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  • Framework Intro & Components
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    • The Participation Score
      • More about Expected Contributions
    • ĀutID: a Member< >Hub bond
    • Interactions, Tasks & Contributions - a context-agnostic standard.
    • Contribution Points
      • Calculating eCP and other dependent & independent params
    • The Hub - or, the whole is greater than the sum of its parts.
    • Roles on-chain. If there is Hope, it lies in the Roles
    • Commitment Level as an RWA
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      • Flow & aggregation of value
  • 🕹️Participation Score
    • Design Thinking
      • Problems with traditional Local Reputation parameters
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      • 1. The Private Island
      • 2. Cannibal Members
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    • PS Formula for all Edge Cases
    • Conclusions
  • 🎇Prestige
    • Prestige: introducing measurable credibility for a DAO
    • Need for a DAO to measure its KPIs overtime (on-chain)
    • Archetypes
      • Defining an Organizational Type
      • Existing Organizational Types
      • Deep-dive: Calculating current Parameters (p)
    • Formulas for Prestige
      • Normalization of p
    • Prestige for all edge cases
      • Relationship between Prestige & Archetype parameters
    • How to expand Prestige through external Data Sources
    • Use-cases & Conclusions
  • 🌎Peer Value
    • Initial Applications
    • Relationship between Participant, Hubs & Peer Value
    • Peer Value (v) as a directed graph
      • Calculating normalized Participation Score (PS'')
      • Calculating normalized Prestige (P'')
      • Calculating the Contributor Archetype (a)
    • The Peer archetype
      • Formulæ for α & deep-dives
      • Formulæ for β & deep-dives
      • Formulæ for γ & deep-dives
    • Conclusions & Initial Applications
  • ⚽Appendices & Playgrounds
    • PS Simulations
    • PS Playground
    • Prestige Simulations
    • Prestige Playground
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  1. Peer Value
  2. The Peer archetype

Formulæ for β & deep-dives

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Last updated 8 months ago

2. Betweenness (β)

In practical terms, the Betweenness of an individual in the Web3 ecosystem represents how well connected they are, and how "central" an individual is for their connections. It's based on a simple, progressive score, that quantifies this "social score" based on how close everyone is to all the others they are connected to.

It's simply calculated by using this formula:

βj=∑(C,w)=1N=4(wN×CN)×100CNβ_j = \displaystyle \sum_{(C, w) = 1}^{N = 4} (w_{\tiny N} \times C_{\tiny N}) \times \frac {100} {C_{\tiny N}}βj​=(C,w)=1∑N=4​(wN​×CN​)×CN​100​

Where:

  • β\betaβ is the Betweenness of j, based on the amount of connections they have with their peers, weighed by the Proximity Level of those connections

  • N is the number of Proximity Levels, in this case, 4:

    • C1,C2,C3,C4C_{\tiny 1}, C_{\tiny 2}, C_{\tiny 3}, C_{\tiny 4}C1​,C2​,C3​,C4​ are the number of connections at each Proximity Level (PL)

    • w1,w2,w3,w4w_{\tiny 1}, w_{\tiny 2}, w_{\tiny 3}, w_{\tiny 4}w1​,w2​,w3​,w4​ are the weights corresponding to each Proximity Level,

      • with w1>w2>w3>w4w_{\tiny 1} > w_{\tiny 2} > w_{\tiny 3} > w_{\tiny 4}w1​>w2​>w3​>w4​,

      • and a weight of:

        • w1=1.00w_{\tiny 1} = 1.00w1​=1.00

        • w2=0.75w_{\tiny 2} = 0.75w2​=0.75

        • w3=0.50w_{\tiny 3} = 0.50w3​=0.50

        • w4=0.25w_{\tiny 4} = 0.25w4​=0.25

  • CNC_{N}CN​ is the total number of connections across all levels

Deep-dive: Proximity Levels & Markets

Proximity Levels (PL)

  • PL 1: same Role a Secondary Node is in the same Hub AND in the same Role as the Central Node

  • PL 2: same Hub a Secondary Node is in the same Hub BUT NOT in the same Role as the Central Node

  • PL 3: same Market a Secondary Node is in the same Market BUT NEITHER in the same Hub OR in the same Role as the Central Node

  • PL 4: have an ĀutID a Secondary Node owns an ĀutID BUT NEITHER is in the same Market, in the same Hub OR in the same Role of the Central Node

Markets

  1. 🧑‍💻 Open-Source & Infra

  2. 💲 DeFi & Payments

  3. 🍀 ReFi & Governance

  4. 🎲 Social, Gaming & Art

  5. 🆔 Identity & Reputation

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