Research reveals an optimal no-feedback guessing strategy for a shelf shuffle model, suggesting significant implications for shuffling machine applications.
We investigate a one-time single shelf shuffle by establishing the position matrix explicitly. In some cases, we prove a no-feedback optimal guessing strategy. A general no-feedback strategy is conjectured, and asymptotics for the expected reward are given. For the complete-feedback case, we give a guessing strategy, prove that it is optimal and unique, and find the expected reward. Our results prove a conjecture of Diaconis, Fulman, and Holmes in a special case.
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Alexander Clay (2025) studied this question.
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