24,280 research outputs found
Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information
Zero-sum stochastic games provide a rich model for competitive decision
making. However, under general forms of state uncertainty as considered in the
Partially Observable Stochastic Game (POSG), such decision making problems are
still not very well understood. This paper makes a contribution to the theory
of zero-sum POSGs by characterizing structure in their value function. In
particular, we introduce a new formulation of the value function for zs-POSGs
as a function of the "plan-time sufficient statistics" (roughly speaking the
information distribution in the POSG), which has the potential to enable
generalization over such information distributions. We further delineate this
generalization capability by proving a structural result on the shape of value
function: it exhibits concavity and convexity with respect to appropriately
chosen marginals of the statistic space. This result is a key pre-cursor for
developing solution methods that may be able to exploit such structure.
Finally, we show how these results allow us to reduce a zs-POSG to a
"centralized" model with shared observations, thereby transferring results for
the latter, narrower class, to games with individual (private) observations
Uniform continuity of the value of zero-sum games with differential information
We establish uniform continuity of the value for zero-sum games with differential information, when the distance between changing information fields of each player is measured by the Boylan (1971) pseudo-metric. We also show that the optimal strategy correspondence is upper semicontinuous when the information fields of players change, even with the weak topology on players' strategy sets
Zero-sum stopping games with asymmetric information
We study a model of two-player, zero-sum, stopping games with asymmetric
information. We assume that the payoff depends on two continuous-time Markov
chains (X, Y), where X is only observed by player 1 and Y only by player 2,
implying that the players have access to stopping times with respect to
different filtrations. We show the existence of a value in mixed stopping times
and provide a variational characterization for the value as a function of the
initial distribution of the Markov chains. We also prove a verification theorem
for optimal stopping rules which allows to construct optimal stopping times.
Finally we use our results to solve explicitly two generic examples
Dynkin games with incomplete and asymmetric information
We study the value and the optimal strategies for a two-player zero-sum
optimal stopping game with incomplete and asymmetric information. In our
Bayesian set-up, the drift of the underlying diffusion process is unknown to
one player (incomplete information feature), but known to the other one
(asymmetric information feature). We formulate the problem and reduce it to a
fully Markovian setup where the uninformed player optimises over stopping times
and the informed one uses randomised stopping times in order to hide their
informational advantage. Then we provide a general verification result which
allows us to find the value of the game and players' optimal strategies by
solving suitable quasi-variational inequalities with some non-standard
constraints. Finally, we study an example with linear payoffs, in which an
explicit solution of the corresponding quasi-variational inequalities can be
obtained.Comment: 31 pages, 5 figures, small changes in the terminology from game
theor
Evaluating information in zero-sum games with incomplete information on both sides
In a Bayesian game some players might receive a noisy signal regarding the specific game actually being played before it starts. We study zero-sum games where each player receives a partial information about his own type and no information about that of the other player and analyze the impact the signals have on the payoffs. It turns out that the functions that evaluate the value of information share two property. The first is Blackwell monotonicity, which means that each player gains from knowing more. The second is concavity on the space of conditional probabilities.Value of information, Blackwell monotonicity, concavity.
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