IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v59y2013i2p358-375.html
   My bibliography  Save this article

Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

Author

Listed:
  • Daniel R. Cavagnaro

    (Mihaylo College of Business and Economics, California State University, Fullerton, Fullerton, California 92834)

  • Richard Gonzalez

    (Department of Psychology, University of Michigan, Ann Arbor, Michigan 48109)

  • Jay I. Myung

    (Department of Psychology, The Ohio State University, Columbus, Ohio 43210)

  • Mark A. Pitt

    (Department of Psychology, The Ohio State University, Columbus, Ohio 43210)

Abstract

Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called adaptive design optimization, adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate expected utility, weighted expected utility, original prospect theory, and cumulative prospect theory models. This paper was accepted by Teck Ho, decision analysis.

Suggested Citation

  • Daniel R. Cavagnaro & Richard Gonzalez & Jay I. Myung & Mark A. Pitt, 2013. "Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach," Management Science, INFORMS, vol. 59(2), pages 358-375, February.
    • Handle: RePEc:inm:ormnsc:v:59:y:2013:i:2:p:358-375
    DOI: 10.1287/mnsc.1120.1558
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.1120.1558
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.1120.1558?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. Birnbaum, Michael H. & Gutierrez, Roman J., 2007. "Testing for intransitivity of preferences predicted by a lexicographic semi-order," Organizational Behavior and Human Decision Processes, Elsevier, vol. 104(1), pages 96-112, September.
    3. John D. Hey, 2018. "Why We Should Not Be Silent About Noise," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 13, pages 309-329, World Scientific Publishing Co. Pte. Ltd..
    4. Chew, Soo Hong, 1983. "A Generalization of the Quasilinear Mean with Applications to the Measurement of Income Inequality and Decision Theory Resolving the Allais Paradox," Econometrica, Econometric Society, vol. 51(4), pages 1065-1092, July.
    5. Vermeulen, Bart & Goos, Peter & Vandebroek, Martina, 2008. "Models and optimal designs for conjoint choice experiments including a no-choice option," International Journal of Research in Marketing, Elsevier, vol. 25(2), pages 94-103.
    6. Deng, Xinwei & Joseph, V. Roshan & Sudjianto, Agus & Wu, C. F. Jeff, 2009. "Active Learning Through Sequential Design, With Applications to Detection of Money Laundering," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 969-981.
    7. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    8. 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..
    9. Wu, George & Gonzalez, Richard, 1998. "Common Consequence Conditions in Decision Making under Risk," Journal of Risk and Uncertainty, Springer, vol. 16(1), pages 115-139, April.
    10. Daria Dzyabura & John R. Hauser, 2011. "Active Machine Learning for Consideration Heuristics," Marketing Science, INFORMS, vol. 30(5), pages 801-819, September.
    11. Henry Stott, 2006. "Cumulative prospect theory's functional menagerie," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 101-130, March.
    12. Pavlo Blavatskyy, 2007. "Stochastic expected utility theory," Journal of Risk and Uncertainty, Springer, vol. 34(3), pages 259-286, June.
    13. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    14. Chris Starmer, 2000. "Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk," Journal of Economic Literature, American Economic Association, vol. 38(2), pages 332-382, June.
    15. Srinivasan, V. Seenu & Netzer, Oded, 2007. "Adaptive Self-Explication of Multi-attribute Preferences," Research Papers 1979, Stanford University, Graduate School of Business.
    16. Meichun Ding & Gary L. Rosner & Peter Müller, 2008. "Bayesian Optimal Design for Phase II Screening Trials," Biometrics, The International Biometric Society, vol. 64(3), pages 886-894, September.
