7,853 research outputs found
On the additivity of preference aggregation methods
The paper reviews some axioms of additivity concerning ranking methods used
for generalized tournaments with possible missing values and multiple
comparisons. It is shown that one of the most natural properties, called
consistency, has strong links to independence of irrelevant comparisons, an
axiom judged unfavourable when players have different opponents. Therefore some
directions of weakening consistency are suggested, and several ranking methods,
the score, generalized row sum and least squares as well as fair bets and its
two variants (one of them entirely new) are analysed whether they satisfy the
properties discussed. It turns out that least squares and generalized row sum
with an appropriate parameter choice preserve the relative ranking of two
objects if the ranking problems added have the same comparison structure.Comment: 24 pages, 9 figure
Ranking authors using fractional counting of citations : an axiomatic approach
This paper analyzes from an axiomatic point of view a recent proposal for counting citations: the value of a citation given by a paper is inversely proportional to the total number of papers it cites. This way of fractionally counting citations was suggested as a possible way to normalize citation counts between fields of research having different citation cultures. It belongs to the “citing-side” approach to normalization. We focus on the properties characterizing this way of counting citations when it comes to ranking authors. Our analysis is conducted within a formal framework that is more complex but also more realistic than the one usually adopted in most axiomatic analyses of this kind
Optimal Belief Approximation
In Bayesian statistics probability distributions express beliefs. However,
for many problems the beliefs cannot be computed analytically and
approximations of beliefs are needed. We seek a loss function that quantifies
how "embarrassing" it is to communicate a given approximation. We reproduce and
discuss an old proof showing that there is only one ranking under the
requirements that (1) the best ranked approximation is the non-approximated
belief and (2) that the ranking judges approximations only by their predictions
for actual outcomes. The loss function that is obtained in the derivation is
equal to the Kullback-Leibler divergence when normalized. This loss function is
frequently used in the literature. However, there seems to be confusion about
the correct order in which its functional arguments, the approximated and
non-approximated beliefs, should be used. The correct order ensures that the
recipient of a communication is only deprived of the minimal amount of
information. We hope that the elementary derivation settles the apparent
confusion. For example when approximating beliefs with Gaussian distributions
the optimal approximation is given by moment matching. This is in contrast to
many suggested computational schemes.Comment: made improvements on the proof and the languag
- …