18,034 research outputs found
Independent Lazy Better-Response Dynamics on Network Games
International audienceWe study an independent best-response dynamics on network games in which the nodes (players) decide to revise their strategies independently with some probability. We provide several bounds on the convergence time to an equilibrium as a function of this probability, the degree of the network, and the potential of the underlying games. These dynamics are somewhat more suitable for distributed environments than the classical better- and best-response dynamics where players revise their strategies "sequentially'", i.e., no two players revise their strategies simultaneously
Independent Lazy Better-Response Dynamics on Network Games
International audienceWe study an independent best-response dynamics on network games in which the nodes (players) decide to revise their strategies independently with some probability. We provide several bounds on the convergence time to an equilibrium as a function of this probability, the degree of the network, and the potential of the underlying games. These dynamics are somewhat more suitable for distributed environments than the classical better- and best-response dynamics where players revise their strategies "sequentially'", i.e., no two players revise their strategies simultaneously
Metastability of Logit Dynamics for Coordination Games
Logit Dynamics [Blume, Games and Economic Behavior, 1993] are randomized best
response dynamics for strategic games: at every time step a player is selected
uniformly at random and she chooses a new strategy according to a probability
distribution biased toward strategies promising higher payoffs. This process
defines an ergodic Markov chain, over the set of strategy profiles of the game,
whose unique stationary distribution is the long-term equilibrium concept for
the game. However, when the mixing time of the chain is large (e.g.,
exponential in the number of players), the stationary distribution loses its
appeal as equilibrium concept, and the transient phase of the Markov chain
becomes important. It can happen that the chain is "metastable", i.e., on a
time-scale shorter than the mixing time, it stays close to some probability
distribution over the state space, while in a time-scale multiple of the mixing
time it jumps from one distribution to another.
In this paper we give a quantitative definition of "metastable probability
distributions" for a Markov chain and we study the metastability of the logit
dynamics for some classes of coordination games. We first consider a pure
-player coordination game that highlights the distinctive features of our
metastability notion based on distributions. Then, we study coordination games
on the clique without a risk-dominant strategy (which are equivalent to the
well-known Glauber dynamics for the Curie-Weiss model) and coordination games
on a ring (both with and without risk-dominant strategy)
Cooperative learning in multi-agent systems from intermittent measurements
Motivated by the problem of tracking a direction in a decentralized way, we
consider the general problem of cooperative learning in multi-agent systems
with time-varying connectivity and intermittent measurements. We propose a
distributed learning protocol capable of learning an unknown vector from
noisy measurements made independently by autonomous nodes. Our protocol is
completely distributed and able to cope with the time-varying, unpredictable,
and noisy nature of inter-agent communication, and intermittent noisy
measurements of . Our main result bounds the learning speed of our
protocol in terms of the size and combinatorial features of the (time-varying)
networks connecting the nodes
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