• Media type: E-Book
  • Title: Optimal Sequential Search Among Alternatives
  • Contributor: Choi, Michael [Author]; Smith, Lones [Other]
  • Published: [S.l.]: SSRN, [2020]
  • Extent: 1 Online-Ressource (48 p)
  • Language: English
  • DOI: 10.2139/ssrn.2858097
  • Identifier:
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 26, 2016 erstellt
  • Description: We explore costly sequential search among finitely many risky options, and an outside option. Payoffs are the sum of a known and hidden random factor.(a) We resolve a long open question about how riskier payoffs impact search duration: expected search time is higher for more dispersed idiosyncratic noise.(b) Since options differ ex ante, we incorporate selection effects into search: Counterintuitively, with few options, the quitting chance falls if search costs rise; also, while stopping rates rise over time, earlier options are recalled more.(c) We find that the stationary search model is a misleading benchmark: For as the number of options explodes, the recall chance is bounded away from zero if the known factor has a distribution without a thin tail (eg. exponential).(d) A special case of our model captures web search engines that rank order options: We prove that the click through rate — the chance of initiating a search — is a poor quality measure since it falls in accuracy for expensive goods
  • Access State: Open Access