  Many electoral bribery, control, and manipulation problems (which we will
refer to in general as "manipulative actions" problems) are NP-hard in the
general case. It has recently been noted that many of these problems fall into
polynomial time if the electorate is single-peaked (i.e., is polarized along
some axis/issue). However, real-world electorates are not truly single-peaked.
There are usually some mavericks, and so real-world electorates tend to merely
be nearly single-peaked. This paper studies the complexity of
manipulative-action algorithms for elections over nearly single-peaked
electorates, for various notions of nearness and various election systems. We
provide instances where even one maverick jumps the manipulative-action
complexity up to $\np$-hardness, but we also provide many instances where a
reasonable number of mavericks can be tolerated without increasing the
manipulative-action complexity.
