104 research outputs found
Expanding Commitment to Those Who Served
The opening of Golden Gate University School of Law’s Veterans Legal Advocacy Center (VLAC) this fall represents a major stride in the university’s commitment to serve military veterans by helping some with their legal issues and by supporting others who wish to join the legal profession — while also offering GGU Law students an opportunity to assist with veterans’ legal needs
Expanding Commitment to Those Who Served
The opening of Golden Gate University School of Law’s Veterans Legal Advocacy Center (VLAC) this fall represents a major stride in the university’s commitment to serve military veterans by helping some with their legal issues and by supporting others who wish to join the legal profession — while also offering GGU Law students an opportunity to assist with veterans’ legal needs
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
Centrality measures for graphons: Accounting for uncertainty in networks
As relational datasets modeled as graphs keep increasing in size and their
data-acquisition is permeated by uncertainty, graph-based analysis techniques
can become computationally and conceptually challenging. In particular, node
centrality measures rely on the assumption that the graph is perfectly known --
a premise not necessarily fulfilled for large, uncertain networks. Accordingly,
centrality measures may fail to faithfully extract the importance of nodes in
the presence of uncertainty. To mitigate these problems, we suggest a
statistical approach based on graphon theory: we introduce formal definitions
of centrality measures for graphons and establish their connections to
classical graph centrality measures. A key advantage of this approach is that
centrality measures defined at the modeling level of graphons are inherently
robust to stochastic variations of specific graph realizations. Using the
theory of linear integral operators, we define degree, eigenvector, Katz and
PageRank centrality functions for graphons and establish concentration
inequalities demonstrating that graphon centrality functions arise naturally as
limits of their counterparts defined on sequences of graphs of increasing size.
The same concentration inequalities also provide high-probability bounds
between the graphon centrality functions and the centrality measures on any
sampled graph, thereby establishing a measure of uncertainty of the measured
centrality score. The same concentration inequalities also provide
high-probability bounds between the graphon centrality functions and the
centrality measures on any sampled graph, thereby establishing a measure of
uncertainty of the measured centrality score.Comment: Authors ordered alphabetically, all authors contributed equally. 21
pages, 7 figure
Nash and Wardrop equilibria in aggregative games with coupling constraints
We consider the framework of aggregative games, in which the cost function of
each agent depends on his own strategy and on the average population strategy.
As first contribution, we investigate the relations between the concepts of
Nash and Wardrop equilibrium. By exploiting a characterization of the two
equilibria as solutions of variational inequalities, we bound their distance
with a decreasing function of the population size. As second contribution, we
propose two decentralized algorithms that converge to such equilibria and are
capable of coping with constraints coupling the strategies of different agents.
Finally, we study the applications of charging of electric vehicles and of
route choice on a road network.Comment: IEEE Trans. on Automatic Control (Accepted without changes). The
first three authors contributed equall
Gradient Dynamics in Linear Quadratic Network Games with Time-Varying Connectivity and Population Fluctuation
In this paper, we consider a learning problem among non-cooperative agents
interacting in a time-varying system. Specifically, we focus on repeated linear
quadratic network games, in which the network of interactions changes with time
and agents may not be present at each iteration. To get tractability, we assume
that at each iteration, the network of interactions is sampled from an
underlying random network model and agents participate at random with a given
probability. Under these assumptions, we consider a gradient-based learning
algorithm and establish almost sure convergence of the agents' strategies to
the Nash equilibrium of the game played over the expected network.
Additionally, we prove, in the large population regime, that the learned
strategy is an -Nash equilibrium for each stage game with high
probability. We validate our results over an online market application.Comment: 8 pages, 2 figures, Extended version of the original paper to appear
in the proceedings of the 2023 IEEE Conference on Decision and Control (CDC
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