Michel Benaïm | University of Neuchâtel (original) (raw)

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Papers by Michel Benaïm

Research paper thumbnail of Random switching between vector fields having a common zero

Annals of Applied Probability, Feb 1, 2019

Research paper thumbnail of Convergence of adaptive biasing potential methods for diffusions

Comptes Rendus Mathematique, Aug 1, 2016

Research paper thumbnail of Asymptotic pseudotrajectories and chain recurrent flows, with applications

Journal of Dynamics and Differential Equations, 1996

We present a general framework to study compact limit sets of trajectories for a class of nonauto... more We present a general framework to study compact limit sets of trajectories for a class of nonautonomous systems, including asymptotically autonomous differential equations, certain stochastic differential equations, stochastic approximarion processes with decreasing gain, and fictitious plays in game theory. Such limit sets are shown to be internally chain recurrent, and conversely,

Research paper thumbnail of Irreducibility

Research paper thumbnail of Self-repelling diffusions on a Riemannian manifold

Probability Theory and Related Fields, May 6, 2016

Research paper thumbnail of Self-repelling diffusions via an infinite dimensional approach

Stochastics And Partial Differential Equations: Analysis And Computations, Sep 8, 2015

Research paper thumbnail of Vertex-reinforced random walks and a conjecture of Pemantle

Annals of Probability, 1997

Research paper thumbnail of Harris and Positive Recurrence

Research paper thumbnail of A user-friendly condition for exponential ergodicity in randomly switched environments

Electronic Communications in Probability, 2018

Research paper thumbnail of Learning in games with unstable equilibria

Journal of Economic Theory, Jul 1, 2009

We propose a new concept for the analysis of games, the TASP, which gives a precise prediction ab... more We propose a new concept for the analysis of games, the TASP, which gives a precise prediction about non-equilibrium play in games whose Nash equilibria are mixed and are unstable under fictitious play-like learning processes. We show that, when players learn using weighted stochastic fictitious play and so place greater weight on more recent experience, the time average of play often converges in these "unstable" games, even while mixed strategies and beliefs continue to cycle. This time average, the TASP, is related to the best response cycle first identified by . Though conceptually distinct from Nash equilibrium, for many games the TASP is close enough to Nash to create the appearance of convergence to equilibrium. In other games, the TASP may be quite distant from Nash.

Research paper thumbnail of Stochastic approximation of quasi-stationary distributions on compact spaces and applications

HAL (Le Centre pour la Communication Scientifique Directe), Aug 9, 2018

Research paper thumbnail of Convergence analysis of adaptive biasing potential methods for diffusion processes

Communications in Mathematical Sciences, 2019

Research paper thumbnail of A Class Of Mean Field Interaction Models for Computer and Communication Systems

Research paper thumbnail of On strict convergence of stochastic gradients

arXiv (Cornell University), Oct 11, 2016

Research paper thumbnail of Markov Chains

Research paper thumbnail of Countable Markov Chains

Research paper thumbnail of Analysis of an Adaptive Biasing Force method based on self-interacting dynamics

arXiv (Cornell University), Oct 10, 2019

This article fills a gap in the mathematical analysis of Adaptive Biasing algorithms, which are e... more This article fills a gap in the mathematical analysis of Adaptive Biasing algorithms, which are extensively used in molecular dynamics computations. Given a reaction coordinate, ideally, the bias in the overdamped Langevin dynamics would be given by the gradient of the associated free energy function, which is unknown. We consider an adaptive biased version of the overdamped dynamics, where the bias depends on the past of the trajectory and is designed to approximate the free energy. The main result of this article is the consistency and efficiency of this approach. More precisely we prove the almost sure convergence of the bias as time goes to infinity, and that the limit is close to the ideal bias, as an auxiliary parameter of the algorithm goes to 0. The proof is based on interpreting the process as a self-interacting dynamics, and on the study of a non-trivial fixed point problem for the limiting flow obtained using the ODE method.

