Bayesian algorithmic mechanism design

Abstract

This article surveys recent work with an algorithmic flavor in Bayesian mechanism design. Bayesian mechanism design involves optimization in economic settings where the designer possesses some stochastic information about the input. Recent years have witnessed huge advances in our knowledge and understanding of algorithmic techniques for Bayesian mechanism design problems. These include, for example, revenue maximization in settings where buyers have multi-dimensional preferences, optimization of non-linear objectives such as makespan, and generic reductions from mechanism design to algorithm design. However, a number of tantalizing questions remain un-solved. This article is meant to serve as an introduction to Bayesian mechanism design for a novice, as well as a starting point for a broader literature search for an experienced researcher.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 29 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.