Diversity in Kemeny Rank Aggregation: A Parameterized Approach
Emmanuel Arrighi, Henning Fernau, Daniel Lokshtanov, Mateus de, Oliveira Oliveira, Petra Wolf

TL;DR
This paper explores a parameterized approach to diversity in Kemeny Rank Aggregation, showing it is fixed-parameter tractable with respect to natural diversity parameters, applicable to both linear and partial orders.
Contribution
It introduces a novel parameterized framework for diversity in Kemeny Rank Aggregation, demonstrating fixed-parameter tractability for both linear and partial order settings.
Findings
Kemeny Rank Aggregation is fixed-parameter tractable with respect to diversity parameters.
The approach applies to both linear and partially ordered votes.
Enhances solution richness by providing diverse, sufficiently good solutions.
Abstract
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…
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