4,049 research outputs found
A Survey on Approximation Mechanism Design without Money for Facility Games
In a facility game one or more facilities are placed in a metric space to
serve a set of selfish agents whose addresses are their private information. In
a classical facility game, each agent wants to be as close to a facility as
possible, and the cost of an agent can be defined as the distance between her
location and the closest facility. In an obnoxious facility game, each agent
wants to be far away from all facilities, and her utility is the distance from
her location to the facility set. The objective of each agent is to minimize
her cost or maximize her utility. An agent may lie if, by doing so, more
benefit can be obtained. We are interested in social choice mechanisms that do
not utilize payments. The game designer aims at a mechanism that is
strategy-proof, in the sense that any agent cannot benefit by misreporting her
address, or, even better, group strategy-proof, in the sense that any coalition
of agents cannot all benefit by lying. Meanwhile, it is desirable to have the
mechanism to be approximately optimal with respect to a chosen objective
function. Several models for such approximation mechanism design without money
for facility games have been proposed. In this paper we briefly review these
models and related results for both deterministic and randomized mechanisms,
and meanwhile we present a general framework for approximation mechanism design
without money for facility games
Asymptotically Truthful Equilibrium Selection in Large Congestion Games
Studying games in the complete information model makes them analytically
tractable. However, large player interactions are more realistically
modeled as games of incomplete information, where players may know little to
nothing about the types of other players. Unfortunately, games in incomplete
information settings lose many of the nice properties of complete information
games: the quality of equilibria can become worse, the equilibria lose their
ex-post properties, and coordinating on an equilibrium becomes even more
difficult. Because of these problems, we would like to study games of
incomplete information, but still implement equilibria of the complete
information game induced by the (unknown) realized player types.
This problem was recently studied by Kearns et al. and solved in large games
by means of introducing a weak mediator: their mediator took as input reported
types of players, and output suggested actions which formed a correlated
equilibrium of the underlying game. Players had the option to play
independently of the mediator, or ignore its suggestions, but crucially, if
they decided to opt-in to the mediator, they did not have the power to lie
about their type. In this paper, we rectify this deficiency in the setting of
large congestion games. We give, in a sense, the weakest possible mediator: it
cannot enforce participation, verify types, or enforce its suggestions.
Moreover, our mediator implements a Nash equilibrium of the complete
information game. We show that it is an (asymptotic) ex-post equilibrium of the
incomplete information game for all players to use the mediator honestly, and
that when they do so, they end up playing an approximate Nash equilibrium of
the induced complete information game. In particular, truthful use of the
mediator is a Bayes-Nash equilibrium in any Bayesian game for any prior.Comment: The conference version of this paper appeared in EC 2014. This
manuscript has been merged and subsumed by the preprint "Robust Mediators in
Large Games": http://arxiv.org/abs/1512.0269
Facility location with double-peaked preference
We study the problem of locating a single facility on a real line based on
the reports of self-interested agents, when agents have double-peaked
preferences, with the peaks being on opposite sides of their locations. We
observe that double-peaked preferences capture real-life scenarios and thus
complement the well-studied notion of single-peaked preferences. We mainly
focus on the case where peaks are equidistant from the agents' locations and
discuss how our results extend to more general settings. We show that most of
the results for single-peaked preferences do not directly apply to this
setting; this makes the problem essentially more challenging. As our main
contribution, we present a simple truthful-in-expectation mechanism that
achieves an approximation ratio of 1+b/c for both the social and the maximum
cost, where b is the distance of the agent from the peak and c is the minimum
cost of an agent. For the latter case, we provide a 3/2 lower bound on the
approximation ratio of any truthful-in-expectation mechanism. We also study
deterministic mechanisms under some natural conditions, proving lower bounds
and approximation guarantees. We prove that among a large class of reasonable
mechanisms, there is no deterministic mechanism that outperforms our
truthful-in-expectation mechanism
- …