  Decentralized team problems where players have asymmetric information about
the state of the underlying stochastic system have been actively studied, but
\emph{games} between such teams are less understood. We consider a general
model of zero-sum stochastic games between two competing teams. This model
subsumes many previously considered team and zero-sum game models. For this
general model, we provide bounds on the upper (min-max) and lower (max-min)
values of the game. Furthermore, if the upper and lower values of the game are
identical (i.e., if the game has a \emph{value}), our bounds coincide with the
value of the game. Our bounds are obtained using two dynamic programs based on
a sufficient statistic known as the common information belief (CIB). We also
identify certain information structures in which only the minimizing team
controls the evolution of the CIB. In these cases, we show that one of our CIB
based dynamic programs can be used to find the min-max strategy (in addition to
the min-max value). We propose an approximate dynamic programming approach for
computing the values (and the strategy when applicable) and illustrate our
results with the help of an example.
