The geometry of influence inside clustered systems

Adler’s Algorithm is the informal name for my PhD research in mathematics at Princeton. At its core, it studies how tightly connected networks behave under pressure — how clusters form, interact, and influence each other inside complex systems.

The work lives at the intersection of graph theory, dynamics, and game-theoretic intuition. Rather than treating stability as a static property, Adler’s Algorithm asks how patterns of connection and stress propagate through a system over time, and what that reveals about its underlying structure.

Origins and influences

The project is inspired in part by the ideas of John Forbes Nash — especially the notion that equilibrium is not just a number or solution, but a story about how agents, incentives, and structures fit together. My work extends that intuition into highly interconnected networks, where behavior is shaped as much by cluster geometry as by any single node.

What Adler’s Algorithm explores

At a conceptual level, Adler’s Algorithm focuses on:

• How clusters emerge and persist inside dense networks
• How stress, perturbation, or pressure redistribute across connected components
• How local structure influences global behavior in ways that are not obvious from individual elements alone

While the work is mathematical and abstract, it reflects a broader obsession: understanding how systems behave when they are pushed, distorted, or forced to reveal their deeper shape.

Why it matters for my broader work

Adler’s Algorithm is not a product feature, and it’s not directly embedded in My AI Analyst. But the research deeply shapes how I think about reasoning frameworks, system design, and the flows of information and influence that underlie any analytical engine.

The same instincts that drive this work — respect for structure, skepticism of surface-level patterns, and a desire to “see the system” rather than just its outputs — are the instincts I bring to building tools for data and decision-making.