Multiunit I.I.D. Prophet Inequalities via Extreme Value Asymptotics

We study the i.i.d. $k$-selection prophet inequality problem, where a decision-maker sequentially observes $n$ independent nonnegative rewards and may accept at most $k$ of them without knowledge of future realizations.

The objective is to maximize the expected total reward relative to that of a prophet who observes all rewards in advance.

This problem captures the performance limits achievable in online resource allocation and underlies posted-price mechanisms in online marketplaces.

We characterize the optimal welfare achievable relative to the prophet in terms of $k$ and the extreme value index of the reward distribution, in the asymptotic regime where the number of offers $n$ grows large.

This optimal performance ratio turns out to be at least $1-\frac{\log k}{8k}[1+\epsilon]$ for any $\epsilon > 0$ and sufficiently large $k$, improving upon the respective, tight $1 - \frac{1}{\sqrt{2\pi k}}$ guarantee of static-threshold algorithms.

We additionally analyze the certainty-equivalent (CE) heuristic, a widely used online allocation algorithm known to yield optimal regret growth in $n$ when evaluated under the fluid scaling assumption.

Even in the absence of the fluid scaling, the CE heuristics's performance improves with $k$ to eventually match the leading order terms of the optimal dynamic program's performance ratio.

A finer analysis nevertheless reveals that regret can be divergent and large relative to the optimal dynamic program when $n/k \to \infty$.

This highlights the sensitivity in viewing the CE heuristic's performance under the commonly adopted, though subjective, fluid scaling assumption.