IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004965.html
   My bibliography  Save this article

How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences

Author

Listed:
  • Michael Moutoussis
  • Raymond J Dolan
  • Peter Dayan

Abstract

The weight with which a specific outcome feature contributes to preference quantifies a person’s ‘taste’ for that feature. However, far from being fixed personality characteristics, tastes are plastic. They tend to align, for example, with those of others even if such conformity is not rewarded. We hypothesised that people can be uncertain about their tastes. Personal tastes are therefore uncertain beliefs. People can thus learn about them by considering evidence, such as the preferences of relevant others, and then performing Bayesian updating. If a person’s choice variability reflects uncertainty, as in random-preference models, then a signature of Bayesian updating is that the degree of taste change should correlate with that person’s choice variability. Temporal discounting coefficients are an important example of taste–for patience. These coefficients quantify impulsivity, have good psychometric properties and can change upon observing others’ choices. We examined discounting preferences in a novel, large community study of 14–24 year olds. We assessed discounting behaviour, including decision variability, before and after participants observed another person’s choices. We found good evidence for taste uncertainty and for Bayesian taste updating. First, participants displayed decision variability which was better accounted for by a random-taste than by a response-noise model. Second, apparent taste shifts were well described by a Bayesian model taking into account taste uncertainty and the relevance of social information. Our findings have important neuroscientific, clinical and developmental significance.Author Summary: People often change their preferences in the light of what others choose. One form of such change is ‘epistemic trust’ for preferences, i.e. preference alignment over and above any direct benefits that may accrue. We sought to explain preference shifting in terms of normative Bayesian inference in which, along with updating beliefs about what the world is like, and what the correct or profitable answers are given one's tastes, subjects also learn about their own personal tastes when these are incompletely certain. In a novel study based on a well-established paradigms, 740 young people expressed their tastes about the degree to which they preferred a smaller but immediate, versus a larger but delayed, reward. They did this both before and after learning about another agent’s choices. We found taste changed between the two assessments to a degree that was correlated with subjects’ choice variability in the absence of social influence. This is consistent with our Bayesian model if, for instance, people make choices by taking random samples from their own uncertain beliefs. Younger people were influenced by others more than older ones, and this observation was explained in the model by the former being less certain about their own preferences.

Suggested Citation

  • Michael Moutoussis & Raymond J Dolan & Peter Dayan, 2016. "How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-17, July.
  • Handle: RePEc:plo:pcbi00:1004965
    DOI: 10.1371/journal.pcbi.1004965
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004965
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004965&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1004965?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Loomes, Graham & Moffatt, Peter G & Sugden, Robert, 2002. "A Microeconometric Test of Alternative Stochastic Theories of Risky Choice," Journal of Risk and Uncertainty, Springer, vol. 24(2), pages 103-130, March.
    2. Hurwicz,Leonid & Reiter,Stanley, 2008. "Designing Economic Mechanisms," Cambridge Books, Cambridge University Press, number 9780521724104, October.
    3. Peter Moffatt & Simon Peters, 2001. "Testing for the Presence of a Tremble in Economic Experiments," Experimental Economics, Springer;Economic Science Association, vol. 4(3), pages 221-228, December.
    4. David Revelt and Kenneth Train., 2000. "Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier," Economics Working Papers E00-274, University of California at Berkeley.
    5. R. H. Strotz, 1955. "Myopia and Inconsistency in Dynamic Utility Maximization," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 23(3), pages 165-180.
    6. John Hey & Enrica Carbone, "undated". "Which Error Theory is Best?," Discussion Papers 99/31, Department of Economics, University of York.
    7. Ting Xiang & Debajyoti Ray & Terry Lohrenz & Peter Dayan & P Read Montague, 2012. "Computational Phenotyping of Two-Person Interactions Reveals Differential Neural Response to Depth-of-Thought," PLOS Computational Biology, Public Library of Science, vol. 8(12), pages 1-9, December.
    8. Batley, Richard & Ibáñez, J. Nicolás, 2012. "Randomness in preference orderings, outcomes and attribute tastes: An application to journey time risk," Journal of choice modelling, Elsevier, vol. 5(3), pages 157-175.
    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. Magda Dubois & Tobias U. Hauser, 2022. "Value-free random exploration is linked to impulsivity," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Marie Devaine & Jean Daunizeau, 2017. "Learning about and from others' prudence, impatience or laziness: The computational bases of attitude alignment," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-28, March.
    3. Runtian Zhang & Jinye Li, 2020. "Impact of incentive and selection strength on green technology innovation in Moran process," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-15, June.

