Fuzzy Cognitive Maps (FCMs) are powerful tools for modeling systems where variables influence each other in complex, uncertain ways. Traditionally, building them requires domain experts, people with deep knowledge of how concepts interact. But what if we could replace these experts with language model agents?

Multi-Agent System powered by Large Language Models (LLMs) can autonomously generate, refine, and simulate FCMs, using natural language, structured reasoning, and collaborative memory to build complex causal systems from scratch.

1. Using Multi-Agent LLMs as Expert Replacements

Each agent acts like a virtual domain expert. Given a topic (e.g., economics, climate, or digital addiction), agents:

• Propose concepts (nodes) based on contextual relevance
• Identify potential causal relationships („A increases B“, „C inhibits D“)
• Justify their suggestions with reasoning or supporting text

The multi-agent setup allows diverse perspectives and iterative refinement, similar to a panel of experts debating a model.

2. Creating the FCM in Neo4j via Multi-Agent Collaboration

Through MCP (Model Context Protocol), agents interact with a graph database like Neo4j to:

• Add or refine :Concept nodes
• Establish directional :CAUSES relationships with a weight between -1.0 and 1.0
• Merge similar concepts and prevent duplication

This process turns abstract discussions into structured, editable maps, live graphs that can be queried, visualized, and extended.

3. Simulating the FCM with Initial Guesses or Real Data

Once the map is built:

• A relationship matrix is extracted (concepts x concepts)
• Initial activations for each node can be guessed (uniform, expert estimate, or real measurements)
• The system runs a stepwise simulation, where each timestep updates concept values based on their causal influences

This allows for dynamic forecasting, scenario analysis, or even reinforcement learning over cognitive structures.

Conclusion

This approach transforms FCM creation from a slow, expert-driven process into a high-speed, intelligent collaboration. Synthetic experts that can work continuously and scale across domains.