Nothing Special   »   [go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/zur/econwp/306.html
   My bibliography  Save this paper

Time will tell: recovering preferences when choices are noisy

Author

Listed:
  • Carlos Alós-Ferrer
  • Ernst Fehr
  • Nick Netzer
Abstract
The ability to uncover preferences from choices is fundamental for both positive economics and welfare analysis. Overwhelming evidence shows that choice is stochastic, which has given rise to random utility models as the dominant paradigm in applied microeconomics. However, as is well known, it is not possible to infer the structure of preferences in the absence of assumptions on the structure of noise. We show that the difficulty can be overcome if data sets are enlarged to include response times. A simple condition on response time distributions (a weaker version of first-order stochastic dominance) ensures that choices reveal preferences without assumptions on the structure of utility noise. Standard random utility models from economics and standard drift-diffusion models from psychology generate data sets fulfilling this condition. Sharper results are obtained if the analysis is restricted to specific classes of noise. Under symmetric noise, response times allow to uncover preferences for choice pairs outside the data set, and if noise is Fechnerian, precise choice probabilities can be forecast out-of-sample. We apply our tools to an experimental data set, illustrating that the application is simple and generates a remarkable prediction accuracy.

Suggested Citation

  • Carlos Alós-Ferrer & Ernst Fehr & Nick Netzer, 2018. "Time will tell: recovering preferences when choices are noisy," ECON - Working Papers 306, Department of Economics - University of Zurich, revised Jun 2020.
  • Handle: RePEc:zur:econwp:306
    as

