Neural Quorum Governance

Since October 2023, SCF utilizes Neural Quorum Governance to support community-powered award allocation decisions in the Community Vote phase of SCF Build. Designed in collaboration with R&D firm BlockScience, Neural Quorum Governance (NQG) meet SCF's needs for effective, inclusive, flexible, and adaptive governance participation.

This mechanism is modeled after neural networks and contains layered voting neurons that allow members to adjust their voting weights based on their Verified Tier, trust given by peers, and historical contributions to SCF. In addition, voters can delegate their weighted votes to a group (or quorum) of other individuals, to support a lack of availability, expertise on a particular topic, or not having a high enough level to vote actively.

Resources

The Vision for Neural Quorum Governance

NQG introduces a liquid democracy mechanism that combines reputation-driven voting power (Neural Governance) with the flexibility of opt-in delegation (Quorum Delegation). NQG aims to set a new governance standard that prioritizes community alignment, participation, and adaptability, effectively addressing common, hard-to-solve issues experienced amongst voting and governance mechanisms.

Neural Governance and Quorum Delegation have been added as individual modules in the Governance Modules Library (GML) created by BlockScience, a curation of both new and classic templates that can be readily implemented and tuned to specific applications which depend on or require governance solutions.

Stellar Community Fund’s Implementation of NQG

The inaugural implementation of NQG will be used to signal award allocation of the Stellar Community Fund (SCF), an open-application awards program managed by the Stellar Development Foundation (SDF) that draws on community input to support projects building on Stellar.

Resources

Github
Soroban Contract
BlockScience reports: SCF #27, SCF #26, SCF #25, SCF #24, SCF #23
Voting outcome sheets: SCF #28, SCF #29, SCF #30, SCF #31, SCF #32, SCF #33

Current Implementation Details

[More complete documentation coming soon]

Non-determinative—final deliberation based on votes + feedback by Category Delegates and voters.
Onchain tallying using Stellar smart contracts + off-chain computation of neurons.
Three reputation neurons:
- Voting History (logistic voting power attribution)
- Assigned Trust (logistic voting power attribution)
- Verified Tier (linear voting power attribution)
SCF Navigators and Pilots can vote actively or delegate, Pathfinders can only delegate.
Available members to delegate to are appointed through a quarterly nomination and selection process.

Development Roadmap

Phase 1–4: Ideation, Specification, Prototype Development & Beta Version Implementation

Completed in 2023

Guided by BlockScience’ ‘Engineering Design Process’, our journey commenced with the ideation phase, surveying existing solutions and mapping out the landscape of desirables and requirements. A prototype emerged, leveraging the novel capabilities of Soroban, the smart contract platform on Stellar, then in its testnet phase. Through this exploratory stage, we encountered and addressed challenges related to testnet limitations, optimizing our prototype for better efficiency and verifiability. From SCF #20, a beta version of NQG was implemented, serving primarily as a signaling mechanism for SCF’s award allocation, marking our first step towards operational deployment.

In collaboration with BlockScience, several resources were developed during these phases:

Overview of Neural Quorum Governance
Development process of Neural Quorum Governance
Open source library for governance modules: Governance Modules Library

Phase 5: Simulation & Calibration

Starting Q1 ’24

Objective: To conduct data & simulations driven optimization over NQG’s parameters, structure and neurons by using cadCAD and historical data for modeling a diverse set of scenarios in terms of what-ifs and governance-originated adjustments.

Publish detailed periodical data & simulation driven reports and seek community feedback through Discord, structure discussions, and direct consultations, ensuring a broad spectrum of insights to inform the calibration process.
- See past BlockScience reports: SCF #27, SCF #26, SCF #25, SCF #24, SCF #23
- See past voting outcome sheets: SCF #28, SCF #29, SCF #30, SCF #31, SCF #32, SCF #33
Leverage cadCAD-based simulations to validate the NQG performance by assessing voter behavior, award allocation outcomes, and overall system resilience.
- Done for BlockScience reports
Explore dynamic voting power attribution within neurons, simulating real-life learning and trust curves instead of linear voting power attribution, alongside inflationary mechanisms to ensure fairness and adaptability.
- BlockScience has created a resource "Introducing Learning & Saturation Effects into the Prior Voting History Neuron".
- Implemented in Q1 '25 in Voting History and Assigned Trust Neuron [Documentation coming soon]

Phase 6: Full-Scale Deployment within SCF

Starting in Q2 ‘24

Objective: To transition towards a fully operational NQG system within SCF, incorporating lessons learned and community feedback.

Phase 7: Ecosystem Collaboration

Starting in Q4 ‘24 into '25

Objective: To bootstrap collaboration on governance modules within and beyond the SCF ecosystem through an accessible and agnostic library, contributing to a sustainable and representative governance of our digital future.

Develop a holistic governance framework to streamline structure and governance changes within SCF’s NQG implementation based on community feedback.
Comprehensive Documentation and Accessibility: Commit to thorough documentation and easy accessibility of all NQG components, including its structure, neurons, and supportive tools.
Foster Ecosystem-wide Innovation and Collaboration: Encourage a culture of innovation, feedback, and collaboration across the Stellar ecosystem.
Pursue Cross-Community Collaboration: Proactively seek partnerships with diverse communities and ecosystems beyond Stellar to explore the scalability and adaptability of NQG.