IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_10644.html
   My bibliography  Save this paper

Quantifying Lottery Choice Complexity

Author

Listed:
  • Benjamin Enke
  • Cassidy Shubatt

Abstract

We develop interpretable, quantitative indices of the objective and subjective complexity of lottery choice problems that can be computed for any standard dataset. These indices capture the predicted error rate in identifying the lottery with the highest expected value, where the predictions are computed as convex combinations of choice set features. The most important complexity feature in the indices is a measure of the excess dissimilarity of the cumulative distribution functions of the lotteries in the set. Using our complexity indices, we study behavioral responses to complexity out-of-sample across one million decisions in 11,000 unique experimental choice problems. Complexity makes choices substantially noisier, which can generate systematic biases in revealed preference measures such as spurious risk aversion. These effects are very large, to the degree that complexity explains a larger fraction of estimated choice errors than proximity to indifference. Accounting for complexity in structural estimations improves model fit substantially.

Suggested Citation

  • Benjamin Enke & Cassidy Shubatt, 2023. "Quantifying Lottery Choice Complexity," CESifo Working Paper Series 10644, CESifo.
    • Handle: RePEc:ces:ceswps:_10644
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp10644.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adrian Bruhin & Helga Fehr-Duda & Thomas Epper, 2010. "Risk and Rationality: Uncovering Heterogeneity in Probability Distortion," Econometrica, Econometric Society, vol. 78(4), pages 1375-1412, July.
    2. Michael H. Birnbaum, 2005. "Three New Tests of Independence That Differentiate Models of Risky Decision Making," Management Science, INFORMS, vol. 51(9), pages 1346-1358, September.
    3. Ryan Webb & Paul W. Glimcher & Kenway Louie, 2021. "The Normalization of Consumer Valuations: Context-Dependent Preferences from Neurobiological Constraints," Management Science, INFORMS, vol. 67(1), pages 93-125, January.
    4. 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.
    5. Ferdinand M. Vieider, 2018. "Violence and Risk Preference: Experimental Evidence from Afghanistan: Comment," American Economic Review, American Economic Association, vol. 108(8), pages 2366-2382, August.
    6. Yoram Halevy, 2007. "Ellsberg Revisited: An Experimental Study," Econometrica, Econometric Society, vol. 75(2), pages 503-536, March.
    7. Cary Frydman & Lawrence J. Jin, 2023. "On the Source and Instability of Probability Weighting," NBER Working Papers 31573, National Bureau of Economic Research, Inc.
    8. John D. Hey, 2018. "Experimental investigations of errors in decision making under risk," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 17, pages 381-388, World Scientific Publishing Co. Pte. Ltd..
    9. Rubinstein, Ariel, 1988. "Similarity and decision-making under risk (is there a utility theory resolution to the Allais paradox?)," Journal of Economic Theory, Elsevier, vol. 46(1), pages 145-153, October.
    10. Leandro Carvalho & Dan Silverman, 2019. "Complexity and Sophistication," NBER Working Papers 26036, National Bureau of Economic Research, Inc.
    11. Mel Win Khaw & Ziang Li & Michael Woodford, 2021. "Cognitive Imprecision and Small-Stakes Risk Aversion [Linear Mapping of Numbers onto Space Requires Attention]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(4), pages 1979-2013.
    12. Huck, Steffen & Weizsacker, Georg, 1999. "Risk, complexity, and deviations from expected-value maximization: Results of a lottery choice experiment," Journal of Economic Psychology, Elsevier, vol. 20(6), pages 699-715, December.
    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. Benjamin Enke & Thomas Graeber & Ryan Oprea & Thomas W. Graeber, 2023. "Complexity and Hyperbolic Discounting," CESifo Working Paper Series 10861, CESifo.
    2. Cassidy Shubatt & Jeffrey Yang, 2024. "Similarity and Comparison Complexity," Papers 2401.17578, arXiv.org.

