Learning to hesitate
Ambroise Descamps (),
Sébastien Massoni and
Lionel Page
Additional contact information
Ambroise Descamps: School of Economics and Finance, Queensland University of Technology; Oxera Consulting LLP
No 2019/04, Working Paper Series from Economics Discipline Group, UTS Business School, University of Technology, Sydney
Abstract:
We investigate how people make choices when they are unsure about the value of the options they face and have to decide whether to choose now or wait and acquire more information first. We design a laboratory experiment to study whether human behaviour is able to approximate the optimal solution to this problem. We find that participants deviate from it in a systematic manner: they acquire too much information (when costly) or not enough (when cheap). These deviations costs participants between 10% and 25% of their potential payoffs. With time, participants tend to learn to approximate the optimal strategy.
Keywords: search; decision under uncertainty; information; optimal stopping; real option (search for similar items in EconPapers)
JEL-codes: C91 D81 D83 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2019-03-01
New Economics Papers: this item is included in nep-cbe and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.uts.edu.au/sites/default/files/2019-06 ... paper%20hesitate.pdf (application/pdf)
Related works:
Journal Article: Learning to hesitate (2022) 
Working Paper: Learning to hesitate (2021) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:uts:ecowps:2019/04
Access Statistics for this paper
More papers in Working Paper Series from Economics Discipline Group, UTS Business School, University of Technology, Sydney PO Box 123, Broadway, NSW 2007, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Duncan Ford ().