6 Jun, 32 tweets, 6 min read
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.
3/ So, the LHS 30%-vs-70% split tells us that in #Avalanche a transaction needs the approval of at least of those 91 mega-validators to get processed.
4/ The RHS 70%-vs-30% split tells us that 922 validators control 70%, and the remaining 944 - 922 = 22 mega-validators control 30% of the stakes.
5/ Why is this RHS 70%-vs-30% split important? Well, it means that in #Avalanche these *22* mega-validators can *censor* your transaction, because in each voting round of the consensus algorithm the quorum needs to be larger than 70%.
6/ So summarized: In #Avalanche a TX is required to have the approval of the largest *91* mega-validators to pass, and out of these *22* monster-validators can reject your TX and censor it.
7/ Let's continue our analysis: Actually, it is possible to *group* validators by their _reward addresses_ and then repeat the same analysis we've done above. Here is the GINI graph for those validators groups:
8/ As observable in #Avalanche, there a) are 884 such validator *groups* [by their _reward address_], b) the TVL is obviously still 213.0M \$AVAX, and c) GINI is higher with 89.6%, due to even higher stake concentration.
9/ As this graph shows, in #Avalanche 884 - 832 = *52* mega-validator (groups) control 70% of the stake, and 884 - 875 = *9* monster-validator (groups) control 30% of the stake.
10/ This implies that in #Avalanche a TX needs the approval of the largest *52* mega-validator (groups), and a rejection by the largest *9* monster-validator (groups) leads to a censoring of a TX.
11/ So, we have successfully put an *upper bound* regarding the (de)centralization of the #Avalanche network: At most *9* entities control whether a TX gets processed or not! It's up to our followers to decide for themselves if such a setting is decentralized or not.
I call these largest 9 monster-validator (groups) the "Censors". Their P-chain addresses & node IDs are (in descending) order:
Node IDs: KNLkh3KVKFFhBWujmcZ5P3p2fJc3BbdNA,
NkYLNRp4S6exbWamVvMzUUpXvHeVEzLR6,
LvKMfPzfWT1VfAPLef6denZ8hTwSAFMGY,
TgaMGAEpkXKAisumnnmzzRzVkexbSkB7,
EEQ4pJQ8FTsFmPi5qEiZDBkKsqCpBRZiT,
9zPtXnScuWRvoiTDe498ZtjgoTXwTwxr9,
4o17gdHmyD1dWGxwez5KqKdyLDfiYmPX9
NodeIDs:5s9gCg2xSYZQEyw6Dp8ShsQom1W8SXnsc
H8eZ2xwjXoFUoo5kLCF7rFgMEGaqfVfQS
7AnwdDA9QLTgTwzKoFL7j9wsBvourjD3S
LSoyHgtuX8UGyfz5ydHePK1aXXpSEakUZ
4gTwepTF5fcacXB7gdYZLTtfSFsYh4faj
Ns1eDN3K9HTnavgiBQhtpzRxUCxJ55Xhc
MXNBsr8xSHE9CKfmyzvBYyEzxAbLuHTAn
6hAyZe6HS7qKuSuZthSCXeRAMyMPhkouU
NodeIDs: 6kDsBHKX9q4veBHoCjyHh94CyLNobXiY4
FVgkUobMuUBABuMA2KngEsm2SG9XLGvdW
3DwhPYMEQABuocceWDAZEpi8GMcLyvTYy
NodeIDs: 2JZva9683sZuXueZvphJfBnZLd9JTmy13
Fg3L1eo53UWpf3J9Y45PK2ksQ1zyqajHL
NodeIDs: HpMSfYT2ox1vkr1hFNMwmdWuq8QWhvH2u
N2t1CAS75972obgtRPHwVanMnRF1rRo1B
NodeIDs: Pu5SGCYS3hxAeqCtmuP82mFVTDQAmzWeF
Nz93c8UB78eEVVtfxpcecxHmiy2gZ4iAi
NodeIDs: AWPFmXs1VyVmGod6eg14ZC67QZafBN8BZ
9jFyex1HDP581kQufBBjGuNARQEQ9nJEx
NodeIDs: C3aMR9tsKqTNQra8FbqpFkMgHV5DtuJGx
NodeIDs: EZ38CcWHoSyoEfAkDN9zaieJ5Yq64YePY

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# More from @notexeditor

4 Jun
State of #Avalanche: a) number of validators at 965, 🥳 b) TVL at 220.5M \$AVAX, 🤑 c) GINI at 84.6%:
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.
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.
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).
27 May
State of #Avalanche: a) number of validators at 982, 😎 b) TVL at 258.7M \$AVAX, 🤑 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: