We are living through an era that mistakes computational intelligence for the whole of intelligence. That misconception, that cognition precedes emotion rather than emerges through it, is a blind spot shaping our technologies, our institutions, and our collective future. As AI systems accelerate in scale and influence, the inability to model emotion meaningfully is becoming not merely a technical limitation, but the defining constraint of the field.

From polarized elections to market swings driven by social contagion, we continually see that emotional forces move systems long before data does. Yet our technologies still treat affect, like trust, fear, awe, and grief, as noise in the signal. If we do not model these dynamics with the rigor they require, the tools guiding our economies, our politics, and even our interpersonal communication will become more capable but not more coherent, more powerful but not more human.

My work began with a simple question: "What if emotion behaves less like a private state and more like a field, something distributive, relational, and measurable?" To explore this, I have been developing what I call Affective Ontology, an attempt to model affect using the conceptual scaffolding of applied mathematics, physics, and network science. This work is an effort to quantify the dynamics of feeling by borrowing methods from disciplines that already know how to model invisible forces.

The project's structure draws inspiration from a historical........

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