Inoculation Theory Visualizer¶
Learning Objective¶
Analyze how psychological inoculation ("prebunking") reduces the spread of misinformation across a population, and compare its effectiveness to debunking after misinformation has spread.
How to Use¶
- Click Release Misinformation to start the spread in both populations.
- Watch how the right population, where about 65% have been prebunked (shown with shield outlines), resists infection at a much higher rate than the left.
- Adjust the Spread rate slider (1–5) to model more or less aggressive misinformation.
- Toggle Debunk Mode ON and click Release Misinformation again to see what happens when corrections are deployed only AFTER misinformation has spread — note that corrections reach only a small fraction of believers.
- Click Deploy Correction at any time to manually attempt to flip believers to "Correction Reached".
- Click Reset to randomize a new shielded population and clear all states.
Specification¶
The full specification below is extracted from Chapter 12: Public Health Communication and Health Literacy.
Type: microsim
sim-id: inoculation-theory-sim
Library: p5.js
Status: Specified
Side-by-side simulation comparing two populations: "No Prebunk" (left panel)
and "Prebunked" (right panel). Each population is represented as a grid of
100 people icons. A slider labeled "Misinformation spread rate" (1-5) controls
how aggressively icons turn red (believed misinformation) over time. A
"Release Misinformation" button triggers the spread animation. In the
Prebunked panel, approximately 60-70% of icons have a "shield" overlay
(representing inoculation). When misinformation spreads, shielded icons
resist and remain green; unshielded icons turn red. A counter at the bottom
of each panel shows: Exposed, Believed, Resisted, Correction Reached. A
second mode toggle labeled "Debunk Mode" changes the simulation so that
correction messages deploy after misinformation spreads, showing the limited
reach of corrections. Reset button restores both panels.