About Me
Vishnu / Vishnupriya Napa Ravikumar
I trained as an architect and moved through urban planning into economics, chasing a way to make inequality tractable — to get it out of description and into equations, thresholds, and quantities that move. I do that from human centered design and engineering, which keeps the question of who a system is for attached to the formalism rather than downstream of it. I am now building the models I kept needing and not finding.
My work asks why innovation systems concentrate their gains, and follows that question down until the standard formal apparatus stops being able to represent it — from the institutional mechanisms that produce concentration, to their firm-level source in endogenous concavity, to whether the mathematics economics borrowed from classical mechanics can carry the explanatory weight placed on it.
The extension I am building treats growth as the feasible rate at which an economy expands its own possibility space, bounded by how fast human cognition and institutions can actually move. AI is a direct shock to that bound, which is why I am formalizing it now.
PhD student in Human Centered Design and Engineering at the University of Washington. Advised by Dr. Stephen Turnovsky (Economics), Dr. Beth Kolko (Human Centered Design & Engineering), and Dr. Joshua Eisenthal (Philosophy of Science, Physics).
Research
Four strands of one question, at descending levels of abstraction.
Innovation System Inequality
Doctoral preliminary examination paper, 2025.
Innovation studies has documented cumulative advantage qualitatively since Merton. I translate it into mechanism. Three institutional forces interact: profit-filtering, where problems without a business model never become visible; cumulative advantage, where early leads compound into permanent dominance; and adaptive resistance, where concentrated interests coordinate against oversight faster than oversight can grow — and which amplifies super-linearly, pushing back hardest exactly when the challenge is most serious.
Jointly they produce concentration exceeding what any one produces alone. A boundary condition (β₁ × Π × γ1.2 = 1.4) partitions innovation systems into three regimes with distinct stability properties: Market Fundamentalist, Human-Centered, and Social Democratic. Because the cross-partials are negative, reforms compound — comprehensive institutional change outperforms incremental adjustment by 2.17×, and prevention costs 4.5× less than correction.
Applied to AI governance, the framework places current arrangements at 1.011 against a critical threshold of 1.4: approaching the point at which technocratic control becomes structurally immune to democratic correction, but not yet past it.
The paper distinguishes computational coherence from empirical validation throughout, reporting fuzzy-set membership and Monte Carlo confidence intervals rather than point classifications, and specifies the longitudinal and cross-national data required to test the framework.
Endogenous Mode-Switching
In preparation.
Why do large firms escape diminishing returns? Standard growth theory treats the curvature of the production function as a fixed parameter — Samuelson's comparative statics yield signed results precisely because concavity is exogenous.
I make it endogenous. Firm size governs the number of qualitatively distinct modes a firm can operate across: production, R&D, lobbying, acquisition, IP licensing. Each mode diminishes individually, but a firm large enough to rotate between them faces weaker effective concavity, grows faster, and grows larger still. The second-order conditions the standard apparatus depends on become variables rather than parameters, and identical policy shocks produce divergent responses across the firm-size distribution.
The concentration is structural. It follows from scale itself, not from conduct.
Foundations of the Formalism
In preparation, with Dr. Joshua Eisenthal.
Samuelson imported the Lagrangian from classical mechanics — the physics of fixed state spaces and known constraints. This companion paper asks whether the experimental identifications that ground that formalism in physics survive the transfer to economics, or whether the borrowing yields a mathematics that predicts without explaining.
I treat the modeling and the critique as one project rather than two.
The Inter-Firm Epistemological Loop
Long-run research program.
When firms produce knowledge, they expand a shared epistemological commons that raises every other firm's capacity to discover new modes — which in turn expands the commons again. New firm types emerge endogenously; the set of possible types is a variable, not a parameter.
This breaks the apparatus. The dimensionality of the dynamical system becomes itself a dynamic variable, and you cannot write it as a fixed system of N equations because N changes as the economy evolves.
It also reframes what growth is. Not optimization within a known space, nor expansion of a known space, but the speed at which the space itself can be revealed — bounded by how fast human cognition, institutional learning, and epistemological transmission can actually move.
Building the mathematics for this may require a second round of borrowing from physics — this time from general relativity, quantum field theory, and Prigogine's work on dissipative structures, the physics of evolving state spaces and time-bounded possibility-expansion that did not exist when Samuelson borrowed the first time. Done, this time, with the critical awareness the companion paper develops.