    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. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    2. Anna Conte & M. Vittoria Levati & Natalia Montinari, 2019. "Experience in public goods experiments," Theory and Decision, Springer, vol. 86(1), pages 65-93, February.
    3. Daniel Navarro-Martinez & Graham Loomes & Andrea Isoni & David Butler & Larbi Alaoui, 2018. "Boundedly rational expected utility theory," Journal of Risk and Uncertainty, Springer, vol. 57(3), pages 199-223, December.
    4. Anna Conte & Peter G. Moffatt & Fabrizio Botti & Daniela T. Di Cagno & Carlo D’Ippoliti, 2012. "A test of the rational expectations hypothesis using data from a natural experiment," Applied Economics, Taylor & Francis Journals, vol. 44(35), pages 4661-4678, December.
    5. Anna Conte & John D. Hey & Peter G. Moffatt, 2018. "Mixture models of choice under risk," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 1, pages 3-12, World Scientific Publishing Co. Pte. Ltd..
    6. Peter Moffatt, 2005. "Stochastic Choice and the Allocation of Cognitive Effort," Experimental Economics, Springer;Economic Science Association, vol. 8(4), pages 369-388, December.
    7. Hans-Martin Gaudecker & Arthur Soest & Erik Wengström, 2012. "Experts in experiments," Journal of Risk and Uncertainty, Springer, vol. 45(2), pages 159-190, October.
    8. Arianna Galliera & E. Elisabet Rutström, 2021. "Crowded out: Heterogeneity in risk attitudes among poor households in the US," Journal of Risk and Uncertainty, Springer, vol. 63(2), pages 103-132, October.
    9. Nicholas Bardsley & Peter Moffatt, 2007. "The Experimetrics of Public Goods: Inferring Motivations from Contributions," Theory and Decision, Springer, vol. 62(2), pages 161-193, March.
    10. Wilcox, Nathaniel T., 2011. "'Stochastically more risk averse:' A contextual theory of stochastic discrete choice under risk," Journal of Econometrics, Elsevier, vol. 162(1), pages 89-104, May.
    11. Kemptner, Daniel & Tolan, Songül, 2018. "The role of time preferences in educational decision making," Economics of Education Review, Elsevier, vol. 67(C), pages 25-39.
    12. Xiang Meng, 2019. "Dynamic Mean-Variance Portfolio Optimisation," Papers 1907.03093, arXiv.org.
    13. Allais, Olivier & Etilé, Fabrice & Lecocq, Sébastien, 2015. "Mandatory labels, taxes and market forces: An empirical evaluation of fat policies," Journal of Health Economics, Elsevier, vol. 43(C), pages 27-44.
    14. Xiangyu Cui & Xun Li & Duan Li & Yun Shi, 2014. "Time Consistent Behavior Portfolio Policy for Dynamic Mean-Variance Formulation," Papers 1408.6070, arXiv.org, revised Aug 2015.
    15. Lex Borghans & Angela Lee Duckworth & James J. Heckman & Bas ter Weel, 2008. "The Economics and Psychology of Personality Traits," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
    16. Tarna Silue, 2021. "Financial Inclusion and Economic Growth : Evidence in the Digital Environment of Developing Countries," Working Papers hal-03281843, HAL.
    17. Sarah Jacobson & Ragan Petrie, 2009. "Learning from mistakes: What do inconsistent choices over risk tell us?," Journal of Risk and Uncertainty, Springer, vol. 38(2), pages 143-158, April.
    18. Yu-Jui Huang & Adrien Nguyen-Huu, 2018. "Time-consistent stopping under decreasing impatience," Finance and Stochastics, Springer, vol. 22(1), pages 69-95, January.
    19. , G. & , & ,, 2008. "Non-Bayesian updating: A theoretical framework," Theoretical Economics, Econometric Society, vol. 3(2), June.
    20. Hill, Brian, 2010. "An additively separable representation in the Savage framework," Journal of Economic Theory, Elsevier, vol. 145(5), pages 2044-2054, September.

    More about this item

    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:plo:pcbi00:1004965. See general information about how to correct material in RePEc.

    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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.