20,432 research outputs found
On the Convergence Time of the Best Response Dynamics in Player-specific Congestion Games
We study the convergence time of the best response dynamics in
player-specific singleton congestion games. It is well known that this dynamics
can cycle, although from every state a short sequence of best responses to a
Nash equilibrium exists. Thus, the random best response dynamics, which selects
the next player to play a best response uniformly at random, terminates in a
Nash equilibrium with probability one. In this paper, we are interested in the
expected number of best responses until the random best response dynamics
terminates.
As a first step towards this goal, we consider games in which each player can
choose between only two resources. These games have a natural representation as
(multi-)graphs by identifying nodes with resources and edges with players. For
the class of games that can be represented as trees, we show that the
best-response dynamics cannot cycle and that it terminates after O(n^2) steps
where n denotes the number of resources. For the class of games represented as
cycles, we show that the best response dynamics can cycle. However, we also
show that the random best response dynamics terminates after O(n^2) steps in
expectation.
Additionally, we conjecture that in general player-specific singleton
congestion games there exists no polynomial upper bound on the expected number
of steps until the random best response dynamics terminates. We support our
conjecture by presenting a family of games for which simulations indicate a
super-polynomial convergence time
DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion
Non-recurring traffic congestion is caused by temporary disruptions, such as
accidents, sports games, adverse weather, etc. We use data related to real-time
traffic speed, jam factors (a traffic congestion indicator), and events
collected over a year from Nashville, TN to train a multi-layered deep neural
network. The traffic dataset contains over 900 million data records. The
network is thereafter used to classify the real-time data and identify
anomalous operations. Compared with traditional approaches of using statistical
or machine learning techniques, our model reaches an accuracy of 98.73 percent
when identifying traffic congestion caused by football games. Our approach
first encodes the traffic across a region as a scaled image. After that the
image data from different timestamps is fused with event- and time-related
data. Then a crossover operator is used as a data augmentation method to
generate training datasets with more balanced classes. Finally, we use the
receiver operating characteristic (ROC) analysis to tune the sensitivity of the
classifier. We present the analysis of the training time and the inference time
separately
Statics and dynamics of selfish interactions in distributed service systems
We study a class of games which model the competition among agents to access
some service provided by distributed service units and which exhibit congestion
and frustration phenomena when service units have limited capacity. We propose
a technique, based on the cavity method of statistical physics, to characterize
the full spectrum of Nash equilibria of the game. The analysis reveals a large
variety of equilibria, with very different statistical properties. Natural
selfish dynamics, such as best-response, usually tend to large-utility
equilibria, even though those of smaller utility are exponentially more
numerous. Interestingly, the latter actually can be reached by selecting the
initial conditions of the best-response dynamics close to the saturation limit
of the service unit capacities. We also study a more realistic stochastic
variant of the game by means of a simple and effective approximation of the
average over the random parameters, showing that the properties of the
average-case Nash equilibria are qualitatively similar to the deterministic
ones.Comment: 30 pages, 10 figure
Price of Anarchy in Bernoulli Congestion Games with Affine Costs
We consider an atomic congestion game in which each player participates in
the game with an exogenous and known probability , independently
of everybody else, or stays out and incurs no cost. We first prove that the
resulting game is potential. Then, we compute the parameterized price of
anarchy to characterize the impact of demand uncertainty on the efficiency of
selfish behavior. It turns out that the price of anarchy as a function of the
maximum participation probability is a nondecreasing
function. The worst case is attained when players have the same participation
probabilities . For the case of affine costs, we provide an
analytic expression for the parameterized price of anarchy as a function of
. This function is continuous on , is equal to for , and increases towards when . Our work can be interpreted as
providing a continuous transition between the price of anarchy of nonatomic and
atomic games, which are the extremes of the price of anarchy function we
characterize. We show that these bounds are tight and are attained on routing
games -- as opposed to general congestion games -- with purely linear costs
(i.e., with no constant terms).Comment: 29 pages, 6 figure
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
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