    Download full text from publisher

    File URL: https://www.zora.uzh.ch/id/eprint/157504/7/econwp306.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jose Apesteguia & Miguel A. Ballester, 2018. "Monotone Stochastic Choice Models: The Case of Risk and Time Preferences," Journal of Political Economy, University of Chicago Press, vol. 126(1), pages 74-106.
    2. Jörg Rieskamp & Jerome R. Busemeyer & Barbara A. Mellers, 2006. "Extending the Bounds of Rationality: Evidence and Theories of Preferential Choice," Journal of Economic Literature, American Economic Association, vol. 44(3), pages 631-661, September.
    3. Ian Krajbich & Björn Bartling & Todd Hare & Ernst Fehr, 2015. "Rethinking fast and slow based on a critique of reaction-time reverse inference," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    4. Paola Manzini & Marco Mariotti, 2014. "Stochastic Choice and Consideration Sets," Econometrica, Econometric Society, vol. 82(3), pages 1153-1176, May.
    5. David Buschena & David Zilberman, 2008. "Generalized expected utility, heteroscedastic error, and path dependence in risky choice," Journal of Risk and Uncertainty, Springer, vol. 36(2), pages 201-201, April.
    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. Jose Apesteguia & Miguel A. Ballester, 2015. "A Measure of Rationality and Welfare," Journal of Political Economy, University of Chicago Press, vol. 123(6), pages 1278-1310.
    8. Andrew Caplin & Daniel Martin, 2015. "A Testable Theory of Imperfect Perception," Economic Journal, Royal Economic Society, vol. 125(582), pages 184-202, February.
    9. Anja Achtziger & Carlos Alós-Ferrer, 2014. "Fast or Rational? A Response-Times Study of Bayesian Updating," Management Science, INFORMS, vol. 60(4), pages 923-938, April.
    10. Jean-Michel Benkert & Nick Netzer, 2018. "Informational Requirements of Nudging," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2323-2355.
    11. Daniel McFadden, 2001. "Economic Choices," American Economic Review, American Economic Association, vol. 91(3), pages 351-378, June.
    12. Christopher F. Chabris & David Laibson & Carrie L. Morris & Jonathon P. Schuldt & Dmitry Taubinsky, 2009. "The Allocation of Time in Decision-Making," Journal of the European Economic Association, MIT Press, vol. 7(2-3), pages 628-637, 04-05.
    13. Leonidas Spiliopoulos & Andreas Ortmann, 2018. "The BCD of response time analysis in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 21(2), pages 383-433, June.
    14. Yusufcan Masatlioglu & Daisuke Nakajima & Erkut Y. Ozbay, 2012. "Revealed Attention," American Economic Review, American Economic Association, vol. 102(5), pages 2183-2205, August.
    15. Andrew Caplin & Mark Dean, 2015. "Revealed Preference, Rational Inattention, and Costly Information Acquisition," American Economic Review, American Economic Association, vol. 105(7), pages 2183-2203, July.
    16. Andrew Schotter & Isabel Trevino, 2021. "Is response time predictive of choice? An experimental study of threshold strategies," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 87-117, March.
    17. Camerer, Colin F, 1989. "Does the Basketball Market Believe in the 'Hot Hand'?," American Economic Review, American Economic Association, vol. 79(5), pages 1257-1261, December.
    18. Grether, David M & Plott, Charles R, 1979. "Economic Theory of Choice and the Preference Reversal Phenomenon," American Economic Review, American Economic Association, vol. 69(4), pages 623-638, September.
    19. Ariel Rubinstein, 2007. "Instinctive and Cognitive Reasoning: A Study of Response Times," Economic Journal, Royal Economic Society, vol. 117(523), pages 1243-1259, October.
    20. Marina Agranov & Pietro Ortoleva, 2017. "Stochastic Choice and Preferences for Randomization," Journal of Political Economy, University of Chicago Press, vol. 125(1), pages 40-68.
    21. Shafer, Wayne J, 1974. "The Nontransitive Consumer," Econometrica, Econometric Society, vol. 42(5), pages 913-919, September.
    22. repec:hal:pseose:halshs-01155313 is not listed on IDEAS
    23. Jose Apesteguia & Miguel A. Ballester & Jay Lu, 2017. "Single‐Crossing Random Utility Models," Econometrica, Econometric Society, vol. 85, pages 661-674, March.
    24. Michael Woodford, 2014. "Stochastic Choice: An Optimizing Neuroeconomic Model," American Economic Review, American Economic Association, vol. 104(5), pages 495-500, May.
    25. Ryan Webb, 2019. "The (Neural) Dynamics of Stochastic Choice," Management Science, INFORMS, vol. 65(1), pages 230-255, January.
    26. B. Douglas Bernheim, 2009. "Behavioral Welfare Economics," Journal of the European Economic Association, MIT Press, vol. 7(2-3), pages 267-319, 04-05.
    27. Ariel Rubinstein & Yuval Salant, 2012. "Eliciting Welfare Preferences from Behavioural Data Sets," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(1), pages 375-387.
    28. Echenique, Federico & Saito, Kota, 2017. "Response time and utility," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 49-59.
    29. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
    30. Alós-Ferrer, Carlos & Ritschel, Alexander, 2018. "The reinforcement heuristic in normal form games," Journal of Economic Behavior & Organization, Elsevier, vol. 152(C), pages 224-234.
    31. Barbera, Salvador & Pattanaik, Prasanta K, 1986. "Falmagne and the Rationalizability of Stochastic Choices in Terms of Random Orderings," Econometrica, Econometric Society, vol. 54(3), pages 707-715, May.
    32. B. Douglas Bernheim & Antonio Rangel, 2009. "Beyond Revealed Preference: Choice-Theoretic Foundations for Behavioral Welfare Economics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(1), pages 51-104.
    33. Yuval Salant & Ariel Rubinstein, 2008. "(A, f): Choice with Frames -super-1," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(4), pages 1287-1296.
