Prophet inequalities made easy: Stochastic optimization by pricing nonstochastic inputs

Paul Dütting, Michal Feldman, Thomas Kesselheim, Brendan Lucier

Research output: Contribution to journalArticlepeer-review

Abstract

We present a general framework for stochastic online maximization problems with combinatorial feasibility constraints. The framework establishes prophet inequalities by constructing price-based online approximation algorithms, a natural extension of threshold algorithms for settings beyond binary selection. Our analysis takes the form of an extension theorem: we derive sufficient conditions on prices when all weights are known in advance, then prove that the resulting approximation guarantees extend directly to stochastic settings. Our framework unifies and simplifies much of the existing literature on prophet inequalities and posted price mechanisms and is used to derive new and improved results for combinatorial markets (with and without complements), multidimensional matroids, and sparse packing problems. Finally, we highlight a surprising connection between the smoothness framework for bounding the price of anarchy of mechanisms and our framework, and show that many smooth mechanisms can be recast as posted price mechanisms with comparable performance guarantees.

Original languageEnglish
Pages (from-to)540-582
Number of pages43
JournalSIAM Journal on Computing
Volume49
Issue number3
DOIs
StatePublished - 2020

Keywords

  • Competitive analysis
  • Online algorithms
  • Optimal stopping
  • Posted-price mechanisms
  • Price of anarchy
  • Prophet inequalities

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