Egalitarianism in the rank aggregation problem: a new dimension for democracy

Winner selection by majority, in elections between two candidates, is the only rule compatible with democratic principles. Instead, when candidates are three or more and 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 eighteenth century Condorcet theory, whose idea was maximising total voter satisfaction, we propose here 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, ranging from the Condorcet solution to the the most egalitarian one with respect to the voters. Most importantly, we show that highly egalitarian rankings are much more robust, with respect to random fluctuations in the votes, than consensus rankings returned by classical voting rules (Copeland, Tideman, Schulze). The newly introduced dimension provides, when used together with that of Condorcet, a more informative 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.

[1]  Michel Truchon Aggregation of Rankings: A Brief Review of Distance-Based Rules , 2007 .

[2]  Christoph Brgers Mathematics of Social Choice: Voting, Compensation, and Division , 2009 .

[3]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[4]  Nicolas de Condorcet Essai Sur L'Application de L'Analyse a la Probabilite Des Decisions Rendues a la Pluralite Des Voix , 2009 .

[5]  K. Arrow A Difficulty in the Concept of Social Welfare , 1950, Journal of Political Economy.

[6]  Rolf Niedermeier,et al.  Fixed-parameter algorithms for Kemeny rankings , 2009, Theor. Comput. Sci..

[7]  R. Graham,et al.  Spearman's Footrule as a Measure of Disarray , 1977 .

[8]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[9]  Umberto Straccia,et al.  Web metasearch: rank vs. score based rank aggregation methods , 2003, SAC '03.

[10]  Ronald Fagin,et al.  Comparing and aggregating rankings with ties , 2004, PODS '04.

[11]  John G. Kemeny,et al.  Mathematical models in the social sciences , 1964 .

[12]  A. Feldman Welfare economics and social choice theory , 1980 .

[13]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[14]  N. J. A. Sloane,et al.  The On-Line Encyclopedia of Integer Sequences , 2003, Electron. J. Comb..

[15]  Helmut G Katzgraber,et al.  Dealing with correlated choices: how a spin-glass model can help political parties select their policies. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  L. Seiford,et al.  On the Borda-Kendall Consensus Method for Priority Ranking Problems , 1982 .

[17]  Donald G. Saari,et al.  A geometric examination of Kemeny's rule , 2000, Soc. Choice Welf..

[18]  H. Young Condorcet's Theory of Voting , 1988, American Political Science Review.

[19]  Christoph Börgers,et al.  Mathematics of Social Choice - Voting, Compensation, and Division , 2010 .

[20]  Moni Naor,et al.  Rank aggregation methods for the Web , 2001, WWW '01.

[21]  Matteo Marsili,et al.  Statistical mechanics model for the emergence of consensus. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Jarek Gryz,et al.  Algorithms and analyses for maximal vector computation , 2007, The VLDB Journal.

[23]  B. Monjardet "Mathématique Sociale" and Mathematics. A case study: Condorcet's effect and medians , 2008 .

[24]  John G. Kemeny,et al.  Mathematical models in the social sciences , 1964 .

[25]  Patricia Rose Gomes de Melo Viol Martins,et al.  MATHEMATICS WITHOUT NUMBERS: AN INTRODUCTION TO THE STUDY OF LOGIC , 2015 .

[26]  Willem J. Heiser,et al.  Clustering and Prediction of Rankings Within a Kemeny Distance Framework , 2013, Algorithms from and for Nature and Life.