Models for Truthful Online Double Auctions

TL;DR

This paper introduces a general method for designing truthful online double auctions that prevent agents from benefiting through misreporting, and demonstrates the importance of dynamic pricing for market efficiency in volatile or low-volume settings.

Contribution

It proposes a flexible framework for truthful dynamic double auctions, generalizing McAfee's auction to dynamic environments, and evaluates their performance empirically.

Findings

Dynamic pricing enhances market efficiency in volatile markets.

Truthful auction designs prevent strategic misreporting.

Different DAs vary in allocative and agent surplus outcomes.

Abstract

Online double auctions (DAs) model a dynamic two-sided matching problem with private information and self-interest, and are relevant for dynamic resource and task allocation problems. We present a general method to design truthful DAs, such that no agent can benefit from misreporting its arrival time, duration, or value. The family of DAs is parameterized by a pricing rule, and includes a generalization of McAfee's truthful DA to this dynamic setting. We present an empirical study, in which we study the allocative-surplus and agent surplus for a number of different DAs. Our results illustrate that dynamic pricing rules are important to provide good market efficiency for markets with high volatility or low volume.