Let X 1 , X 2 , . . . , X n be independent, identically distributed random variables, uniform on [0, 1]. We observe the X k sequentially and must stop on exactly one of them. No recollection of the preceding observations is permitted. What stopping rule τ minimizes the expected rank of the selected observation? This full-information expected-rank problem is known as Robbins' problem. The general solution is still unknown, and only some bounds are known for the limiting value as n tends to infinity. After a short discussion of the history and background of this problem, we summarize what is known. We then try to present, in an easily accessible form, what the author believes should be seen as the essence of the more difficult remaining questions. The aim of this article is to evoke interest in this problem and so, simply by viewing it from what are possibly new angles, to increase the probability that a reader may see what seems to evade probabilistic intuition.
On the other end, Robbin's problem REF seeks to minimize the expectation of the rank of the selected candidate (note: low ranks are better).
What is known about Robbins' Problem?
Journal of Applied Probability
Mathematics