TY - GEN
T1 - Inducing approximately optimal flow using truthful mediators
AU - Rogers, Ryan
AU - Roth, Aaron
AU - Ullman, Jonathan
AU - Wu, Zhiwei Steven
N1 - Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2015/6/15
Y1 - 2015/6/15
N2 - We revisit a classic coordination problem from the perspective of mechanism design: how can we coordinate a social welfare maximizing flow in a network congestion game with selfish players? The classical approach, which computes tolls as a function of known demands, fails when the demands are unknown to the mechanism designer, and naively eliciting them does not necessarily yield a truthful mechanism. Instead, we introduce a weak mediator that can provide suggested routes to players and set tolls as a function of reported demands. However, players can choose to ignore or misreport their type to this mediator. Using techniques from differential privacy, we show how to design a weak mediator such that it is an asymptotic ex-post Nash equilibrium for all players to truthfully report their types to the mediator and faithfully follow its suggestion, and that when they do, they end up playing a nearly optimal flow. Notably, our solution works in settings of incomplete information even in the absence of a prior distribution on player types. Along the way, we develop new techniques for privately solving convex programs which may be of independent interest.
AB - We revisit a classic coordination problem from the perspective of mechanism design: how can we coordinate a social welfare maximizing flow in a network congestion game with selfish players? The classical approach, which computes tolls as a function of known demands, fails when the demands are unknown to the mechanism designer, and naively eliciting them does not necessarily yield a truthful mechanism. Instead, we introduce a weak mediator that can provide suggested routes to players and set tolls as a function of reported demands. However, players can choose to ignore or misreport their type to this mediator. Using techniques from differential privacy, we show how to design a weak mediator such that it is an asymptotic ex-post Nash equilibrium for all players to truthfully report their types to the mediator and faithfully follow its suggestion, and that when they do, they end up playing a nearly optimal flow. Notably, our solution works in settings of incomplete information even in the absence of a prior distribution on player types. Along the way, we develop new techniques for privately solving convex programs which may be of independent interest.
KW - Differential privacy
KW - Large games
KW - Routing games
UR - http://www.scopus.com/inward/record.url?scp=84962069168&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962069168&partnerID=8YFLogxK
U2 - 10.1145/2764468.2764509
DO - 10.1145/2764468.2764509
M3 - Conference contribution
AN - SCOPUS:84962069168
T3 - EC 2015 - Proceedings of the 2015 ACM Conference on Economics and Computation
SP - 471
EP - 488
BT - EC 2015 - Proceedings of the 2015 ACM Conference on Economics and Computation
PB - Association for Computing Machinery, Inc
T2 - 16th ACM Conference on Economics and Computation, EC 2015
Y2 - 15 June 2015 through 19 June 2015
ER -