ABOUT:

Everything that evolves (minds, ecosystems, technologies, societies and even the scientific disciplines, mathematics and  the cosmos itself ) does so through different kinds of domain specific "feedback".  Each nested layer of our reality builds and refines processes by responding to its own patterns and how they interact with the patterns around it, inside it or possibly even encompassing it. RG Emergence studies this process, showing how recursive feedback can generate structure, awareness, and cooperation across scales and systems.

Through ongoing research, we’re developing mathematical and conceptual models that describe how systems learn, stabilize, and adapt. These models connect physics, cognition, and artificial intelligence into a single framework for understanding how complexity organizes itself.

Our aim is simple: to uncover the principles that let information grow into meaning—and to use those insights to build technologies and societies that are more transparent, balanced, and intelligent by design.

To be more technical, this work aims to examine how complex systems use recurrent processes to logically organize information and maintain stability while continuously growing and evolving via the creation, use, reuse and embedding of self similar structures (blocks of logical operations and rules) nested within themselves. This is not unlike the linguistic and grammatical conventions that allows sentences to contain clauses within clauses, or plans to contain sub-plans, while still keeping everything coherent.

To formalize this, we introduce two tools:
The first is a metric called P of trajectories and goals, which measures how well short-term trajectories (the system’s immediate actions or behaviors) stay aligned with long-term global goals (its enduring purposes or intentions).
The second is an agent-based model called the Justice–Cooperation–Balance attractor, where individual agents coordinate their actions around shared higher-order values of justice, cooperation, and balance. The simulations show that collective behavior remains organized when guided by these attractors, even when individual agents follow different paths.

Together, these results support recursion as a stabilizing principle. The coherence metric captures it as measurable alignment between local actions and global aims, while the attractor simulations show how it sustains group coordination. This suggests that recursive organization, when disciplined by higher-order attractors, underlies both individual meaning-making and resilient coordination across collective systems.

Please see the blog for my latest posts and be sure to check out the growing collection of rich explanatory resources and research material on the other pages in the menu. Thankyou for your time and please feel free to reach out for questions or comments.

-C.L Vaillant