    17. George Wu & Jiao Zhang & Mohammed Abdellaoui, 2005. "Testing Prospect Theories Using Probability Tradeoff Consistency," Journal of Risk and Uncertainty, Springer, vol. 30(2), pages 107-131, January.
    18. Michael Birnbaum, 2005. "A Comparison of Five Models that Predict Violations of First-Order Stochastic Dominance in Risky Decision Making," Journal of Risk and Uncertainty, Springer, vol. 31(3), pages 263-287, December.
    19. Machina, Mark J, 1982. ""Expected Utility" Analysis without the Independence Axiom," Econometrica, Econometric Society, vol. 50(2), pages 277-323, March.
    20. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    21. Linda M. Haines & Inna Perevozskaya & William F. Rosenberger, 2003. "Bayesian Optimal Designs for Phase I Clinical Trials," Biometrics, The International Biometric Society, vol. 59(3), pages 591-600, September.
    22. Peter Muller & Bruno Sanso & Maria De Iorio, 2004. "Optimal Bayesian Design by Inhomogeneous Markov Chain Simulation," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 788-798, January.
    23. Camerer, Colin F, 1989. "An Experimental Test of Several Generalized Utility Theories," Journal of Risk and Uncertainty, Springer, vol. 2(1), pages 61-104, April.
    24. Aigner, Dennis J., 1979. "A brief introduction to the methodology of optimal experimental design," Journal of Econometrics, Elsevier, vol. 11(1), pages 7-26, September.
    25. Mohammed Abdellaoui, 2000. "Parameter-Free Elicitation of Utility and Probability Weighting Functions," Management Science, INFORMS, vol. 46(11), pages 1497-1512, November.
    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. Balcombe, Kelvin & Fraser, Iain, 2024. "A Note on an Alternative Approach to Experimental Design of Lottery Prospects," MPRA Paper 119743, University Library of Munich, Germany.
    2. Daniel R. Cavagnaro & Gabriel J. Aranovich & Samuel M. McClure & Mark A. Pitt & Jay I. Myung, 2016. "On the functional form of temporal discounting: An optimized adaptive test," Journal of Risk and Uncertainty, Springer, vol. 52(3), pages 233-254, June.
    3. Krzysztof Kontek, 2018. "Boundary effects in the Marschak-Machina triangle," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(6), pages 587-606, November.
    4. Ryan O. Murphy & Robert H. W. ten Brincke, 2018. "Hierarchical Maximum Likelihood Parameter Estimation for Cumulative Prospect Theory: Improving the Reliability of Individual Risk Parameter Estimates," Management Science, INFORMS, vol. 64(1), pages 308-328, January.
    5. Xiaoxue Sherry Gao & Glenn W. Harrison & Rusty Tchernis, 2023. "Behavioral welfare economics and risk preferences: a Bayesian approach," Experimental Economics, Springer;Economic Science Association, vol. 26(2), pages 273-303, April.
    6. Jonathan Chapman & Erik Snowberg & Stephanie Wang & Colin Camerer, 2018. "Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)," CESifo Working Paper Series 7262, CESifo.
    7. Daniel Cavagnaro & Mark Pitt & Richard Gonzalez & Jay Myung, 2013. "Discriminating among probability weighting functions using adaptive design optimization," Journal of Risk and Uncertainty, Springer, vol. 47(3), pages 255-289, December.
    8. Kontek, Krzysztof, 2015. "Fanning-Out or Fanning-In? Continuous or Discontinuous? Estimating Indifference Curves Inside the Marschak-Machina Triangle using Certainty Equivalents," MPRA Paper 63965, University Library of Munich, Germany.
    9. Lisheng He & Pantelis P. Analytis & Sudeep Bhatia, 2022. "The Wisdom of Model Crowds," Management Science, INFORMS, vol. 68(5), pages 3635-3659, May.
    10. repec:cup:judgdm:v:13:y:2018:i:6:p:587-606 is not listed on IDEAS
    11. Haag, Fridolin & Chennu, Arjun, 2023. "Assessing whether decisions are more sensitive to preference or prediction uncertainty with a value of information approach," Omega, Elsevier, vol. 121(C).
    12. Haag, Fridolin & Lienert, Judit & Schuwirth, Nele & Reichert, Peter, 2019. "Identifying non-additive multi-attribute value functions based on uncertain indifference statements," Omega, Elsevier, vol. 85(C), pages 49-67.

