Information Elicitation for Bayesian Auctions

TL;DR

This paper develops mechanisms for Bayesian auctions that effectively aggregate scattered players' distribution knowledge, achieving near-optimal revenue without assuming common prior knowledge.

Contribution

It introduces novel information elicitation mechanisms for auctions with unstructured player knowledge, ensuring truthfulness and near-optimal revenue.

Findings

Mechanisms are 2-step dominant-strategy truthful.

Revenue improves with increased collective player knowledge.

Achieves constant approximation to optimal Bayesian revenue.

Abstract

In this paper we design information elicitation mechanisms for Bayesian auctions. While in Bayesian mechanism design the distributions of the players' private types are often assumed to be common knowledge, information elicitation considers the situation where the players know the distributions better than the decision maker. To weaken the information assumption in Bayesian auctions, we consider an information structure where the knowledge about the distributions is arbitrarily scattered among the players. In such an unstructured information setting, we design mechanisms for unit-demand auctions and additive auctions that aggregate the players' knowledge, generating revenue that are constant approximations to the optimal Bayesian mechanisms with a common prior. Our mechanisms are 2-step dominant-strategy truthful and the revenue increases gracefully with the amount of knowledge the players collectively have.