The Generalized Second Price (GSP) auction is the primary auction used for
monetizing the use of the Internet. It is well-known that truthtelling is not a
dominant strategy in this auction and that inefficient equilibria can arise. In
this paper we study the space of equilibria in GSP, and quantify the efficiency
loss that can arise in equilibria under a wide range of sources of uncertainty,
as well as in the full information setting. The traditional Bayesian game
models uncertainty in the valuations (types) of the participants.
We study the design of mechanisms in combinatorial auction domains. We focus
on settings where the auction is repeated, motivated by auctions for licenses
or advertising space. We consider models of agent behaviour in which they
either apply common learning techniques to minimize the regret of their bidding
strategies, or apply short-sighted best-response strategies. We ask: when can a
black-box approximation algorithm for the base auction problem be converted
into a mechanism that approximately preserves the original algorithm's
approximation factor on average over many iterations?
The principal problem in algorithmic mechanism design is in merging the
incentive constraints imposed by selfish behavior with the algorithmic
constraints imposed by computational intractability.
We consider mechanisms for utilitarian combinatorial allocation problems,
where agents are not assumed to be single-minded. This class of problems
includes combinatorial auctions, multi-unit auctions, unsplittable flow
problems, profit-maximizing scheduling, and others. We study the price of
anarchy for such mechanisms, which is a bound on the approximation ratio
obtained at any mixed Nash equilibrium. We demonstrate that a broad class of
greedy approximation algorithms can be implemented as mechanisms for which the
price of anarchy nearly matches the performance of the original algorithm.