entire simulator for 1 million+ nodes in the process. Anything you throw at it, has either been easily solved or is easily solveable.. it's just unbreakeable:
2/ Protective ephemeral centralization by the #Avalanche foundation? That's easy to fix: distribute $AVAX via sales, tax larger validator rewards, subsidize smaller validators or modify staking to voting relationship w/o affecting safety too much.
3/ Liveness suffering due to theoretical fat-tails distributions like Pareto or Cauchy? Easy to fix: apply adaptive staking vote shaping to measure & recognize such distributions in real time. Applt counter measure by dynamic staking power adaptation.
Read 10 tweets
27 May
1/ Why #Avalanche is even better than I initially thought (part 2)?
2/ In our previous thread we discussed how *fast* the distributed #Avalanche *consensus* mechanism can sync all honest nodes:
3/ Above you see how after *only* 3 rounds the entire set of honest participants are in sync: Despite 15% being faulty (or malicious), the system manages to achieve the max. possible consensus level of 85% (for the overall network).
Read 32 tweets
27 May
State of #Avalanche: a) number of validators at 982, 😎 b) TVL at 258.7M $AVAX, πŸ€‘ c) GINI at 85.6%:
WTF: TVL is *down* by 40M $AVAX in the last 7 days? I guess somebody is pumping up validators numbers, and trying to reduce the *apparent* GINI I'm measuring.. 🀣 Note that *real* GINI is by definition _worse_, due to the top validators belonging to the foundation..
1/ Why the socio-economic structure of #Avalanche and #Turkey are similar? Let me explain:
Read 16 tweets
24 May
1/ Why #Avalanche consensus is even more powerful and flexible than I though initially? Check this diagram: Image
2/ Above you see that given a *uniform* distribution of stakes the #Avalanche *Snowflake* consensus reaches after *only* 3 rounds the maximum expected levels!
3/ Let's investigate another distribution of stakes e.g. according to the *Cauchy* distribution: Image
Read 11 tweets