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Agent-based dashboard: how a deepfake spreads through a school network, and where a limited education budget actually helps. Illustrative, not validated.
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Justin Visser c40e483ee6 engine: education strategies (random vs most-connected) 
Two ways to spend the same education budget: a uniform random sample
(spray and pray) vs the highest-degree hubs, ties broken stably towards
lower node numbers so the pick is deterministic. The origin is never
educated; they post the fake. rng.Shuffle does a seeded Fisher-Yates,
so the random pick is reproducible too. min() is a builtin since
Go 1.21.
2026-06-10 12:08:39 +02:00
cmd/spreadlab batman 2026-06-10 11:57:10 +02:00
internal/engine engine: education strategies (random vs most-connected) 2026-06-10 12:08:39 +02:00
.gitignore batman 2026-06-10 11:57:10 +02:00
go.mod batman 2026-06-10 11:57:10 +02:00
LICENSE batman 2026-06-10 11:57:10 +02:00
README.md batman 2026-06-10 11:57:10 +02:00

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.