Agent-based dashboard: how a deepfake spreads through a school network, and where a limited education budget actually helps. Illustrative, not validated.
Justin Visser
736cd9070d
One random threshold per DIRECTED edge, drawn once per world: scenarios sharing thresholds differ only in who is educated, the prototype's 'same world, different lever' trick. The cascade is plain BFS in rounds; an educated student receives the fake but never forwards it, which is the entire effect of the lever. The result stores the first-reached round per node (exactly what a frontend animation needs). Tests hand-craft a 4-node line graph with exact thresholds, so every expectation is exact: spread, directional blocking, educated cutoff. |
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| cmd/spreadlab | ||
| internal/engine | ||
| .gitignore | ||
| go.mod | ||
| LICENSE | ||
| README.md | ||
spreadlab
Self-hosted dashboard that runs an agent-based deepfake-spread model live: change the levers, watch the spread, and (later) search for the best intervention under a budget. Output is illustrative, not validated.
Status: milestone 1, porting the simulation engine.
Layout:
internal/engine/- the pure simulation engine (no web dependencies)cmd/spreadlab/- the server binary (milestone 2)web/- Vue 3 + TypeScript frontend (milestone 2+)
MIT licensed.