• Forum has been upgraded, all links, images, etc are as they were. Please see Official Announcements for more information

Network Analytics & Business Intelligence

Would you vote yes?

  • Yes

  • No

  • Abstain

  • Need more information


Results are only viewable after voting.

DashNexus

New member
OVERVIEW
While the Dash DAO is a unique and radically transparent governing body, there has been very limited data-driven analysis performed to evaluate its function.

We would like to provide the Dash community with a deeper look into the inner workings of the Dash Governance and Budgeting System by thoroughly analyzing masternode voting patterns, proposal attributes, and many other network parameters to gain additional insight into how the treasury system behaves and how this behavior changes over time.

This information will provide value for the MNOs - by giving them a much deeper understanding of how their governance body functions, while also being incredibly useful for those submitting proposals - by providing them with analytics that will help understand the network’s needs and thus structure proposals that are more likely to deliver value to the network and thus pass with ease.


DELIVERABLES
Our work will consist of two primary functions:
  1. Data Collection - ongoing development of a comprehensive governance database containing proposal, votes, categories, pricing information, and additional info and insights.

  2. Build-out of user-facing website
    • Easily digestible visualization of the collected data, updated live.
    • Public access to API, containing the raw data.

PRELIMINARY FINDINGS
As MNOs, we have a genuine curiosity about the way the DAO operates and have already begun collecting and analyzing network data. While limited by our ‘bootstrap’ budget, we have been able to develop a basic database and extract some interesting insights. The following section provides a few examples of our initial
findings.



DATA EXAMPLES
  1. Here is a basic stacked bar graph showing what day of the month the votes came in for the January 2018 budget period. It gives Proposers an idea of when MNOs are most active. Additional insight can be gained by looking at how this distribution has changed over time across budget periods, as well as further examining anomalies, such as the vote spike in the middle of the month.
    4FwELDg-Napv0SYC00WgTFfor_PWiIgnpiXjmU5zX68vH_damF-teFVwPKIttTJQ75AOGQL9KvH18r13DwdDAPyxuCU1VOLnDymt2VjsPlszM6ublkiYmnYaEVaVrLenxopkeSzl
  2. The following graphs gives additional insight into masternode activity, by showing what time of day/day of week votes come in on (shown in PST)
    ALFnVphj1fb5uCfIqlcT7oVppjSp49DFptXCrlD5jaIYghq6WH8BdOWqXEUzi5mQAmaQlI2aUAVjlb22BIDbe9QmMQjRpF8gLsbnIKLIPnV5hyjvbPYoF-lr4NfAPeYzSh89FmGY


  3. This graph shows how many unique masternodes voted in each budget period. Evaluating the changes in MNO engagement over time is a powerful tool in assessing the health of the overall ecosystem.
    -LUkYzbrWSH3FSgPhuynr21hOIsesKEoBNBpVRyV5rLgKtfhbV3z_X7ESh3eNXaaceKvx6SxZ0fzfdfIC9CGs0EPceaZ2mYsY8kDpk1TD4hLiG4382d2twD1tu2DLn2AiN4v01xh
4. This graph provides some additional insight into MNO voting patterns, by showing how individual MNOs vote. From this graph, we see that in this budget period, 502 MNOs voted YES with 100% of their votes, while 65 MNOs voted NO on 100% of theirs, with a somewhat standard distribution for the rest.
muVS8Q2ClOYVHM4fg82t3qnOQKV-KMQ6TxUAVew0vf_9jxxqSBAKuXlLIaly0G5MNxqVs2pGpmFHaJ3kVmCFrrHk5eJ5qCcoS1NlYkBFBp2GS8O2cY5aM9s4qErK3cEviTvQEn-l



5. Our preliminary research shows that we can find Masternode Groupings based on whether or not votes have the same value and are cast at the same time. We make the assumption that each cluster is a single actor with multiple nodes using the Dash Masternode Tool or something similar to cast all the votes simultaneously.

Ryhdc8Tw-_TbowkjprMT9b_jcgig7_nXAYygiLiFZJ4KwjW2m80xX9NDZYj1oQ0Q-eWF_4IHl-IhD6m0ImjSZ6uZanQO1GpX2yMAnFjfUVlTsDptBK9QtAlM743VMXx3rTfBs_Sh


However, there are some tools that provide vote-blinding methods by taking multiple votes and spreading them out randomly over time. To address this, we will be looking into the correlation between MN votes without looking at the vote timing. For example if nodes A, B, C always vote the same way across a range of proposals - we can assume that they belong to the same owner or the same group.

Our initial research appears to indicate that the Masternode Groupings have gotten smaller over time, indicating that the network is becoming more decentralized. This is a very positive indicator for the health of the network and we are excited to complete our research and provide the community with the entirety of our findings.


FURTHER DEVELOPMENT
Our objective is to use the additional funding from the Dash Treasury to accelerate the gathering and analysis of data, as well as making our research publically available to the network. More specifically, we would like to further explore the following areas:

  • Network Analysis - we will seek to find insights on how individual nodes operate, how the network behaves as a whole, and how consensus is reached. These are core components to understanding the behavior of any network, from an ant colony to an election. Our research will begin with the examination of the following:
    • Looking at voting patterns to determine how nodes influence each other
    • Identification of nodes that act as “opinion leaders"
    • Identifying tipping points during a voting process where the outcome can be predicted.
  • Sentiment Analysis - using tools like Watson Tone Analyzer to gauge comment sentiment on proposals and pre-proposals will give us further insight into how the MNOs and the broader community react to various types of proposals. This research will be focused on determining:
    • Who are the most vocal/influential members of the community?
    • How does the community react to various categories of proposals, such as development, marketing, and integration?
    • Which proposals are the most contentious?
    • Examining the patterns of ‘troll behavior’ and the community response
  • Statistical Analysis - we will use a regression based models to test our hypothesis and determine which factors are statistically significant, which will be particularly useful for:
    • Examining correlation vs causation relationships
    • Identifying outliers that require further qualitative examination
    • Mathematically validating or discrediting our hypotheses

COMMUNITY INVOLVEMENT
We see tremendous value in making our work publically available to the community and would like to encourage collaboration with all its members.

