  We consider sequences of games $\mathcal{G}=\{G_1,G_2,\ldots\}$ where, for
all $n$, $G_n$ has the same set of players. Such sequences arise in the
analysis of running time of players in games, in electronic money systems such
as Bitcoin and in cryptographic protocols. Assuming that one-way functions
exist, we prove that there is a sequence of 2-player zero-sum Bayesian games
$\mathcal{G}$ such that, for all $n$, the size of every action in $G_n$ is
polynomial in $n$, the utility function is polynomial computable in $n$, and
yet there is no polynomial-time Nash equilibrium, where we use a notion of Nash
equilibrium that is tailored to sequences of games. We also demonstrate that
Nash equilibrium may not exist when considering players that are constrained to
perform at most $T$ computational steps in each of the games
$\{G_i\}_{i=1}^{\infty}$. These examples may shed light on competitive settings
where the availability of more running time or faster algorithms lead to a
"computational arms race", precluding the existence of equilibrium. They also
point to inherent limitations of concepts such "best response" and Nash
equilibrium in games with resource-bounded players.