    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. Despoina Alempaki & Emina Canic & Timothy L. Mullett & William J. Skylark & Chris Starmer & Neil Stewart & Fabio Tufano, 2019. "Reexamining How Utility and Weighting Functions Get Their Shapes: A Quasi-Adversarial Collaboration Providing a New Interpretation," Management Science, INFORMS, vol. 65(10), pages 4841-4862, October.
    2. Ola Andersson & Håkan J. Holm & Jean-Robert Tyran & Erik Wengström, 2020. "Robust inference in risk elicitation tasks," Journal of Risk and Uncertainty, Springer, vol. 61(3), pages 195-209, December.
    3. Brice Corgnet & Roberto Hernán González, 2023. "On The Appeal Of Complexity," Working Papers 2312, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    4. Michael H. Birnbaum & Ulrich Schmidt & Miriam D. Schneider, 2017. "Testing independence conditions in the presence of errors and splitting effects," Journal of Risk and Uncertainty, Springer, vol. 54(1), pages 61-85, February.
    5. Buschena, David E. & Atwood, Joseph A., 2011. "Evaluation of similarity models for expected utility violations," Journal of Econometrics, Elsevier, vol. 162(1), pages 105-113, May.
    6. Matthew Ryan, 2018. "Uncertainty and binary stochastic choice," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(3), pages 629-662, May.
    7. Yoram Halevy, 2008. "Strotz Meets Allais: Diminishing Impatience and the Certainty Effect," American Economic Review, American Economic Association, vol. 98(3), pages 1145-1162, June.
    8. Glimcher, Paul W. & Tymula, Agnieszka A., 2023. "Expected subjective value theory (ESVT): A representation of decision under risk and certainty," Journal of Economic Behavior & Organization, Elsevier, vol. 207(C), pages 110-128.
    9. Andersen, Steffen & Harrison, Glenn W. & Lau, Morten Igel & Rutström, Elisabet E., 2010. "Behavioral econometrics for psychologists," Journal of Economic Psychology, Elsevier, vol. 31(4), pages 553-576, August.
    10. 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.
    11. Königsheim, C. & Lukas, M. & Nöth, M., 2019. "Salience theory: Calibration and heterogeneity in probability distortion," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 477-495.
    12. Michael Birnbaum & Ulrich Schmidt, 2008. "An experimental investigation of violations of transitivity in choice under uncertainty," Journal of Risk and Uncertainty, Springer, vol. 37(1), pages 77-91, August.
    13. 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..
    14. Blavatskyy, Pavlo, 2016. "Probability weighting and L-moments," European Journal of Operational Research, Elsevier, vol. 255(1), pages 103-109.
    15. Michael Theil, 2003. "The Value of Personal Contact in Marketing Insurance: Client Judgments of Representativeness and Mental Availability," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 6(2), pages 145-157, September.
    16. Matthew Ryan, 2015. "A Strict Stochastic Utility Theorem," Economics Bulletin, AccessEcon, vol. 35(4), pages 2664-2672.
    17. Abdellaoui, Mohammed & Kemel, Emmanuel & Panin, Amma & Vieider, Ferdinand M., 2019. "Measuring time and risk preferences in an integrated framework," Games and Economic Behavior, Elsevier, vol. 115(C), pages 459-469.
    18. Ferdinand M. Vieider & Peter Martinsson & Pham Khanh Nam & Nghi Truong, 2019. "Risk preferences and development revisited," Theory and Decision, Springer, vol. 86(1), pages 1-21, February.
    19. Blavatskyy, Pavlo R., 2008. "Stochastic utility theorem," Journal of Mathematical Economics, Elsevier, vol. 44(11), pages 1049-1056, December.
    20. Burghart, Daniel R. & Epper, Thomas & Fehr, Ernst, 2015. "The Ambiguity Triangle: Uncovering Fundamental Patterns of Behavior Under Uncertainty," IZA Discussion Papers 9150, Institute of Labor Economics (IZA).

    More about this item

    Keywords

    complexity; choice under risk; cognitive uncertainty; experiments;
    All these keywords.

    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:ces:ceswps:_10644. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.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.