37,617 research outputs found
Mean-Field Games of Finite-Fuel Capacity Expansion with Singular Controls
We study Nash equilibria for a sequence of symmetric -player stochastic
games of finite-fuel capacity expansion with singular controls and their
mean-field game (MFG) counterpart. We construct a solution of the MFG via a
simple iterative scheme that produces an optimal control in terms of a
Skorokhod reflection at a (state-dependent) surface that splits the state space
into action and inaction regions. We then show that a solution of the MFG of
capacity expansion induces approximate Nash equilibria for the -player games
with approximation error going to zero as tends to infinity.
Our analysis relies entirely on probabilistic methods and extends the
well-known connection between singular stochastic control and optimal stopping
to a mean-field framework
A probabilistic weak formulation of mean field games and applications
Mean field games are studied by means of the weak formulation of stochastic
optimal control. This approach allows the mean field interactions to enter
through both state and control processes and take a form which is general
enough to include rank and nearest-neighbor effects. Moreover, the data may
depend discontinuously on the state variable, and more generally its entire
history. Existence and uniqueness results are proven, along with a procedure
for identifying and constructing distributed strategies which provide
approximate Nash equlibria for finite-player games. Our results are applied to
a new class of multi-agent price impact models and a class of flocking models
for which we prove existence of equilibria
A Probabilistic Approach to Mean Field Games with Major and Minor Players
We propose a new approach to mean field games with major and minor players.
Our formulation involves a two player game where the optimization of the
representative minor player is standard while the major player faces an
optimization over conditional McKean-Vlasov stochastic differential equations.
The definition of this limiting game is justified by proving that its solution
provides approximate Nash equilibriums for large finite player games. This
proof depends upon the generalization of standard results on the propagation of
chaos to conditional dynamics. Because it is on independent interest, we prove
this generalization in full detail. Using a conditional form of the Pontryagin
stochastic maximum principle (proven in the appendix), we reduce the solution
of the mean field game to a forward-backward system of stochastic differential
equations of the conditional McKean-Vlasov type, which we solve in the Linear
Quadratic setting. We use this class of models to show that Nash equilibriums
in our formulation can be different from those of the formulations contemplated
so far in the literature
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
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