166 research outputs found
Mean Field Equilibrium in Dynamic Games with Complementarities
We study a class of stochastic dynamic games that exhibit strategic
complementarities between players; formally, in the games we consider, the
payoff of a player has increasing differences between her own state and the
empirical distribution of the states of other players. Such games can be used
to model a diverse set of applications, including network security models,
recommender systems, and dynamic search in markets. Stochastic games are
generally difficult to analyze, and these difficulties are only exacerbated
when the number of players is large (as might be the case in the preceding
examples).
We consider an approximation methodology called mean field equilibrium to
study these games. In such an equilibrium, each player reacts to only the long
run average state of other players. We find necessary conditions for the
existence of a mean field equilibrium in such games. Furthermore, as a simple
consequence of this existence theorem, we obtain several natural monotonicity
properties. We show that there exist a "largest" and a "smallest" equilibrium
among all those where the equilibrium strategy used by a player is
nondecreasing, and we also show that players converge to each of these
equilibria via natural myopic learning dynamics; as we argue, these dynamics
are more reasonable than the standard best response dynamics. We also provide
sensitivity results, where we quantify how the equilibria of such games move in
response to changes in parameters of the game (e.g., the introduction of
incentives to players).Comment: 56 pages, 5 figure
Online Active Linear Regression via Thresholding
We consider the problem of online active learning to collect data for
regression modeling. Specifically, we consider a decision maker with a limited
experimentation budget who must efficiently learn an underlying linear
population model. Our main contribution is a novel threshold-based algorithm
for selection of most informative observations; we characterize its performance
and fundamental lower bounds. We extend the algorithm and its guarantees to
sparse linear regression in high-dimensional settings. Simulations suggest the
algorithm is remarkably robust: it provides significant benefits over passive
random sampling in real-world datasets that exhibit high nonlinearity and high
dimensionality --- significantly reducing both the mean and variance of the
squared error.Comment: Published in AAAI 201
Competition in Wireless Systems via Bayesian Interference Games
We study competition between wireless devices with incomplete information
about their opponents. We model such interactions as Bayesian interference
games. Each wireless device selects a power profile over the entire available
bandwidth to maximize its data rate. Such competitive models represent
situations in which several wireless devices share spectrum without any central
authority or coordinated protocol.
In contrast to games where devices have complete information about their
opponents, we consider scenarios where the devices are unaware of the
interference they cause to other devices. Such games, which are modeled as
Bayesian games, can exhibit significantly different equilibria. We first
consider a simple scenario of simultaneous move games, where we show that the
unique Bayes-Nash equilibrium is where both devices spread their power equally
across the entire bandwidth. We then extend this model to a two-tiered spectrum
sharing case where users act sequentially. Here one of the devices, called the
primary user, is the owner of the spectrum and it selects its power profile
first. The second device (called the secondary user) then responds by choosing
a power profile to maximize its Shannon capacity. In such sequential move
games, we show that there exist equilibria in which the primary user obtains a
higher data rate by using only a part of the bandwidth.
In a repeated Bayesian interference game, we show the existence of reputation
effects: an informed primary user can bluff to prevent spectrum usage by a
secondary user who suffers from lack of information about the channel gains.
The resulting equilibrium can be highly inefficient, suggesting that
competitive spectrum sharing is highly suboptimal.Comment: 30 pages, 3 figure
Information Theoretic Operating Regimes of Large Wireless Networks
In analyzing the point-to-point wireless channel, insights about two
qualitatively different operating regimes--bandwidth- and power-limited--have
proven indispensable in the design of good communication schemes. In this
paper, we propose a new scaling law formulation for wireless networks that
allows us to develop a theory that is analogous to the point-to-point case. We
identify fundamental operating regimes of wireless networks and derive
architectural guidelines for the design of optimal schemes.
Our analysis shows that in a given wireless network with arbitrary size,
area, power, bandwidth, etc., there are three parameters of importance: the
short-distance SNR, the long-distance SNR, and the power path loss exponent of
the environment. Depending on these parameters we identify four qualitatively
different regimes. One of these regimes is especially interesting since it is
fundamentally a consequence of the heterogeneous nature of links in a network
and does not occur in the point-to-point case; the network capacity is {\em
both} power and bandwidth limited. This regime has thus far remained hidden due
to the limitations of the existing formulation. Existing schemes, either
multihop transmission or hierarchical cooperation, fail to achieve capacity in
this regime; we propose a new hybrid scheme that achieves capacity.Comment: 12 pages, 5 figures, to appear in IEEE Transactions on Information
Theor
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