7 research outputs found
Finding Any Nontrivial Coarse Correlated Equilibrium Is Hard
One of the most appealing aspects of the (coarse) correlated equilibrium
concept is that natural dynamics quickly arrive at approximations of such
equilibria, even in games with many players. In addition, there exist
polynomial-time algorithms that compute exact (coarse) correlated equilibria.
In light of these results, a natural question is how good are the (coarse)
correlated equilibria that can arise from any efficient algorithm or dynamics.
In this paper we address this question, and establish strong negative
results. In particular, we show that in multiplayer games that have a succinct
representation, it is NP-hard to compute any coarse correlated equilibrium (or
approximate coarse correlated equilibrium) with welfare strictly better than
the worst possible. The focus on succinct games ensures that the underlying
complexity question is interesting; many multiplayer games of interest are in
fact succinct. Our results imply that, while one can efficiently compute a
coarse correlated equilibrium, one cannot provide any nontrivial welfare
guarantee for the resulting equilibrium, unless P=NP. We show that analogous
hardness results hold for correlated equilibria, and persist under the
egalitarian objective or Pareto optimality.
To complement the hardness results, we develop an algorithmic framework that
identifies settings in which we can efficiently compute an approximate
correlated equilibrium with near-optimal welfare. We use this framework to
develop an efficient algorithm for computing an approximate correlated
equilibrium with near-optimal welfare in aggregative games.Comment: 21 page
Signaling in Bayesian Network Congestion Games: the Subtle Power of Symmetry
Network congestion games are a well-understood model of multi-agent strategic
interactions. Despite their ubiquitous applications, it is not clear whether it
is possible to design information structures to ameliorate the overall
experience of the network users. We focus on Bayesian games with atomic
players, where network vagaries are modeled via a (random) state of nature
which determines the costs incurred by the players. A third-party entity---the
sender---can observe the realized state of the network and exploit this
additional information to send a signal to each player. A natural question is
the following: is it possible for an informed sender to reduce the overall
social cost via the strategic provision of information to players who update
their beliefs rationally? The paper focuses on the problem of computing optimal
ex ante persuasive signaling schemes, showing that symmetry is a crucial
property for its solution. Indeed, we show that an optimal ex ante persuasive
signaling scheme can be computed in polynomial time when players are symmetric
and have affine cost functions. Moreover, the problem becomes NP-hard when
players are asymmetric, even in non-Bayesian settings
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
On the Interplay between Social Welfare and Tractability of Equilibria
Computational tractability and social welfare (aka. efficiency) of equilibria
are two fundamental but in general orthogonal considerations in algorithmic
game theory. Nevertheless, we show that when (approximate) full efficiency can
be guaranteed via a smoothness argument \`a la Roughgarden, Nash equilibria are
approachable under a family of no-regret learning algorithms, thereby enabling
fast and decentralized computation. We leverage this connection to obtain new
convergence results in large games -- wherein the number of players
-- under the well-documented property of full efficiency via smoothness in the
limit. Surprisingly, our framework unifies equilibrium computation in disparate
classes of problems including games with vanishing strategic sensitivity and
two-player zero-sum games, illuminating en route an immediate but overlooked
equivalence between smoothness and a well-studied condition in the optimization
literature known as the Minty property. Finally, we establish that a family of
no-regret dynamics attains a welfare bound that improves over the smoothness
framework while at the same time guaranteeing convergence to the set of coarse
correlated equilibria. We show this by employing the clairvoyant mirror descent
algortihm recently introduced by Piliouras et al.Comment: To appear at NeurIPS 202