  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.
