• Medientyp: E-Book
  • Titel: Robust Non-Bayesian Learning
  • Beteiligte: Mueller-Frank, Manuel [Verfasser:in]
  • Erschienen: [S.l.]: SSRN, [2017]
  • Umfang: 1 Online-Ressource (16 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.3039241
  • Identifikator:
  • Entstehung:
  • Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 18, 2017 erstellt
  • Beschreibung: This paper investigates the robustness of three of the core insights of the DeGroot model of boundedly rational updating in social networks. Two updating systems are ε-close if their supremum norm distance is equal to ε. An ε-perturbation of a DeGroot (weighted average) updating system is any continuous updating system that is ε-close. A property of long run opinions is robust if it is retained in the limit for all ε-perturbations as ε converges to zero. We show consensus is a robust outcome but influence and naive learning are not
  • Zugangsstatus: Freier Zugang