hivemind-python 0.1.0

A Condorcet-style Ranked Choice Voting System that stores all data on IPFS

Hivemind Protocol

A decentralized decision-making protocol implementing Condorcet-style Ranked Choice Voting with IPFS-based data storage and Bitcoin-signed message verification.

What is the Hivemind Protocol?

The Hivemind Protocol is a revolutionary approach to decentralized decision-making that combines:

• Condorcet-style ranked choice voting
• Immutable IPFS-based data storage
• Cryptographic verification using Bitcoin signed messages
• Flexible voting mechanisms and constraints

Key Features

1. Decentralized & Transparent

   • All voting data stored on IPFS
   • Complete audit trail of decisions
   • No central authority or server
   • Cryptographically verifiable results

2. Advanced Voting Mechanisms

   • Condorcet-style ranked choice voting
   • Multiple ranking strategies (fixed, auto-high, auto-low)
   • Support for various answer types (String, Integer, Float)
   • Weighted voting capabilities
   • Custom voting restrictions and rules

3. Secure & Verifiable

   • Bitcoin-style message signing for vote verification
   • Immutable voting history
   • Cryptographic proof of participation
   • Tamper-evident design

How It Works

1. Issue Creation

An issue represents a decision to be made. It can contain:

• Multiple questions
• Answer type constraints (String/Integer/Float)
• Participation rules
• Custom validation rules

issue = HivemindIssue()
issue.name = "Protocol Upgrade"
issue.add_question("Should we implement EIP-1559?")
issue.answer_type = "String"

2. Option Submission

Participants can submit options as potential answers:

• Each option is stored on IPFS
• Options are validated against issue constraints
• Options require cryptographic signatures
• Options can be added dynamically

option = HivemindOption()
option.set_hivemind_issue(issue.cid)
option.set("Yes, implement EIP-1559")

3. Opinion Formation

Participants express preferences through three ranking methods:

1. Fixed Ranking

   opinion = HivemindOpinion()
   opinion.ranking.set_fixed([option1.cid, option2.cid])  # Explicit order
   

2. Auto-High Ranking

   opinion.ranking.set_auto_high(preferred_option.cid)  # Higher values preferred
   

3. Auto-Low Ranking

   opinion.ranking.set_auto_low(preferred_option.cid)  # Lower values preferred
   

4. State Management

The protocol maintains state through:

• Option tracking
• Opinion collection
• Signature verification
• Result calculation
• State transitions

state = HivemindState()
state.set_hivemind_issue(issue.cid)
state.add_option(timestamp, option.cid, voter_address, signature)
state.add_opinion(timestamp, opinion.cid, signature, voter_address)

5. Result Calculation

Results are calculated using Condorcet method:

1. Pairwise comparison of all options
2. Preference matrix creation
3. Winner determination
4. Tie resolution

results = state.calculate_results()
winner = state.consensus()

Examples

Detailed examples can be found in the examples/ directory:

1. basic_voting.py - Simple voting example
2. advanced_features.py - Advanced protocol features
3. protocol_upgrade.py - Governance decision example

Each example is thoroughly documented and can be run independently. See the examples README for more details.

Installation

You can install the package using pip:

pip install hivemind-python

Or install from source:

git clone https://github.com/ValyrianTech/hivemind-python.git
cd hivemind-python
pip install -e .

Requirements

• Python 3.10 or higher
• ipfs-dict-chain >= 1.0.9

Advanced Features

Custom Constraints

issue.set_constraints({
    'min_value': 0,
    'max_value': 100,
    'specs': {'type': 'Integer'}
})

Voting Restrictions

issue.set_restrictions({
    'min_participants': 5,
    'allowed_addresses': ['addr1', 'addr2'],
    'min_weight': 10
})

Auto-Ranking with Values

option1.set(75)  # Integer value
option2.set(25)  # Integer value
opinion.ranking.set_auto_high(option1.cid)  # Will rank options by proximity to 75

Use Cases

1. Governance Decisions

   • Protocol upgrades
   • Parameter adjustments
   • Resource allocation

2. Community Polling

   • Feature prioritization
   • Community preferences
   • Strategic decisions

3. Multi-stakeholder Decisions

   • Investment decisions
   • Project prioritization
   • Resource allocation

Documentation

Full documentation is available at https://valyriantech.github.io/hivemind-python/

License

This project is licensed under the MIT License - see the LICENSE file for details.