  The original coded caching scenario assumes a content library that is of
interest to all receiving users. In a realistic scenario though, the users may
have diverging interests which may intersect to various degrees. What happens
for example if each file is of potential interest to, say, $40\,\%$ of the
users and each user has potential interest in $40\,\%$ of the library? In this
work, we investigate the so-called symmetrically selfish coded caching
scenario, where each user only makes requests from a subset of the library that
defines its own File Demand Set (FDS), each user caches selfishly only contents
from its own FDS, and where the different FDSs symmetrically overlap to some
extent. In the context of various traditional prefetching scenarios (prior to
the emergence of coded caching), selfish approaches were known to be
potentially very effective. On the other hand, with the exception of some
notable works, little is known about selfish coded caching. We here present a
new information-theoretic converse that proves, in a general setting of
symmetric FDS structures, that selfish coded caching, despite enjoying a much
larger local caching gain and a much smaller set of possible demands,
introduces an unbounded load increase compared to the unselfish case. In
particular, in the $K$-user broadcast channel where each user stores a fraction
$\gamma$ of the library, where each file (class) is of interest to $\alpha$
users, and where any one specific file is of interest to a fraction $\delta$ of
users, the optimal coding gain of symmetrically selfish caching is at least $(K
- \alpha)\gamma + 1$ times smaller than in the unselfish scenario. This allows
us to draw the powerful conclusion that the optimal selfish coding gain is
upper bounded by $1/(1 - \delta)$, and thus does not scale with $K$. These
derived limits are shown to be exact for different types of demands.
