IDEAS home Printed from https://ideas.repec.org/p/jrp/jrpwrp/2014-004.html
   My bibliography  Save this paper

The Determinants of Decision Time

Author

Listed:
  • Anna Conte

    (Strategic Interaction Group, Max Planck Institute of Economics, Jena, and Department of Economics and Quantitative methods, WBS, University of Westminster)

  • John D. Hey

    (Department of Economics and Related Studies, University of York)

  • Ivan Soraperra

    (Dipartimento di Scienze Economiche, University of Verona,)

Abstract

This paper estimates the determinants of decision time for different types of decision maker in the context of an experimental investigation of multiple prior models of behaviour under ambiguity. Four models are considered: Expected Utility, Smooth, Rank Dependent Expected Utility and Alpha model. The results of a mixture model which assigns subjects to types enable us to distinguish the factors influencing the decision time of each of these four types. We find that the different types are influenced by different factors. In general, the Rank Dependent type takes more time, followed by the Smooth, the Expected Utility and finally the Alpha type, whose decision time is always the lowest. Our results reflect the relative complexity of the preference functionals used by the different types. Consequently, the importance of looking at the process of pairwise choices rather than simply at the choice made is raised to the attention of theorists and analysts.

Suggested Citation

  • Anna Conte & John D. Hey & Ivan Soraperra, 2014. "The Determinants of Decision Time," Jena Economic Research Papers 2014-004, Friedrich-Schiller-University Jena.
  • Handle: RePEc:jrp:jrpwrp:2014-004
    as

    Download full text from publisher

    File URL: http://www2.wiwi.uni-jena.de/Papers/jerp2014/wp_2014_004.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Johanna Etner & Meglena Jeleva & Jean‐Marc Tallon, 2012. "Decision Theory Under Ambiguity," Journal of Economic Surveys, Wiley Blackwell, vol. 26(2), pages 234-270, April.
    2. Anna Conte & John D. Hey, 2018. "Assessing multiple prior models of behaviour under ambiguity," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 7, pages 169-188, World Scientific Publishing Co. Pte. Ltd..
    3. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Clithero, John A., 2018. "Response times in economics: Looking through the lens of sequential sampling models," Journal of Economic Psychology, Elsevier, vol. 69(C), pages 61-86.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amit Kothiyal & Vitalie Spinu & Peter Wakker, 2014. "An experimental test of prospect theory for predicting choice under ambiguity," Journal of Risk and Uncertainty, Springer, vol. 48(1), pages 1-17, February.
    2. Robin Cubitt & Gijs van de Kuilen & Sujoy Mukerji, 2020. "Discriminating Between Models of Ambiguity Attitude: a Qualitative Test," Journal of the European Economic Association, European Economic Association, vol. 18(2), pages 708-749.
    3. Théodora Dupont-Courtade, 2012. "Insurance demand under ambiguity and conflict for extreme risks : Evidence from a large representative survey," Post-Print halshs-00718642, HAL.
    4. Guo, Peijun, 2019. "Focus theory of choice and its application to resolving the St. Petersburg, Allais, and Ellsberg paradoxes and other anomalies," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1034-1043.
    5. Ali al-Nowaihi & Sanjit Dhami & Mengxing Wei, 2018. "Quantum Decision Theory and the Ellsberg Paradox," CESifo Working Paper Series 7158, CESifo.
    6. Théodora Dupont-Courtade, 2012. "Insurance demand under ambiguity and conflict for extreme risks : Evidence from a large representative survey," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00718642, HAL.
    7. Coutts, Alexander, 2019. "Testing models of belief bias: An experiment," Games and Economic Behavior, Elsevier, vol. 113(C), pages 549-565.
    8. Enrica Carbone & Konstantinos Georgalos & Gerardo Infante, 2019. "Individual vs. group decision-making: an experiment on dynamic choice under risk and ambiguity," Theory and Decision, Springer, vol. 87(1), pages 87-122, July.
    9. L. A. Franzoni, 2016. "Optimal liability design under risk and ambiguity," Working Papers wp1048, Dipartimento Scienze Economiche, Universita' di Bologna.
    10. Andrew J. Keith & Darryl K. Ahner, 2021. "A survey of decision making and optimization under uncertainty," Annals of Operations Research, Springer, vol. 300(2), pages 319-353, May.
    11. Robin Cubitt & Gijs Kuilen & Sujoy Mukerji, 2018. "The strength of sensitivity to ambiguity," Theory and Decision, Springer, vol. 85(3), pages 275-302, October.
    12. Eric Giraud-Héraud & Maria Aguiar Fontes & Alexandra Seabra Pinto, 2014. "Crise sanitaires de l'alimentation et analyses comportementales," Working Papers hal-00949126, HAL.
    13. Ali al-Nowaihi & Sanjit Dhami, 2016. "The Ellsberg paradox: A challenge to quantum decision theory?," Discussion Papers in Economics 16/08, Division of Economics, School of Business, University of Leicester.
    14. Arthur E. Attema & Han Bleichrodt & Olivier L'Haridon, 2018. "Ambiguity preferences for health," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1699-1716, November.
    15. Prokosheva, Sasha, 2016. "Comparing decisions under compound risk and ambiguity: The importance of cognitive skills," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 64(C), pages 94-105.
    16. Smith, Robert Elliott, 2016. "Idealizations of Uncertainty, and Lessons from Artificial Intelligence," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 10, pages 1-40.
    17. Christiane Goodfellow & Dirk Schiereck & Steffen Wippler, 2013. "Are behavioural finance equity funds a superior investment? A note on fund performance and market efficiency," Journal of Asset Management, Palgrave Macmillan, vol. 14(2), pages 111-119, April.
    18. Philippe Fevrier & Sebastien Gay, 2005. "Informed Consent Versus Presumed Consent The Role of the Family in Organ Donations," HEW 0509007, University Library of Munich, Germany.
    19. Shuang Yao & Donghua Yu & Yan Song & Hao Yao & Yuzhen Hu & Benhai Guo, 2018. "Dry Bulk Carrier Investment Selection through a Dual Group Decision Fusing Mechanism in the Green Supply Chain," Sustainability, MDPI, Open Access Journal, vol. 10(12), pages 1-19, November.
    20. Senik, Claudia, 2009. "Direct evidence on income comparisons and their welfare effects," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 408-424, October.

    More about this item

    Keywords

    decision time; choice under uncertainty; censored regression;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jrp:jrpwrp:2014-004. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.wiwiss.uni-jena.de/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Markus Pasche (email available below). General contact details of provider: http://www.wiwiss.uni-jena.de/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.