Greedy Shortcut Model
A developer created a simple bandit model to test user behavior with new-age apps. The model reinforces options that are used more, rather than estimating which option is better. The developer notes that users tend to pick what they already know, rather than the best feature, and is seeking feedback on this observation and the model.
- Published
- 09/07, 07:07 UTC+0
- Ingested
- 09/08, 00:00 UTC+0
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- Dev community
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- Community
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- Healthy
Tier is a per-source editorial setting, not a per-item score.
Not quite for this channel, but relevant to choosing new-age apps: users don’t pick the best feature; they pick what they know.
I built a simple bandit model to test whether usage converges on quality over time. It doesn’t estimate which option is better. It simply reinforces whatever gets used more.
Three update rules produce very different outcomes: one locks onto early winners regardless of quality; one mostly self-corrects but can still get stuck under strong reinforcement; and one control always finds the true best option.
Same mechanism, wildly different UX. Basically, habit formation in miniature.
Feedback welcome :)