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https://github.com/leonvanbokhorst/friction-flow

The Friction-Flow framework aims to analyze and track narrative field dynamics in complex social systems, emphasizing the evolution, interaction, and influence of stories.
https://github.com/leonvanbokhorst/friction-flow

complexity narrative narrative-field narrative-field-dynamics simulation simulation-framework social-dynamics

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The Friction-Flow framework aims to analyze and track narrative field dynamics in complex social systems, emphasizing the evolution, interaction, and influence of stories.

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# Friction Flow



## Project Overview



Friction Flow is an advanced Python-based research project aimed at developing a framework for analyzing and simulating complex human behavior and group interaction based on Narrative Field Dynamics. This project leverages AI and machine learning techniques, with a focus on integrating Large Language Models (LLMs) for natural language-based decision making and interactions.



## Key Features



1. **Multi-Agent Systems**: Simulates emergent behavior in complex social systems using Graph Attention Networks (GAT)

2. **Psychological Modeling**: Incorporates models of individual and group psychology with emotional states

3. **LLM Integration**: Utilizes language models for natural language processing and generation

4. **Meta-Learning**: Implements Model-Agnostic Meta-Learning (MAML) for rapid adaptation

5. **Social Network Analysis**: Advanced relationship modeling with quantum-inspired dynamics



## Technical Stack



**Python**: Core programming language (version >= 3.12.6 recommended)



- **PyTorch**: For neural network components and tensor operations

- **Transformers**: For integration with pre-trained language models

- **Ray**: For distributed computing

- **FastAPI**: For service endpoints

- **Redis**: For state management

- **Ollama**: For local LLM integration

- **ChromaDB**: For vector storage and similarity search



## Core Components



### 1. Social Network Analysis (gat_social_network.py)



- Graph Attention Network (GAT) for relationship modeling

- Multi-head attention mechanisms

- Community detection using Louvain method

- Real-time visualization of social dynamics

- Comprehensive metrics tracking

- Classroom social dynamics demonstration



### 2. Meta-Learning Framework (maml_model_agnostic_meta_learning.py)



- Model-Agnostic Meta-Learning (MAML) implementation

- Adaptive learning rate scheduling

- Skip connections for improved gradient flow

- Comprehensive visualization capabilities

- Task-specific adaptation

- Enhanced visualization with feature importance analysis



### 3. Narrative Field Dynamics



The project implements three core approaches to narrative field dynamics:



#### Story Waves



- Quantum-inspired approach to modeling narrative dynamics

- Resonance level tracking

- Theme interaction analysis

- Emotional impact measurement



#### Three Story Evolution



- Detailed evolution of interacting stories with emotional states

- Story state management with resonance tracking

- Memory-based updating mechanism

- Collective story emergence analysis



#### Simple Lab Scenario



- Practical application in simulated environments

- Real-world interaction modeling

- Team dynamics simulation

- Ethics and mental health integration



### 4. Belief Systems (bayes_updating.py)



- Bayesian belief updating using LLM embeddings

- Dynamic confidence tracking

- Historical state maintenance

- Time-based decay modeling

- Visualization of belief evolution



### 5. Deep Learning Components



#### Deep Belief Networks (DBN)



- MNIST demonstration implementation

- Hierarchical feature learning

- Layer-wise pretraining

- Comprehensive visualization tools



#### Hopfield Networks



- Pattern recognition and completion

- Associative memory demonstration

- Modern attention-like mechanisms

- Quantum-inspired dynamics



## Experimental Results



### 1. Social Network Analysis



- Successfully modeled classroom dynamics with 5+ distinct personality types

- Detected natural community formations

- Tracked influence pathways between agents

- Visualized relationship networks and evolution



### 2. Meta-Learning Performance



- Rapid adaptation to new tasks (3-5 gradient steps)

- Robust performance across varying task complexities

- Effective feature importance identification

- Clear visualization of adaptation progress



### 3. Belief System Dynamics



- Demonstrated smooth belief transitions

- Tracked confidence evolution

- Showed effective handling of contradictory evidence

- Visualized belief space trajectories



## Development Guidelines



- Follow PEP 8 style guide and use Black for code formatting

- Implement type hints as per PEP 484

- Maintain a minimum of 80% test coverage

- Adhere to SOLID principles

- Use meaningful commit messages following conventional commits format



## Testing



Run the test suite using pytest:



```bash

pytest tests/

```



## CI/CD



The project uses GitHub Actions for continuous integration with:



- Python 3.12.6 setup

- Dependency installation

- Automated testing

- Code quality checks



## Contributing



We welcome contributions. Key points:



- No commented-out code in main branch

- No TODOs in main branch

- Clear variable and function naming

- Adherence to DRY and SOLID principles



## License



This project is licensed under the Apache License 2.0. See the LICENSE file for details.



## Acknowledgments



This project builds upon research in cognitive science, complex systems theory, social network analysis, and organizational behavior. Special thanks to the open-source community and the developers of the libraries and tools used in this project.