    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. Simone Ferrari-Toniolo & Leo Chi U. Seak & Wolfram Schultz, 2022. "Risky choice: Probability weighting explains independence axiom violations in monkeys," Journal of Risk and Uncertainty, Springer, vol. 65(3), pages 319-351, December.
    2. Simone Cerreia‐Vioglio & David Dillenberger & Pietro Ortoleva, 2015. "Cautious Expected Utility and the Certainty Effect," Econometrica, Econometric Society, vol. 83, pages 693-728, March.
    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. Adam Booij & Bernard Praag & Gijs Kuilen, 2010. "A parametric analysis of prospect theory’s functionals for the general population," Theory and Decision, Springer, vol. 68(1), pages 115-148, February.
    5. Pavlo Blavatskyy, 2018. "A second-generation disappointment aversion theory of decision making under risk," Theory and Decision, Springer, vol. 84(1), pages 29-60, January.
    6. 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.
    7. Pavlo Blavatskyy, 2007. "Stochastic expected utility theory," Journal of Risk and Uncertainty, Springer, vol. 34(3), pages 259-286, June.
    8. Peter Brooks & Horst Zank, 2005. "Loss Averse Behavior," Journal of Risk and Uncertainty, Springer, vol. 31(3), pages 301-325, December.
    9. Jakusch, Sven Thorsten & Meyer, Steffen & Hackethal, Andreas, 2019. "Taming models of prospect theory in the wild? Estimation of Vlcek and Hens (2011)," SAFE Working Paper Series 146, Leibniz Institute for Financial Research SAFE, revised 2019.
    10. Kontek, Krzysztof, 2015. "Fanning-Out or Fanning-In? Continuous or Discontinuous? Estimating Indifference Curves Inside the Marschak-Machina Triangle using Certainty Equivalents," MPRA Paper 63965, University Library of Munich, Germany.
    11. Arjan Verschoor & Ben D’Exelle, 2022. "Probability weighting for losses and for gains among smallholder farmers in Uganda," Theory and Decision, Springer, vol. 92(1), pages 223-258, February.
    12. Peter Brooks & Simon Peters & Horst Zank, 2014. "Risk behavior for gain, loss, and mixed prospects," Theory and Decision, Springer, vol. 77(2), pages 153-182, August.
    13. Pavlo R. Blavatskyy, "undated". "A Stochastic Expected Utility Theory," IEW - Working Papers 231, Institute for Empirical Research in Economics - University of Zurich.
    14. Daniel Cavagnaro & Mark Pitt & Richard Gonzalez & Jay Myung, 2013. "Discriminating among probability weighting functions using adaptive design optimization," Journal of Risk and Uncertainty, Springer, vol. 47(3), pages 255-289, December.
    15. Gijs van de Kuilen & Peter P. Wakker, 2011. "The Midweight Method to Measure Attitudes Toward Risk and Ambiguity," Management Science, INFORMS, vol. 57(3), pages 582-598, March.
    16. George Wu & Alex B. Markle, 2008. "An Empirical Test of Gain-Loss Separability in Prospect Theory," Management Science, INFORMS, vol. 54(7), pages 1322-1335, July.
    17. Glenn W. Harrison & J. Todd Swarthout, 2016. "Cumulative Prospect Theory in the Laboratory: A Reconsideration," Experimental Economics Center Working Paper Series 2016-04, Experimental Economics Center, Andrew Young School of Policy Studies, Georgia State University.
    18. Liang Zou, 2006. "An Alternative to Prospect Theory," Annals of Economics and Finance, Society for AEF, vol. 7(1), pages 1-28, May.
    19. Robin Cubitt & Daniel Navarro-Martinez & