A small interactive lab demonstrating how constraints progressively shrink a possibility space until an outcome becomes predictable.
The demos visualize a principle used in many real systems — search, recommendation engines, routing, and decision systems — where a large set of possible outcomes is narrowed step‑by‑step through constraints.
https://putmanmodel.github.io/bounded-choice-lab/
A classic reasoning puzzle demonstrating how structured constraints can make an outcome appear inevitable.
The demo visualizes how a wide initial possibility space gradually collapses as conditions are applied.
A simple algebraic sequence showing deterministic collapse:
n → n × 2 → +4 → ÷2 → − n = 2
No matter the starting number, the sequence always resolves to the same final value.
Users can:
• Step through the sequence to reveal the constraint process • Enter their own numbers and experiment
Illustrates how modern recommendation systems narrow a massive content space through progressive filtering.
Example flow:
120,000 possibilities
→ 15,000 filtered by topic
→ 1,200 filtered by preference
→ 500 filtered by behavior
→ 40 highly relevant
→ final recommendation
Shows how spatial and behavioral constraints guide shoppers toward a small subset of products from a large inventory.
Example model:
30,000 total items
→ 500 visible nearby
→ 60 on the aisle
→ 5 immediately considered
All demos illustrate the same structural pattern:
Large Possibility Space
↓
Constraints Applied
↓
Remaining Possibilities Shrink
↓
Prediction Becomes Possible
What often feels like intuition, magic, or a trick is actually the result of a constraint sequence collapsing the possibility space.
The numbers displayed in these demonstrations are illustrative modeling values only.
They are simplified examples used to visualize how constraints reduce a possibility space.
They should not be interpreted as real‑world:
• market sizes
• inventory counts
• recommendation system outputs
• production algorithms
• retail behavior data
The goal of the demos is conceptual visualization, not empirical modeling.
Start a simple server from the project directory:
python3 -m http.server 8000Then open:
http://localhost:8000
Bounded Choice Lab is a teaching and visualization tool for understanding:
• constraint‑driven prediction
• shrinking possibility spaces
• decision systems
• deterministic transformations
• recommendation funnels
The demos are intentionally minimal so the structure of the system remains visible.
This project is released under the Creative Commons Attribution‑NonCommercial 4.0 International License (CC‑BY‑NC‑4.0).
See the LICENSE file for details.
Stephen A. Putman
putmanmodel@pm.me
X / BlueSky: @putmanmodel
Reddit: u/putmanmodel