Unknown I.I.D. Prophets: Better Bounds, Streaming Algorithms, and a New Impossibility

TL;DR

This paper improves prophet inequality bounds in i.i.d. settings with samples, introduces streaming algorithms for these bounds, and establishes a robust upper bound for distribution-unknown sequences, with implications for algorithmic pricing.

Contribution

It provides new tight bounds for prophet inequalities with samples, develops streaming algorithms for these bounds, and proves the robustness of the $1/e$ upper bound in distribution-uncertain scenarios.

Findings

Improved lower bounds on prophet inequality ratios for various sample sizes.

Development of streaming algorithms achieving these bounds.

Proof that the $1/e$ upper bound remains valid even with distribution uncertainty.

Abstract

A prophet inequality states, for some $\alpha\in[0,1]$, that the expected value achievable by a gambler who sequentially observes random variables $X_1,\dots,X_n$ and selects one of them is at least an $\alpha$ fraction of the maximum value in the sequence. We obtain three distinct improvements for a setting that was first studied by Correa et al. (EC, 2019) and is particularly relevant to modern applications in algorithmic pricing. In this setting, the random variables are i.i.d. from an unknown distribution and the gambler has access to an additional $\beta n$ samples for some $\beta\geq 0$. We first give improved lower bounds on $\alpha$ for a wide range of values of $\beta$; specifically, $\alpha\geq(1+\beta)/e$ when $\beta\leq 1/(e-1)$, which is tight, and $\alpha\geq 0.648$ when $\beta=1$, which improves on a bound of around $0.635$ due to Correa et al. (SODA, 2020). Adding to their practical appeal, specifically in the context of algorithmic pricing, we then show that the new bounds can be obtained even in a streaming model of computation and thus in situations where the use of relevant data is complicated by the sheer amount of data available. We finally establish that the upper bound of $1/e$ for the case without samples is robust to additional information about the distribution, and applies also to sequences of i.i.d. random variables whose distribution is itself drawn, according to a known distribution, from a finite set of known candidate distributions. This implies a tight prophet inequality for exchangeable sequences of random variables, answering a question of Hill and Kertz (Contemporary Mathematics, 1992), but leaves open the possibility of better guarantees when the number of candidate distributions is small, a setting we believe is of strong interest to applications.