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💼 Quantum-Tech Beveridge Curve: 3D Search & Matching Model

Modelled by a Cobb-Douglas matching function, shifting training investment inward compresses the Beveridge curve. Controls adjust matching efficiency, job separations, vacancy creation, and unemployment elasticity.

Economics & Social Systems3DAdvanced60 FPS📱 Mobile-adapted⇄ 2D version

How it Works

This 3D scene visualizes the micro-foundations of the Beveridge curve: rather than only plotting aggregate unemployment (U) and vacancy (V) rates, it shows the individual unemployed quantum-sector workers and open quantum-industry vacancies as particles that physically drift together and merge when they meet. Each merge is a real hire, exactly as counted by the Cobb-Douglas matching function M = A·U^α·V^(1-α) that governs the aggregate flow equations dU/dt = s(1-U) − M and dV/dt = c(1-V) − M. Raising the training-investment slider A shrinks the effective matching radius, so particles pair off faster — visibly thinning the unmatched population and pulling the floating Beveridge chart's curve inward toward the origin, the same real, empirically observed effect shown in the companion 2D simulation.

Frequently Asked Questions

What are the particles in the 3D scene?

Blue particles are individual unemployed quantum-sector workers and amber particles are individual open quantum-tech vacancies, both drifting through a shared 3D matching-market space. When a worker and a vacancy particle drift close enough, they merge in a bright match flash and are removed — a micro-level hire.

How do the individual matches build the Beveridge curve?

Every match event increments the aggregate hire count for that instant. The resulting flow of hires drains the modelled unemployment and vacancy stocks exactly as in the underlying Cobb-Douglas matching function M = A U^α V^(1-α), and the same (U, V) path is plotted on the floating 2D chart inside the scene — so the aggregate curve is literally built up from the visible micro-level matching events, not computed separately.

What does raising training investment A do in 3D?

A higher A shrinks the "meeting radius" at which nearby worker and vacancy particles match, in effect speeding up how efficiently the two clouds find each other. Visually, more merges happen per second and the population of unmatched particles stays lower, while the floating Beveridge chart's curve pulls inward toward the origin.

Can I orbit the camera around the matching market?

Yes — drag to orbit, scroll or pinch to zoom, exactly as in any Three.js OrbitControls scene. The floating Beveridge chart panel stays oriented toward the camera so it is always readable while you look at the matching space from any angle.

⚙ Under the hood

economicslabor marketBeveridge curvesearch and matchingCobb-Douglasquantum careersthree.jssimulation

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install