Ad Microtargeting: Field & ROI Frontier (2D)

Campaign Controls

Targeting precision 70%

Campaign budget 60%

Message intensity 1.0×

Ad-library disclosure (counter-messaging)

Live Readouts

Avg. belief shift

0.000

Targeting ROI

0.000

Reach (segment %)

Polarization σ

0.000

How it works

This is a 2D-native rendition of the same population field as the 3D bar-chart version: 1,600 simulated people sit on a flat political-lean (x) / media-trust (y) grid, each with a fixed susceptibility bump s(x,y), and the identical exposure/belief-update model runs directly on that plane — the field is drawn as a colour-mapped heatmap (blue → red), never as an extruded or camera-projected scene:

s(x,y) = exp( -((x-x0)² + (y-y0)²) / (2σs²) )
e(x,y) = (1-T)·(B/N) + T·e_targeted(x,y)
belief += η · e(x,y) · s(x,y) · intensity · (target − belief) · 0.15

The strip beneath the field is what a flat colour map alone can't show: an analytic ROI-frontier. Instead of running the campaign once, it recomputes — from the population's current belief state, without altering it — what one pulse's ROI and reach would be at every targeting precision from 0% (broadcast) to 100% (fully targeted), at the budget/intensity/disclosure you've currently dialled in. The marker shows exactly where your chosen targeting slider sits on that curve, so you can see the whole efficiency trade-off at once instead of one point at a time — a phase-space view with no equivalent in the 3D scene.

Ad-library disclosure multiplies targeted exposure by (1 − 0.65), modelling ad-transparency mandates that force microtargeted ads into a public archive and blunt precision targeting's ROI edge over honest broadcast — both the live field and the frontier curve react to the toggle immediately.

x → political lean · y → media trust · colour → belief

Unpersuaded (belief ≈ 0) Persuaded (belief → target)