About RIEGEL
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The Objective
RIEGEL is a next-generation hybrid intelligence assistant built to support the art and science of system modeling. Designed at the intersection of artificial intelligence and system dynamics, RIEGEL helps researchers, strategists, and decision-makers uncover hidden patterns, identify archetypes, and simulate dynamic behavior with precision and clarity.
By combining natural language understanding with structural pattern recognition, RIEGEL bridges human insight and machine learning—enabling faster and improved problem articulation, hypothesis generation, and discovery of leverage points. Whether you are exploring sustainability transitions, market dynamics, or organizational challenges, RIEGEL provides an adaptable and scalable foundation for dynamic modeling in a rapidly evolving world.
Built also to accelerate archetype discovery and dynamic system simulation, RIEGEL helps users make sense of complex problems at the earliest stages of modeling or during iterative analyses.
Features
Hybrid-Intelligence
At the heart of RIEGEL is a hybrid intelligence framework that fuses human insight with advanced AI capabilities. By combining natural language processing, structural pattern recognition, and scenario-based reasoning, RIEGEL enhances—not replaces—the modeler’s intuition and judgment. This collaborative approach empowers users to articulate complex problems, surface hidden feedback structures, and explore system behavior with clarity. Whether guiding a novice through their first model or supporting experts in high-stakes strategy, RIEGEL's hybrid design ensures adaptability, learning, and trust in every step of the modeling process.
Archetype Discovery
RIEGEL enables rapid identification of system archetypes—both canonical and emerging—through advanced pattern recognition and feedback structure analysis. By analyzing narratives, behaviors, and causal relationships, RIEGEL surfaces underlying system dynamics such as Shifting the Burden, Success to the Successful, or entirely novel configurations. This feature supports modelers in moving from fragmented symptoms to systemic understanding, accelerating hypothesis formation and strategic insight.
Dynamic System Simulation
RIEGEL performs structural pattern-based simulation, rooted in system dynamics but guided by semantic input and pattern recognition. Rather than relying on predefined stocks, flows, and differential equations, RIEGEL uses archetype-informed structural inference to simulate how variables change over time. It maps qualitative narratives and causal relationships into behavior-over-time profiles (reference modes) using a combination of causal loop extraction, polarity and delay inference, Canonical and emergent archetype alignment.
Queries Answered
What is Cognitive Archetype Discovery?
Cognitive Archetype Discovery, powered by RIEGEL, is the process of using hybrid intelligence to uncover recurring patterns, feedback structures, and behavioral dynamics hidden within complex scenarios or data. Unlike traditional analytics, it combines language understanding, systems thinking, and structural reasoning to identify known archetypes (like Limits to Growth or Shifting the Burden)—or even surface entirely new ones.
This helps researchers, strategists, and decision-makers gain deeper insight into the why behind system behavior—not just the what—so they can respond with smarter, system-aware solutions.
How does RIEGEL ensure security?
In its prototype phase, all interactions are processed through encrypted technology, and no sensitive user input is stored or shared without explicit permission.
Are API integrations available?
Not at this time, but it is being considered in a future roadmap.
Connect with RIEGEL.
If you’d like to learn more or get involved while RIEGEL is in active prototype development, let’s connect.