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-title: Stay in the Loop
-colorFrom: blue
-colorTo: green
-sdk: static
-pinned: false
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-# Stay in the Loop
-
-A static educational game for the PS1 Version 2 proposal **When Should Humans Stay in the Loop? Maintaining Human Capability under Automation Risk**.
-
-## Research question
-
-When is it worth accepting a small current performance cost from manual control in order to preserve human capability for future periods when automation may be unavailable?
-
-## How the game works
-
-You make repeated allocation decisions between **Automation** and **Manual Control**.
-
-- Automation has fixed success probability `p_A = 0.95`.
-- Human manual success depends on current capability `s_t`.
-- Choosing Automation causes non-practice decay in `s_t`.
-- Choosing Manual Control preserves or restores `s_t`.
-- In each round, automation may be unavailable with probability `q`; then manual control is required.
-- A failure incurs loss `L = 100`.
-- The game compares your choices with a finite-horizon dynamic-programming benchmark.
-
-Choose **Low risk** (`q = 0.10`) or **High risk** (`q = 0.30`) and play 12 rounds. The benchmark recommendation is shown after each decision so the game remains an educational demonstration rather than a behavioral measurement instrument.
-
-## Interpretation
-
-The game illustrates the proposal's intertemporal trade-off:
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-`current automation advantage -> allocation -> evolving human capability -> future backup value`
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-The key model prediction is:
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-`q increases -> optimal manual practice increases`
-
-## Scope and limitations
-
-This is a synthetic classroom model. The capability-decay and practice-recovery parameters are design assumptions, not empirical estimates of real human skill. Game choices are not evidence about human behavior unless data are collected under an approved study design.
-
-No backend, login, personal information, or external API is used.