Research paper thumbnail of Markov Chains on Metric Spaces

Research paper thumbnail of Differential and stochastic epidemic models

Research paper thumbnail of Erratum: Qualitative properties of certain piecewise deterministic Markov processes

Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2019

Research paper thumbnail of Random switching between vector fields having a common zero

Annals of Applied Probability, Feb 1, 2019

Research paper thumbnail of Convergence of adaptive biasing potential methods for diffusions

Comptes Rendus Mathematique, Aug 1, 2016

Research paper thumbnail of Asymptotic pseudotrajectories and chain recurrent flows, with applications

Journal of Dynamics and Differential Equations, 1996

We present a general framework to study compact limit sets of trajectories for a class of nonauto... more We present a general framework to study compact limit sets of trajectories for a class of nonautonomous systems, including asymptotically autonomous differential equations, certain stochastic differential equations, stochastic approximarion processes with decreasing gain, and fictitious plays in game theory. Such limit sets are shown to be internally chain recurrent, and conversely,

Research paper thumbnail of Irreducibility

Research paper thumbnail of Self-repelling diffusions on a Riemannian manifold

Probability Theory and Related Fields, May 6, 2016

Research paper thumbnail of Self-repelling diffusions via an infinite dimensional approach

Stochastics And Partial Differential Equations: Analysis And Computations, Sep 8, 2015

Research paper thumbnail of Vertex-reinforced random walks and a conjecture of Pemantle

Annals of Probability, 1997

Research paper thumbnail of Harris and Positive Recurrence

Research paper thumbnail of A user-friendly condition for exponential ergodicity in randomly switched environments

Electronic Communications in Probability, 2018

Research paper thumbnail of Learning in games with unstable equilibria

Journal of Economic Theory, Jul 1, 2009

We propose a new concept for the analysis of games, the TASP, which gives a precise prediction ab... more We propose a new concept for the analysis of games, the TASP, which gives a precise prediction about non-equilibrium play in games whose Nash equilibria are mixed and are unstable under fictitious play-like learning processes. We show that, when players learn using weighted stochastic fictitious play and so place greater weight on more recent experience, the time average of play often converges in these "unstable" games, even while mixed strategies and beliefs continue to cycle. This time average, the TASP, is related to the best response cycle first identified by . Though conceptually distinct from Nash equilibrium, for many games the TASP is close enough to Nash to create the appearance of convergence to equilibrium. In other games, the TASP may be quite distant from Nash.

Research paper thumbnail of Stochastic approximation of quasi-stationary distributions on compact spaces and applications

HAL (Le Centre pour la Communication Scientifique Directe), Aug 9, 2018

Research paper thumbnail of Convergence analysis of adaptive biasing potential methods for diffusion processes

Communications in Mathematical Sciences, 2019

Research paper thumbnail of A Class Of Mean Field Interaction Models for Computer and Communication Systems

Research paper thumbnail of On strict convergence of stochastic gradients

arXiv (Cornell University), Oct 11, 2016

Research paper thumbnail of Markov Chains

Research paper thumbnail of Countable Markov Chains

Research paper thumbnail of Analysis of an Adaptive Biasing Force method based on self-interacting dynamics

arXiv (Cornell University), Oct 10, 2019

This article fills a gap in the mathematical analysis of Adaptive Biasing algorithms, which are e... more This article fills a gap in the mathematical analysis of Adaptive Biasing algorithms, which are extensively used in molecular dynamics computations. Given a reaction coordinate, ideally, the bias in the overdamped Langevin dynamics would be given by the gradient of the associated free energy function, which is unknown. We consider an adaptive biased version of the overdamped dynamics, where the bias depends on the past of the trajectory and is designed to approximate the free energy. The main result of this article is the consistency and efficiency of this approach. More precisely we prove the almost sure convergence of the bias as time goes to infinity, and that the limit is close to the ideal bias, as an auxiliary parameter of the algorithm goes to 0. The proof is based on interpreting the process as a self-interacting dynamics, and on the study of a non-trivial fixed point problem for the limiting flow obtained using the ODE method.

Research paper thumbnail of Markov Chains on Metric Spaces

Research paper thumbnail of Differential and stochastic epidemic models

Research paper thumbnail of Erratum: Qualitative properties of certain piecewise deterministic Markov processes

Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2019

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