  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.

Computer Science and Game Theory