    34. Frederick Mosteller & Philip Nogee, 1951. "An Experimental Measurement of Utility," Journal of Political Economy, University of Chicago Press, vol. 59(5), pages 371-371.
    35. Arkady Konovalov & Ian Krajbich, 2016. "Revealed Indifference: Using Response Times to Infer Preferences," Working Papers 16-01, Ohio State University, Department of Economics.
    36. Ian Krajbich & Bastiaan Oud & Ernst Fehr, 2014. "Benefits of Neuroeconomic Modeling: New Policy Interventions and Predictors of Preference," American Economic Review, American Economic Association, vol. 104(5), pages 501-506, May.
    37. Clithero, John A., 2018. "Improving out-of-sample predictions using response times and a model of the decision process," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 344-375.
    38. Satohiro Tajima & Jan Drugowitsch & Alexandre Pouget, 2016. "Optimal policy for value-based decision-making," Nature Communications, Nature, vol. 7(1), pages 1-12, November.
    39. Wilcox, Nathaniel T, 1993. "Lottery Choice: Incentives, Complexity and Decision Time," Economic Journal, Royal Economic Society, vol. 103(421), pages 1397-1417, November.
    40. Ballinger, T Parker & Wilcox, Nathaniel T, 1997. "Decisions, Error and Heterogeneity," Economic Journal, Royal Economic Society, vol. 107(443), pages 1090-1105, July.
    41. repec:bla:econom:v:43:y:1976:i:172:p:381-90 is not listed on IDEAS
    42. Ariel Rubinstein, 2007. "Instinctive and Cognitive Reasoning: Response Times Study," Levine's Bibliography 321307000000001011, UCLA Department of Economics.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Duffy, Sean & Smith, John, 2020. "An economist and a psychologist form a line: What can imperfect perception of length tell us about stochastic choice?," MPRA Paper 99417, University Library of Munich, Germany.
    3. Carlos Alós-Ferrer & Michele Garagnani, 2022. "Strength of preference and decisions under risk," Journal of Risk and Uncertainty, Springer, vol. 64(3), pages 309-329, June.
    4. Duffy, Sean & Gussman, Steven & Smith, John, 2021. "Visual judgments of length in the economics laboratory: Are there brains in stochastic choice?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    5. Carlos Alós-Ferrer & Johannes Buckenmaier, 2021. "Cognitive sophistication and deliberation times," Experimental Economics, Springer;Economic Science Association, vol. 24(2), pages 558-592, June.
    6. Duffy, Sean & Gussman, Steven & Smith, John, 2019. "Judgments of length in the economics laboratory: Are there brains in choice?," MPRA Paper 93126, University Library of Munich, Germany.
    7. Andrew Schotter & Isabel Trevino, 2021. "Is response time predictive of choice? An experimental study of threshold strategies," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 87-117, March.
    8. Arkady Konovalov & Ian Krajbich, 2016. "Revealed Indifference: Using Response Times to Infer Preferences," Working Papers 16-01, Ohio State University, Department of Economics.
    9. David J. Cooper & Ian Krajbich & Charles N. Noussair, 2019. "Choice-Process Data in Experimental Economics," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 1-13, August.
    10. Ryan Webb, 2019. "The (Neural) Dynamics of Stochastic Choice," Management Science, INFORMS, vol. 65(1), pages 230-255, January.
    11. Strittmatter, Anthony & Sunde, Uwe & Zegners, Dainis, 2022. "Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence," Rationality and Competition Discussion Paper Series 317, CRC TRR 190 Rationality and Competition.
    12. Cary Frydman & Ian Krajbich, 2022. "Using Response Times to Infer Others’ Private Information: An Application to Information Cascades," Management Science, INFORMS, vol. 68(4), pages 2970-2986, April.
    13. Guo, Liang, 2021. "Contextual deliberation and the choice-valuation preference reversal," Journal of Economic Theory, Elsevier, vol. 195(C).
    14. Sean, Duffy & John, Smith, 2023. "Stochastic choice and imperfect judgments of line lengths: What is hiding in the noise?," MPRA Paper 116382, University Library of Munich, Germany.
    15. Francesco Cerigioni, 2021. "Dual Decision Processes: Retrieving Preferences When Some Choices Are Automatic," Journal of Political Economy, University of Chicago Press, vol. 129(6), pages 1667-1704.
    16. Jan Hausfeld & Sven Resnjanskij, 2017. "Risky Decisions and the Opportunity Costs of Time," TWI Research Paper Series 108, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    17. 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.
    18. Arkady Konovalov & Ian Krajbich, 2019. "Revealed strength of preference: Inference from response times," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(4), pages 381-394, July.
    19. Caliari, Daniele, 2023. "Behavioural welfare analysis and revealed preference: Theory and experimental evidence," Discussion Papers, Research Unit: Economics of Change SP II 2023-303, WZB Berlin Social Science Center.
    20. Recalde, María P. & Riedl, Arno & Vesterlund, Lise, 2018. "Error-prone inference from response time: The case of intuitive generosity in public-good games," Journal of Public Economics, Elsevier, vol. 160(C), pages 132-147.

    More about this item

    Keywords

    Revealed preference; random utility models; response times;
    All these keywords.

    JEL classification:

    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics

    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:zur:econwp:306. 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: Severin Oswald (email available below). General contact details of provider: https://edirc.repec.org/data/seizhch.html .

    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.