Your browser doesn't support javascript.
loading
Heterogeneity Improves Speed and Accuracy in Social Networks.
Karamched, Bhargav; Stickler, Megan; Ott, William; Lindner, Benjamin; Kilpatrick, Zachary P; Josic, Kresimir.
Afiliación
  • Karamched B; Department of Mathematics, Florida State University, Tallahassee, Florida 32306, USA.
  • Stickler M; Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA.
  • Ott W; Department of Mathematics, University of Houston, Houston, Texas 77004, USA.
  • Lindner B; Department of Mathematics, University of Houston, Houston, Texas 77004, USA.
  • Kilpatrick ZP; Physics Department of Humboldt University Berlin, Newtonstraße 15, 12489 Berlin, Germany.
  • Josic K; Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, Haus 2, 10115 Berlin, Germany.
Phys Rev Lett ; 125(21): 218302, 2020 Nov 20.
Article en En | MEDLINE | ID: mdl-33274999
ABSTRACT
How does temporally structured private and social information shape collective decisions? To address this question we consider a network of rational agents who independently accumulate private evidence that triggers a decision upon reaching a threshold. When seen by the whole network, the first agent's choice initiates a wave of new decisions; later decisions have less impact. In heterogeneous networks, first decisions are made quickly by impulsive individuals who need little evidence to make a choice but, even when wrong, can reveal the correct options to nearly everyone else. We conclude that groups comprised of diverse individuals can make more efficient decisions than homogenous ones.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Red Social / Modelos Teóricos Tipo de estudio: Prognostic_studies Idioma: En Revista: Phys Rev Lett Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Red Social / Modelos Teóricos Tipo de estudio: Prognostic_studies Idioma: En Revista: Phys Rev Lett Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos