Decentralized Gaming DAO Governance Analysis

Welcome to my repository exploring decentralized governance mechanisms in gaming DAOs using graph databases, AI, and blockchain technologies.

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Project Overview

Domain: In-Game DAO Governance
Core Objective: Build transparent, fair, and anti-manipulation frameworks for decentralized decision-making in gaming communities.

Key Problems Addressed:

1. Guild Collusion: Detecting coordinated voting patterns among clans.
2. Token Imbalance: Identifying players/guilds with excessive governance power.
3. Sybil Attacks: Flagging fake accounts influencing proposals.
4. Low Participation: Analyzing voter turnout and casual player representation.

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Technologies Used

• Graph Database: Neo4j (Cypher queries, AuraDB)
• AI/ML: Retrieval-Augmented Generation (RAG), LLMs (GPT-4, LangChain)
• Data Pipeline: CSV-to-graph ETL, Synthetic data generation with AI
• Tools: Python

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Featured Work

1. DAO Voting Power Analysis

• Graph Structure: Modeled User, Guild, Proposal, and Token nodes with relationships like VOTED_FOR and HOLDS_TOKEN.
• Key Insight: Calculated voting power using token amounts × governance scores to flag imbalances.

2. Collusion Detection

• Identified guilds where members vote identically on proposals using path analysis.
• Example Query:

  MATCH (g:Guild)<-[:MEMBER_OF]-(u:User)-[:VOTED_FOR]->(p:Proposal)
  RETURN g.name, p.title, COUNT(u) AS alignedVotes ORDER BY alignedVotes DESC
  

3. AI-Augmented Governance

Integrated RAG chatbots to answer governance queries using Neo4j data.

Use Case: "Which proposals did EpicGuild create?" → Bot retrieves linked proposals and voting trends.

DAO-Gaming-Governance/
├── Dataset/ # Synthetic CSV files ├── Cypher-Queries/ # Neo4j scripts for node/relationship creation
├── Analysis/ # Voting power calculations & collusion reports
└── AI-Models/ # RAG pipelines for governance Q&A