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

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  • 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
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    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.
    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.
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    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.

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