• Medientyp: E-Book
  • Titel: Deciding Not To Decide
  • Beteiligte: Ellsaesser, Florian [Verfasser:in]; Fioretti, Guido [Verfasser:in]; James, Gail E. [Verfasser:in]
  • Erschienen: [S.l.]: SSRN, [2022]
  • Umfang: 1 Online-Ressource (22 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.4004121
  • Identifikator:
  • Schlagwörter: Radical Uncertainty ; Cognitive Maps ; Scenario Planning ; Deciding Not To Decide ; Machine Learning
  • Entstehung:
  • Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 8, 2022 erstellt
  • Beschreibung: Sometimes unexpected, novel, unconceivable events enter our lives. The cause-effect mappings that usually guide our behaviour are destroyed. Surprised and shocked by possibilities that we had never imagined, we are unable to make any decision beyond mere routine. Among them there are decisions, such as making investments, that are essential for the long-term survival of businesses as well as the economy at large.We submit that the standard machinery of utility maximization does not apply, but we propose measures inspired by scenario planning and graph analysis, pointing to solutions being explored in machine learning
  • Zugangsstatus: Freier Zugang