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Related Experiment Video

Updated: Feb 11, 2026

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
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A networked voting rule for democratic representation.

Alexis R Hernández1,2, Carlos Gracia-Lázaro2, Edgardo Brigatti1

  • 1Instituto de Física, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.

Royal Society Open Science
|April 17, 2018
PubMed
Summary
This summary is machine-generated.

This study presents a new framework for selecting representative committees on decentralized platforms, ensuring accountability. The algorithm-based approach achieves high representativeness, even outperforming traditional methods for large populations.

Keywords:
e-Democracymathematical modellingphysics of social systemssocial networks

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Area of Science:

  • Computational Social Science
  • Network Science
  • Political Science

Background:

  • Decentralized platforms require robust methods for electing accountable representatives.
  • Traditional voting rules may not scale effectively for large, networked populations.

Purpose of the Study:

  • To introduce a general framework for committee selection on decentralized platforms.
  • To evaluate an algorithm-based voting rule for its representativeness and accountability.
  • To analyze the scalability of committee selection in large networks.

Main Methods:

  • Development of a general framework for committee selection.
  • Simulation of a networked voting rule on a decentralized platform.
  • Analysis of the relationship between committee size and population size.

Main Results:

  • The algorithm-based approach achieves high committee representativeness, surpassing classical closed-list voting.
  • A general inverse square root law governs the relationship between committee size and the number of representatives.
  • Normalized committee size scales inversely with community size, ensuring scalability.

Conclusions:

  • The proposed framework offers an effective and scalable solution for representative committee selection in large, decentralized systems.
  • The findings highlight the efficiency of algorithm-based approaches over traditional methods.
  • Network structure has minimal impact, except for highly connected individuals.