This repository contains the research data, simulation code, and analysis tools for the Proof of Synergy (PoSyg) consensus protocol doctoral research.

posyg-research/
├── simulations/
│   ├── posyg_simulation.py      # Main Python simulation framework
│   ├── run_experiments.sh       # Batch experiment runner
│   └── config/
│       ├── baseline.json        # Baseline scenario configuration
│       ├── attacks.json         # Attack scenario configurations
│       └── stress_test.json     # Stress test parameters
├── specifications/
│   ├── PoSyg.tla               # TLA+ formal specification
│   ├── PoSygMC.tla             # Model checking configuration
│   └── invariants.tla          # Additional invariants
├── analysis/
│   ├── posyg_analysis.ipynb    # Main analysis notebook
│   ├── attack_analysis.ipynb   # Attack resistance analysis
│   └── economic_analysis.ipynb # Economic model analysis
├── results/
│   ├── baseline/
│   │   ├── metrics_history.csv
│   │   ├── final_validator_states.csv
│   │   └── simulation_config.json
│   ├── cartel_attack/
│   │   └── [simulation output files]
│   ├── high_byzantine/
│   │   └── [simulation output files]
│   └── summary_statistics.csv
├── docs/
│   ├── usage_guide.md          # How to run simulations
│   ├── metrics_glossary.md     # Explanation of metrics
│   └── tla_guide.md           # TLA+ specification guide
└── requirements.txt            # Python dependencies

## Quick Start

### 1. Install Dependencies

```bash
pip install -r requirements.txt

Required packages:

• numpy>=1.21.0
• pandas>=1.3.0
• matplotlib>=3.4.0
• seaborn>=0.11.0
• networkx>=2.6
• scipy>=1.7.0
• jupyter>=1.0.0

python simulations/posyg_simulation.py

This will run three default scenarios:

• baseline: Normal network conditions
• cartel_attack: 30% cartel coordination
• high_byzantine: 33% Byzantine validators

jupyter notebook analysis/posyg_analysis.ipynb

SimulationConfig(
    # Network parameters
    num_validators=100,              # Number of validators
    initial_stake_distribution="pareto",  # Stake distribution type
    
    # Consensus parameters
    block_time=6.0,                  # Seconds per block
    epoch_length=32,                 # Blocks per epoch
    finality_threshold=0.67,         # 2/3 + 1 Byzantine threshold
    
    # Synergy score weights
    stake_weight=0.4,                # Weight for stake component
    activity_weight=0.4,             # Weight for activity component
    governance_weight=0.2,           # Weight for governance component
    
    # Attack parameters
    byzantine_ratio=0.05,            # Fraction of Byzantine validators
    cartel_size=0.0,                 # Fraction in coordinated cartel
    sybil_nodes=0,                   # Number of Sybil identities
    
    # Simulation settings
    simulation_epochs=1000,          # Duration of simulation
    random_seed=42                   # For reproducibility
)

1. HONEST: Always follows protocol rules
2. BYZANTINE: Actively tries to disrupt consensus
3. LAZY: Minimal participation, occasional attestations
4. OPPORTUNISTIC: Defects when profitable
5. CARTEL_MEMBER: Coordinates with other cartel members
6. ADAPTIVE: Learns and adapts behavior based on network state

Time-series data with per-epoch metrics:

Final state of each validator:

1. Install TLA+ Toolbox
2. Open specifications/PoSyg.tla
3. Create model with configuration from PoSygMC.tla
4. Check properties:
   • TypeInvariant: Type correctness
   • SafetyInvariant: No conflicting finalized blocks
   • ByzantineFaultTolerance: < 1/3 Byzantine control
   • DecentralizationInvariant: No validator > 33% power
   • CartelResistance: No small group majority

\* No two finalized blocks at same height
SafetyInvariant ==
    \A i, j \in 1..Len(blockchain) :
        (i # j /\ blockchain[i].height = blockchain[j].height) =>
        ~(blockchain[i].status = "finalized" /\ 
          blockchain[j].status = "finalized")

\* Byzantine fault tolerance maintained
ByzantineFaultTolerance ==
    byzantineStake < TotalStake * (1 - FinalizationThreshold)

• Network health metrics
• Validator strategy performance
• Synergy score dynamics
• Attack resistance analysis
• Economic analysis
• Statistical tests

• Sybil attack scenarios
• Cartel coordination patterns
• Nothing-at-stake analysis
• Long-range attack resistance

• Token distribution evolution
• Reward optimization
• Slashing economics
• Stability fund modeling

def _make_attestation_decision(self, validator: ValidatorState, block: Block) -> bool:
    if validator.strategy == AgentStrategy.YOUR_STRATEGY:
        # Implement custom decision logic
        return your_decision_logic(validator, block)

config = SimulationConfig(
    num_validators=200,
    byzantine_ratio=0.2,
    cartel_size=0.15,
    # Custom parameters
    your_parameter=value
)

Add to collect_metrics() method:

metrics = {
    # ... existing metrics ...
    "your_metric": calculate_your_metric(),
}

1. Memory Error: Reduce simulation_epochs or num_validators
2. Slow Simulation: Use multiprocessing or reduce network size
3. Missing Results: Check file paths and permissions

• Use PyPy for 2-3x speedup
• Parallelize scenarios with multiprocessing
• Profile with cProfile for bottlenecks

If you use this code in your research, please cite:

@misc{krizhanovskyi2025posyg,
  title={A Multi-Dimensional Analysis of Proof of Synergy},
  author={Krizhanovskyi, Daniil},
  year={2025},
  institution={Independent Research}
}

This research code is provided for academic use. See LICENSE file for details.

• Author: Daniil Krizhanovskyi
• Email: [email protected]
• Area of research: Distributed Systems, Blockchain Technology