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Analyzing a networked social algorithm for collective selection of representative committees.

Alexis R HernándezCarlos Gracia-LázaroEdgardo BrigattiYamir Moreno
Published in: PloS one (2019)
A recent work (Hernández, et al., 2018) introduced a networked voting rule supported by a trust-based social network, where indications of possible representatives were based on individuals opinions. Individual contributions went beyond a simple vote-counting and were based on proxy voting. This mechanism selects committees with high levels of representativeness, weakening the possibility of patronage relations. By incorporating the integrity of individuals and its perception, we here address the question of the resulting committee's trustability. Our results show that this voting rule provides sufficiently small committees with high levels of representativeness and integrity. Furthermore, the voting system displays robustness to strategic and untruthful application of the voting algorithm.
Keyphrases
  • machine learning
  • mental health
  • deep learning