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Collective attention and ranking methods

Author

Listed:
  • Gabrielle Demange

    (PSE - Paris-Jourdan Sciences Economiques - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

In a world with a tremendous amount of choices, ranking systems are becoming increasingly important in helping individuals to find information relevant to them. As such, rankings play a crucial role of influencing the attention that is devoted to the various alternatives. This role generates a feedback when the ranking is based on citations, as is the case for PageRank used by Google. The attention bias due to published rankings affects new stated opinions (citations), which will, in turn, affect the next ranking. The purpose of this paper is to investigate this feedback by studying some simple but reasonable dynamics. We show that the long run behavior of the process much depends on the preferences, in particular on their diversity, and on the used ranking method. Two main families of methods are investigated, one based on the notion of handicaps, the other one on the notion of peers' rankings.

Suggested Citation

  • Gabrielle Demange, 2014. "Collective attention and ranking methods," Post-Print halshs-00941931, HAL.
    • Handle: RePEc:hal:journl:halshs-00941931
    DOI: 10.3934/jdg.2014.1.17
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    Cited by:

    1. Brink, René van den & Rusinowska, Agnieszka, 2021. "The degree ratio ranking method for directed graphs," European Journal of Operational Research, Elsevier, vol. 288(2), pages 563-575.
    2. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    3. Fabrizio Germano & Vicenç Gómez & Gaël Le Mens, 2019. "The few-get-richer: a surprising consequence of popularity-based rankings," Economics Working Papers 1636, Department of Economics and Business, Universitat Pompeu Fabra.

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