A single-page, static browser demo showing a toy crowd field on a 2D grid.
- Activation (a) = agitation / energy (shown as brightness)
- Reference / concentration (r) = alignment / local concentration (shown as hue shift)
The core claim the demo visualizes:
Same Ā, different reference concentration. Two crowds can share the same mean activation (Ā), yet behave differently because effective dissipation depends on how concentrated or aligned the reference field is.
This is a reaction-diffusion-style toy model intended for intuition and screenshots — not a literal social simulation.
Open index.html directly in a browser.
Or publish the folder as static files using GitHub Pages.
No build step and no external libraries are required.
The default view is side-by-side:
- L (baseline crowd / no leaders) — distributed reference (low r)
- R (seeded leaders) — a few localized high-r “leaders” (same mean activation target)
Both sides follow identical rules. The only difference is how the reference field is initialized.
The metrics strip updates live:
- Ā (mean activation)
- C (mean reference)
- D(C) (effective dissipation)
Interpretation:
If Ā is similar but C diverges, the crowd’s effective behavior diverges even when mean activation alone would suggest the same state.
Start / Pause
Runs or stops the simulation.
Reset
Resets the current scenario to its default state.
Trigger Event
Applies a short pulse that primarily shifts reference (r). Activation reacts as a secondary effect.
Run 8s Benchmark
Runs a deterministic 8-second test and stops.
Benchmark Shot (8s)
Runs the same deterministic test and stops on a clean frame for screenshots.
High Activation, Stable
Strong activity while reference alignment remains coherent.
Reference Collapse
Reference fails to hold. Activation may appear high but lacks coordinated alignment.
Knife Edge
System sits near the stability transition boundary.
| Baseline | Benchmark |
|---|---|
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| Event | Amplification |
|---|---|
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- The model is intentionally minimal.
- Deterministic seeding ensures repeatable runs.
- Crowd size is constant — the grid is always fully populated.
- What changes is alignment (reference) and agitation (activation).
Stephen A. Putman
Email: putmanmodel@pm.me
GitHub: https://github.com/putmanmodel
X / Twitter: https://x.com/putmanmodel
BlueSky: https://bsky.app/profile/putmanmodel.bsky.social
Reddit: https://www.reddit.com/user/putmanmodel
This project is licensed under the Creative Commons Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0).
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
Under the following terms:
- Attribution — credit must be given to the author
- NonCommercial — the material may not be used for commercial purposes
See the LICENSE file for the full legal text.