Inducing approximately optimal flow using truthful mediators
Proceedings of the sixteenth ACM conference on Economics and computation, 2015•dl.acm.org
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 …
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 …
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.
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