Āut Labs
  • Āut Labs
  • The Āutonomy Matrix
  • $AUT Token
  • Framework Intro & Components
    • Āutonomy Matrix
    • 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
      • Discrete CL Allocation
    • Peer Value
      • Flow & aggregation of value
  • 🕹️Participation Score
    • Design Thinking
      • Problems with traditional Local Reputation parameters
      • Innovation Compared to other “Local Reputation” protocols
      • Hub<>Participant Accountability & Rewards
    • Core Parameters
    • Formulæ
    • Edge Cases
      • 1. The Private Island
      • 2. Cannibal Members
      • 3. The Ghost & the House on Fire
    • 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

Conclusions & Initial Applications

The parametric, weight-based approach to υ\upsilonυ makes the general formula for υ\upsilonυ infinitely expandable - allowing the addition of more parameters after the 3 original ones (variety, betweenness and centrality), based on free market dynamics.

Initially, at the onboarding Period ( T0T_{0}T0​), each ĀID will receive a default weight of 0.3‾0.\overline30.3 for each parameter. Later (from Period 1, P1P_{1}P1​, ahead), they will be able to distribute a custom weight to each of the 3 parameters at will, till reaching the total of 1.

By calculating the Peer Value of a Participant in the context of each Hub they participate in, we can create a rich, multi-dimensional view of their overall impact within the ecosystem - and a nuanced representation of the relationships between contributors and the communities they participate in.

In this directed graph-based model:

  • each Hub h⋅\boxed {h \cdot}h⋅​ can be represented as a Node

  • each contributor (j) can be represented as an Edge

  • each action/task/interaction completed by j in any of their Hubs is represented as a Link

Incorporating a directed graph-based approach to Peer Value / Global Reputation within the Āutonomy Matrix offers a powerful and granular way to model and visually represent the complex relationships and inter(in)dependencies within the decentralized ecosystem.

To implement this directed graph-based approach in the Āutonomy Matrix, we create and maintain a graph that captures the relationships between contributors and Hubs, as well as the relevant metrics ( PS and PPS \text{ and }\mathfrak PPS and P) for each Node, Edge and Link. The υ\upsilonυ scores can then easily be calculated and updated in real-time as contributors participate in different communities / contribute to different projects, while in parallel the Prestige of those Hubs evolves.

By providing a contextual, multi-dimensional view of contributor reputation, this approach can help foster greater trust, collaboration, and value creation across the ecosystem.

With or without us. Within or beyond our lifetime :)

Conclusions

A global, self-sovereign reputation (= not assigned by others, but gained through one's own actions and weighted through a meritocratic, math-based system) can unlock incredible things, and bring human society 10 steps forward.

Things such as profit-sharing, credit score, mutual credit, UBI, workers' comp (in case of injury), retirement plans etc. would be as easy as multiplying every contributor's/participant's premium for their υ\upsilonυ coefficient.

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Last updated 10 months ago

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