  We study the problem of mismatched guesswork, where we evaluate the number of
symbols $y \in \mathcal{Y}$ which have higher likelihood than $X \sim \mu$
according to a mismatched distribution $\nu$. We discuss the role of the
tilted/exponential families of the source distribution $\mu$ and of the
mismatched distribution $\nu$. We show that the value of guesswork can be
characterized using the tilted family of the mismatched distribution $\nu$,
while the probability of guessing is characterized by an exponential family
which passes through $\mu$. Using this characterization, we demonstrate that
the mismatched guesswork follows a large deviation principle (LDP), where the
rate function is described implicitly using information theoretic quantities.
We apply these results to one-to-one source coding (without prefix free
constraint) to obtain the cost of mismatch in terms of average codeword length.
We show that the cost of mismatch in one-to-one codes is no larger than that of
the prefix-free codes, i.e., $D(\mu\| \nu)$. Further, the cost of mismatch
vanishes if and only if $\nu$ lies on the tilted family of the true
distribution $\mu$, which is in stark contrast to the prefix-free codes. These
results imply that one-to-one codes are inherently more robust to mismatch.
