Optimal stopping in urn models with payoff depending on maximal observed element

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An urn contains N balls, labelled 1,…, N. Optimal stopping rules are considered for payoff functions f( k, m) where f( k, m) is the reward when stopping after k draws, and the largest number seen by then is m. f( k, m) is assumed nondecreasing i m for each k. We show: (i) For any horizon n ⩽ N, under optimal stopping, sampling without replacement yields a larger expected value than sampling with replacement. (ii) A sufficient condition, both when sampling with or without replacement, for the optimal stopping rule to be of the form t = inf{ k: M k ⩾ q k } for some constants q k , where M k is the maximal label Δ( k, m) = f( k, m + 1) − f( k, m) be nonincreasing in k for each m. Better sufficient conditions are given, and several e such as reward minus cost of sampling, or discounted rewards, are considered. Some limiting results, as N →∞, and prophet inequality considerations are included for the example where the payoff is reward minus cost of sampling.