  In its most traditional setting, the main concern of optimization theory is
the search for optimal solutions for instances of a given computational
problem. A recent trend of research in artificial intelligence, called solution
diversity, has focused on the development of notions of optimality that may be
more appropriate in settings where subjectivity is essential. The idea is that
instead of aiming at the development of algorithms that output a single optimal
solution, the goal is to investigate algorithms that output a small set of
sufficiently good solutions that are sufficiently diverse from one another. In
this way, the user has the opportunity to choose the solution that is most
appropriate to the context at hand. It also displays the richness of the
solution space.
  When combined with techniques from parameterized complexity theory, the
paradigm of diversity of solutions offers a powerful algorithmic framework to
address problems of practical relevance. In this work, we investigate the
impact of this combination in the field of Kemeny Rank Aggregation, a
well-studied class of problems lying in the intersection of order theory and
social choice theory and also in the field of order theory itself. In
particular, we show that the Kemeny Rank Aggregation problem is fixed-parameter
tractable with respect to natural parameters providing natural formalizations
of the notions of diversity and of the notion of a sufficiently good solution.
Our main results work both when considering the traditional setting of
aggregation over linearly ordered votes, and in the more general setting where
votes are partially ordered.