We will create an additional discussion board on the DashNexus.com portal, where we would like to engage with the community by answering questions regarding our data and analysis, as well as taking requests for further research.


BUDGET
iWp5TWgj3uC1Lsv5K4LswKyEhbjfcVPYR9RYwd99S4yYQ62JuvB3_ME_rpENqsuMeIX9KBd-aPq2KBq2uzxDv0dUV24Nq9V745trA_7kcGZQii0niVNl8m-sswQY496IAsuKykCT


Using a 30 day moving average price of $510 USD/DASH, the total comes to 50 DASH/month.

* Any increases in Dash price will be used towards further development, including bringing on board additional resources and extending the duration of our services beyond the initial 3 month period.
 
Last edited:
Be sure to clearly define milestones and set up escrow through Dash Core, GreenCandle, InstantKarmaFund, or DemoIncubator.
 
I have met personally with Jeff and Yuri in LA - they are super clever guys and this is just a small sample of what they have done. Very useful insight for anyone using DAO to fund their project or for Masternodes to get a quantitative view of the DAO ecosystem. Easy yes from me.
 
After some constructive feedback from community members we've updated the scope of our project and will be going live later today.
 
To be honest, I could see this being more useful for Proposers than MNOs, per se, but it would definitely be interesting to see at the very least. So suppose we pass this proposal and have all of this raw data, what kinds of projected uses do you see coming from this data? What effect do you think it would have on proposals going forward, on MNO activity, on the DAO as a whole? I have no doubt this is within your capabilities and your budget is not unreasonable, but I'm just wondering what do you intend to do or what do you think others will do with this data? What is its pragmatic value to the DAO as a whole and MNOs specifically going forward? Could you elaborate on that?
 
To be honest, I could see this being more useful for Proposers than MNOs, per se, but it would definitely be interesting to see at the very least. So suppose we pass this proposal and have all of this raw data, what kinds of projected uses do you see coming from this data? What effect do you think it would have on proposals going forward, on MNO activity, on the DAO as a whole? I have no doubt this is within your capabilities and your budget is not unreasonable, but I'm just wondering what do you intend to do or what do you think others will do with this data? What is its pragmatic value to the DAO as a whole and MNOs specifically going forward? Could you elaborate on that?

I think you're right in that it may be more helpful for Proposal Owners than MNOs but I think really anyone looking at Dash from an analytical or critical perspective is going to be interested in the kind of information we'll be presenting.

MNOs would probably like to see our category chart which shows how DASH was distributed across proposal categories.

There's something for everyone in here and I think that it will help inform some decisions going forward from MNOs on where they choose to spend DASH in the future and what kinds of projects that they want to support, or potentially see under-represented in the treasury. That's just one example though, we've also got a work-in-progress study connecting dollar values to proposal payouts which is going to give us a number we can throw around that tells people just much USD the DAO has distributed over the years and that we can then use to show growth.

We plan on making most of this data available through our APIs so that other Dash-focused sites can integrate this information as well.


Always open to answering more questions @Arthyron and I'm on Discord too as @jeffh if you want to follow-up there.
 
Our initial research appears to indicate that the Masternode Groupings have gotten smaller over time, indicating that the network is becoming more decentralized. This is a very positive indicator for the health of the network and we are excited to complete our research and provide the community with the entirety of our findings..

I came to the same conclusion.
Distribution of masternodes according the voting hash.
(see here for explanation)

Code:
curl -s https://demodun.github.io/mnowatch/the_results_dashd_24-02-2018.html| cut -f22 -d"<"|cut -f2 -d">"|grep -v [a-z]|grep -v [A-Z]| grep ^[0-9]|grep -v "-"|sort|uniq -c|sed -e s/'^   '/000/g|sed -s s/'000    '/000000/g|sed -e s/'000  '/00000/g|sed -s s/'000 '/0000/g|sort -r|cut -f1 -d" "|uniq -c
1 0002221
1 0000062
1 0000053
1 0000041
1 0000040
2 0000035
1 0000027
1 0000023
1 0000018
3 0000017
1 0000014
1 0000013
1 0000012
2 0000011
2 0000010
2 0000009
5 0000008
3 0000007
8 0000006
12 0000005
12 0000004
22 0000003
63 0000002
1647 0000001
There is a huge increase of unique masternodes (from 645 to 1647!)
Either it is true, or they adapted to my code in order to hide themselves.
Obviously the votehash is not enough, in order to spot the operators having multiple masternodes. The R&D continues.
(dandelion?)

P.S.
You may also be interested in some other queries [1] , [2] that you can execute in the provided html data.
 
Last edited:
However, there are some tools that provide vote-blinding methods by taking multiple votes and spreading them out randomly over time. To address this, we will be looking into the correlation between MN votes without looking at the vote timing. For example if nodes A, B, C always vote the same way across a range of proposals - we can assume that they belong to the same owner or the same group.

Could you please explain this further?
I use vote hash.
Yes of course it changes, but if you sort the table by votehash, you could see what masternodes have identical votehash.
The more the votes, the more the possibility these masternodes to be operated by the same person.
Thus the person and the number of masternodes he operates, is identified.
Will you use votehash too, or something better than that? and what is it?

My goal is to spot the individuals that are masternode operators, by analyzing the way they vote.
So my main question is this: will you also try to spot these individuals?
 
Last edited:
Back
Top