Title: "Comparing Ways of Obtaining Candidate Axes from Approval Ballots"
Abstract:
To understand and summarize approval preferences and other binary
evaluation data, it is useful to order the items on an axis which
explains the data. In a political election using approval voting, this
could be an ideological left-right axis such that each voter approves
adjacent candidates, an analogue of single-peakedness. In a perfect
axis, every approval set would be an interval, which is usually not
possible, and so we need to choose an axis that gets closest to this
ideal. The literature has developed algorithms for optimizing several
objective functions (e.g., minimize the number of added approvals needed
to get a perfect axis), but provides little help with choosing among
different objectives. In this paper, we take a social choice approach
and compare 5 different axis selection rules axiomatically, by studying
the properties they satisfy. We establish some impossibility theorems,
and characterize (within the class of scoring rules) the rule that
chooses the axes that maximize the number of votes that form intervals,
using the axioms of ballot monotonicity and resistance to cloning.
Finally, we study the behavior of the rules on data from French election
surveys, on the votes of justices of the US Supreme Court, and on
synthetic data.
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