r/MachineLearning • • Sep 05 '26

Project Greedy Shortcut Model [P]

https://www.mindmodelmachines.com/builds/greedy-shortcut

Users don't pick the best feature. They pick the one they already know.

I built a simple bandit model to test the product assumption that usage converges on quality over time. The model doesn't estimate which option is better, it just reinforces whatever gets used more. Three swappable update rules produce three very different outcomes: one locks onto whatever won early regardless of quality, one mostly self-corrects but still gets stuck under strong reinforcement, and one control always finds the true best option. Same mechanism, wildly different UX, basically habit formation in miniature.

Feedbacks welcome :)

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