2015/04/05 by Ilan Reuven Cohen, Alon Eden, Cohen, Ilan Reuven +5 · 1 citation
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Auction Theory and Applications #Game Theory and Voting Systems #Optimization and Search Problems #cs.DS #cs.GT
paper · pdf · doi:10.48550/arxiv.1504.01093
Appeared in the ACM-SIAM Symposium on Discrete Algorithms (SODA), 2015
arxiv created 2015/04/05 · arxiv updated 2015/04/07
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.