  Efficient learning in multi-armed bandit mechanisms such as pay-per-click
(PPC) auctions typically involves three challenges: 1) inducing truthful
bidding behavior (incentives), 2) using personalization in the users (context),
and 3) circumventing manipulations in click patterns (corruptions). Each of
these challenges has been studied orthogonally in the literature; incentives
have been addressed by a line of work on truthful multi-armed bandit
mechanisms, context has been extensively tackled by contextual bandit
algorithms, while corruptions have been discussed via a recent line of work on
bandits with adversarial corruptions. Since these challenges co-exist, it is
important to understand the robustness of each of these approaches in
addressing the other challenges, provide algorithms that can handle all
simultaneously, and highlight inherent limitations in this combination. In this
work, we show that the most prominent contextual bandit algorithm,
$\epsilon$-greedy can be extended to handle the challenges introduced by
strategic arms in the contextual multi-arm bandit mechanism setting. We further
show that $\epsilon$-greedy is inherently robust to adversarial data corruption
attacks and achieves performance that degrades linearly with the amount of
corruption.
