18th International Symposium on Dynamic Games and Applications
Grenoble, France, 9 — 12 juillet 2018
18th International Symposium on Dynamic Games and Applications
Grenoble, France, 9 — 12 juillet 2018
Dynamics and Learning in Games 1 (COST Session)
10 juil. 2018 09h00 – 10h40
Salle: salle H.103
Présidée par Panayotis Mertikopoulos
4 présentations
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09h00 - 09h25
Evolutionary Dynamics in Time-Varying Environments
Population games are a very useful mathematical framework to model various strategic settings in biology, economics and engineering. However, the standard model of a population games assumes that the environment in which the agents act is time-invariant. Over long-time horizons this assumption is rather strict. In this paper we discuss a general class of evolutionary game dynamics in population games where the strategic environment is randomly changing according to a finite-state Markov chain. The resulting dynamics are seen to belong to the rich class of piecewise-deterministic Markov processes, which is the most general class of Markovian dynamics without diffusive component. We study existence and uniqueness of solutions and investigate asymptotic properties of the dynamics.
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09h25 - 09h50
On the Douglas–Rachford splitting for generalized Nash equilibrium seeking in aggregative games
We address the generalized Nash equilibrium problem for monotone aggregative games with affine coupling
constraints. We use operator theory to characterize the generalized Nash equilibria of the game as the zeros
of a monotone set-valued operator and we apply the Douglas–Rachford operator splitting to solve the monotone
inclusion problem. Consequently, a semi-decentralized algorithm with convergence guarantee is derived. Our
numerical experience shows that the Douglas–Rachford algorithm usually has faster convergence than projected-
pseudo-gradient algorithms.
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09h50 - 10h15
Multiplicative Weights Update algorithm in congestion games and emergence of chaos
The Multiplicative Weights Update method is a ubiquitous meta-algorithm used e.g. in machine learning, optimization, theoretical computer science and game theory.
We will analyze convergence of MWU for congestion games to exact Nash equilibria. We will show how aggressive behavior (fast learning) of players may result in lack of convergence - appearance of limit cycles and chaotic behavior. -
10h15 - 10h40
Distributed Optimisation of Routing Decisions
In this talk I will present a new distributed algorithm, run in each node
of a communication network, to optimize routing decision in the multi-flot case,
when link delays depend on the load of the link.
When the delays are strictly convex function of the load, the algorithm converges to the unique
optimal total sum of delays for all flows.