  We consider dynamic pricing schemes in online settings where selfish agents
generate online events. Previous work on online mechanisms has dealt almost
entirely with the goal of maximizing social welfare or revenue in an auction
settings. This paper deals with quite general settings and minimizing social
costs. We show that appropriately computed posted prices allow one to achieve
essentially the same performance as the best online algorithm. This holds in a
wide variety of settings. Unlike online algorithms that learn about the event,
and then make enforceable decisions, prices are posted without knowing the
future events or even the current event, and are thus inherently dominant
strategy incentive compatible.
  In particular we show that one can give efficient posted price mechanisms for
metrical task systems, some instances of the $k$-server problem, and metrical
matching problems. We give both deterministic and randomized algorithms. Such
posted price mechanisms decrease the social cost dramatically over selfish
behavior where no decision incurs a charge. One alluring application of this is
reducing the social cost of free parking exponentially.
