8,573 research outputs found
Decentralized Convergence to Nash Equilibria in Constrained Deterministic Mean Field Control
This paper considers decentralized control and optimization methodologies for
large populations of systems, consisting of several agents with different
individual behaviors, constraints and interests, and affected by the aggregate
behavior of the overall population. For such large-scale systems, the theory of
aggregative and mean field games has been established and successfully applied
in various scientific disciplines. While the existing literature addresses the
case of unconstrained agents, we formulate deterministic mean field control
problems in the presence of heterogeneous convex constraints for the individual
agents, for instance arising from agents with linear dynamics subject to convex
state and control constraints. We propose several model-free feedback
iterations to compute in a decentralized fashion a mean field Nash equilibrium
in the limit of infinite population size. We apply our methods to the
constrained linear quadratic deterministic mean field control problem and to
the constrained mean field charging control problem for large populations of
plug-in electric vehicles.Comment: IEEE Trans. on Automatic Control (cond. accepted
Controlled Matching Game for Resource Allocation and User Association in WLANs
In multi-rate IEEE 802.11 WLANs, the traditional user association based on
the strongest received signal and the well known anomaly of the MAC protocol
can lead to overloaded Access Points (APs), and poor or heterogeneous
performance. Our goal is to propose an alternative game-theoretic approach for
association. We model the joint resource allocation and user association as a
matching game with complementarities and peer effects consisting of selfish
players solely interested in their individual throughputs. Using recent
game-theoretic results we first show that various resource sharing protocols
actually fall in the scope of the set of stability-inducing resource allocation
schemes. The game makes an extensive use of the Nash bargaining and some of its
related properties that allow to control the incentives of the players. We show
that the proposed mechanism can greatly improve the efficiency of 802.11 with
heterogeneous nodes and reduce the negative impact of peer effects such as its
MAC anomaly. The mechanism can be implemented as a virtual connectivity
management layer to achieve efficient APs-user associations without
modification of the MAC layer
The Governance of Services
The problem of assessing a system of governance for composite services in the social economy is approached by means of original methods.The main innovation is that the welfare structure of a society is separated from the legal transaction- or institutional structure.As both the various types of services and the various modes of management are defined in terms of relations between sets of persons, these structures can be compared and the performance of a managementsystem can be assessed.The dynamics of a wide range of hybrid forms of organization - between market and hierarchy - is analyzed in this framework. The approach elaborates on the new institutional economics, and the social theory of micromotives and macrobehavior in exchange and transactions.welfare and transactions;hybrid organizations;typology of services;typology of modes of governance;institutional economics
Navigation of brain networks
Understanding the mechanisms of neural communication in large-scale brain
networks remains a major goal in neuroscience. We investigated whether
navigation is a parsimonious routing model for connectomics. Navigating a
network involves progressing to the next node that is closest in distance to a
desired destination. We developed a measure to quantify navigation efficiency
and found that connectomes in a range of mammalian species (human, mouse and
macaque) can be successfully navigated with near-optimal efficiency (>80% of
optimal efficiency for typical connection densities). Rewiring network topology
or repositioning network nodes resulted in 45%-60% reductions in navigation
performance. Specifically, we found that brain networks cannot be progressively
rewired (randomized or clusterized) to result in topologies with significantly
improved navigation performance. Navigation was also found to: i) promote a
resource-efficient distribution of the information traffic load, potentially
relieving communication bottlenecks; and, ii) explain significant variation in
functional connectivity. Unlike prevalently studied communication strategies in
connectomics, navigation does not mandate biologically unrealistic assumptions
about global knowledge of network topology. We conclude that the wiring and
spatial embedding of brain networks is conducive to effective decentralized
communication. Graph-theoretic studies of the connectome should consider
measures of network efficiency and centrality that are consistent with
decentralized models of neural communication
Distributed Learning in Multi-Armed Bandit with Multiple Players
We formulate and study a decentralized multi-armed bandit (MAB) problem.
There are M distributed players competing for N independent arms. Each arm,
when played, offers i.i.d. reward according to a distribution with an unknown
parameter. At each time, each player chooses one arm to play without exchanging
observations or any information with other players. Players choosing the same
arm collide, and, depending on the collision model, either no one receives
reward or the colliding players share the reward in an arbitrary way. We show
that the minimum system regret of the decentralized MAB grows with time at the
same logarithmic order as in the centralized counterpart where players act
collectively as a single entity by exchanging observations and making decisions
jointly. A decentralized policy is constructed to achieve this optimal order
while ensuring fairness among players and without assuming any pre-agreement or
information exchange among players. Based on a Time Division Fair Sharing
(TDFS) of the M best arms, the proposed policy is constructed and its order
optimality is proven under a general reward model. Furthermore, the basic
structure of the TDFS policy can be used with any order-optimal single-player
policy to achieve order optimality in the decentralized setting. We also
establish a lower bound on the system regret growth rate for a general class of
decentralized polices, to which the proposed policy belongs. This problem finds
potential applications in cognitive radio networks, multi-channel communication
systems, multi-agent systems, web search and advertising, and social networks.Comment: 31 pages, 8 figures, revised paper submitted to IEEE Transactions on
Signal Processing, April, 2010, the pre-agreement in the decentralized TDFS
policy is eliminated to achieve a complete decentralization among player
Stochastic network formation and homophily
This is a chapter of the forthcoming Oxford Handbook on the Economics of
Networks
- âŠ