39,088 research outputs found
Egalitarianism in the rank aggregation problem: a new dimension for democracy
Winner selection by majority, in an election between two candidates, is the
only rule compatible with democratic principles. Instead, when the candidates
are three or more and the voters rank candidates in order of preference, there
are no univocal criteria for the selection of the winning (consensus) ranking
and the outcome is known to depend sensibly on the adopted rule. Building upon
XVIII century Condorcet theory, whose idea was to maximize total voter
satisfaction, we propose here the addition of a new basic principle (dimension)
to guide the selection: satisfaction should be distributed among voters as
equally as possible. With this new criterion we identify an optimal set of
rankings. They range from the Condorcet solution to the one which is the most
egalitarian with respect to the voters. We show that highly egalitarian
rankings have the important property to be more stable with respect to
fluctuations and that classical consensus rankings (Copeland, Tideman, Schulze)
often turn out to be non optimal. The new dimension we have introduced
provides, when used together with that of Condorcet, a clear classification of
all the possible rankings. By increasing awareness in selecting a consensus
ranking our method may lead to social choices which are more egalitarian
compared to those achieved by presently available voting systems.Comment: 18 pages, 14 page appendix, RateIt Web Tool:
http://www.sapienzaapps.it/rateit.php, RankIt Android mobile application:
https://play.google.com/store/apps/details?id=sapienza.informatica.rankit.
Appears in Quality & Quantity, 10 Apr 2015, Online Firs
Social Dynamics of Digg
Online social media provide multiple ways to find interesting content. One
important method is highlighting content recommended by user's friends. We
examine this process on one such site, the news aggregator Digg. With a
stochastic model of user behavior, we distinguish the effects of the content
visibility and interestingness to users. We find a wide range of interest and
distinguish stories primarily of interest to a users' friends from those of
interest to the entire user community. We show how this model predicts a
story's eventual popularity from users' early reactions to it, and estimate the
prediction reliability. This modeling framework can help evaluate alternative
design choices for displaying content on the site.Comment: arXiv admin note: text overlap with arXiv:1010.023
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