Prophet Inequality with Conservative Prediction

Abstract

Prophet inequalities compare online stopping strategies against an omniscient "prophet" using distributional knowledge. In this work, we augment this model with a conservative prediction of the maximum realized value. We quantify the quality of this prediction using a parameter $α\in [0,1]$, ranging from inaccurate to perfect. Our goal is to improve performance when predictions are accurate (consistency) while maintaining theoretical guarantees when they are not (robustness). We propose a threshold-based strategy oblivious to $α$ (i.e., with $α$ unknown to the algorithm) that matches the classic competitive ratio of $1/2$ at $α=0$ and improves smoothly to $3/4$ at $α=1$. We further prove that simultaneously achieving better than $3/4$ at $α=1$ while maintaining $1/2$ at $α=0$ is impossible. Finally, when $α$ is known in advance, we present a strategy achieving a tight competitive ratio of $\frac{1}{2-α}$.

Figures (1)

• Figure 1: Comparison of competitive ratios for known vs. unknown prediction quality.

Theorems & Definitions (52)

• Claim 2.1
• proof
• Theorem 3.1
• Lemma 3.2
• Definition 3.3: Sorted scaled Bernoulli instance
• Lemma 3.4
• Lemma 3.5
• Lemma 3.6
• Claim 3.7
• proof