Governance Modules Library Overview

Facilitating Computer Aided Governance on dApps and DAOs through Specs and Examples

Please note that this Governance Modules Library is created by BlockScience and is currently under ongoing development. You acknowledge and understand that any implementations you make of the specifications from this Governance Modules Library is solely at your own risk.

Power Attribution

Neural Governance

• Neural Governance
  A modular framework for voting power attribution using a neural network-inspired structure.

Signaling Forms

Quorum Delegation

• Quorum Delegation
  Mechanism for delegating voting power through quorum-based decisions.

Reputation Metrics

Trust Graph Bonus

• Trust Graph Bonus
  The Trust Graph Bonus is a module that enhances voting power for trusted and central community members by allowing users to actively form a network of trust within their ecosystem.

Identity Management

Tier-based Role Certifier

• Tier-based Role Certifier
  The Tier-based Role Certifier (TRC) is a framework for assigning per-identity roles through the aggregation of attestations, incorporating role-specific disqualifiers and autoqualifiers.

---

What is the Governance Modules Library

The Governance Modules Library (GML) is a curation of both new and classic modules for governance in dApps and DAOs. Its stated goal is to spread best practices and innovation as well as to accelerate adoption of Soroban as an ecosystem by providing a batteries-included experience for enhancing DAOs and dApps governance.

A visual depiction of what are (non-exhaustively) Governance Modules. They are spread across categories of typical outputs and use cases.

Governance Modules are templates that can be readily implemented and tuned to specific applications which depend on or require governance solutions. As Governance is a wide topic, we've opted to categorize those templates across typical Web3 use cases, such as:

1. How to attribute numerical power to a user (Power Attribution)
2. How to funnel that power through specific signals (Signaling Forms)
3. How to associate identity and/or rights with a user (Identity / Rights Management)
4. How to generate representative signals from behavioral and/or reputational components (Reputation Metrics)

Those use cases are by no means exhaustive, and further categorizations may arise as inspired by efforts such as distilling Metagov's Govbase.

What should a Governance Module contain in order to be published?

A parent page with its title as the name of the module, containing the following sections:

• Summary
• Use Cases
• User Journey
• Module-specific Adjustments

The following sub-pages:

• Specification
• Implementation Instructions
• Tuning Guidelines
• Simulations

Each page should indicate its primary authors (individuals and/or organizations) and a date. Any further modifications/contributions can be acknowledged and/or listed as authors. Dates must be indicated.

---

Projects using the Governance Modules

• Stellar Community Fund: Neural Governance, Quorum Delegation, Trust Graph Bonus (2023-2025)

---

Contributors

This library has been developed by BlockScience and SDF. Contributions are welcome!