7,838 research outputs found
Translation invariant mean field games with common noise
This note highlights a special class of mean field games in which the
coefficients satisfy a convolution-type structural condition. A mean field game
of this type with common noise is related to a certain mean field game without
common noise by a simple transformation, which permits a tractable construction
of a solution of the problem with common noise from a solution of the problem
without
Survival of dominated strategies under evolutionary dynamics
We prove that any deterministic evolutionary dynamic satisfying four mild requirements fails to eliminate strictly dominated strategies in some games. We also show that existing elimination results for evolutionary dynamics are not robust to small changes in the specifications of the dynamics. Numerical analysis reveals that dominated strategies can persist at nontrivial frequencies even when the level of domination is not small.Evolutionary game theory, evolutionary game dynamics, nonconvergnece, dominated strategies
Game-theoretical control with continuous action sets
Motivated by the recent applications of game-theoretical learning techniques
to the design of distributed control systems, we study a class of control
problems that can be formulated as potential games with continuous action sets,
and we propose an actor-critic reinforcement learning algorithm that provably
converges to equilibrium in this class of problems. The method employed is to
analyse the learning process under study through a mean-field dynamical system
that evolves in an infinite-dimensional function space (the space of
probability distributions over the players' continuous controls). To do so, we
extend the theory of finite-dimensional two-timescale stochastic approximation
to an infinite-dimensional, Banach space setting, and we prove that the
continuous dynamics of the process converge to equilibrium in the case of
potential games. These results combine to give a provably-convergent learning
algorithm in which players do not need to keep track of the controls selected
by the other agents.Comment: 19 page
Asymptotic Control for a Class of Piecewise Deterministic Markov Processes Associated to Temperate Viruses
We aim at characterizing the asymptotic behavior of value functions in the
control of piece-wise deterministic Markov processes (PDMP) of switch type
under nonexpansive assumptions. For a particular class of processes inspired by
temperate viruses, we show that uniform limits of discounted problems as the
discount decreases to zero and time-averaged problems as the time horizon
increases to infinity exist and coincide. The arguments allow the limit value
to depend on initial configuration of the system and do not require dissipative
properties on the dynamics. The approach strongly relies on viscosity
techniques, linear programming arguments and coupling via random measures
associated to PDMP. As an intermediate step in our approach, we present the
approximation of discounted value functions when using piecewise constant (in
time) open-loop policies.Comment: In this revised version, statements of the main results are gathered
in Section 3. Proofs of the main results (Theorem 4 and Theorem 7) make the
object of separate sections (Section 5, resp. Section 6). The biological
example makes the object of Section 4. Notations are gathered in Subsection
2.1. This is the final version to be published in SICO
Towards an exact reconstruction of a time-invariant model from time series data
Dynamic processes in biological systems may be profiled by measuring system properties over time. One way of representing such time series data is through weighted interaction networks, where the nodes in the network represent the measurables and the weighted edges represent interactions between any pair of nodes. Construction of these network models from time series data may involve seeking a robust data-consistent and time-invariant model to approximate and describe system dynamics. Many problems in mathematics, systems biology and physics can be recast into this form and may require finding the most consistent solution to a set of first order differential equations. This is especially challenging in cases where the number of data points is less than or equal to the number of measurables. We present a novel computational method for network reconstruction with limited time series data. To test our method, we use artificial time series data generated from known network models. We then attempt to reconstruct the original network from the time series data alone. We find good agreement between the original and predicted networks
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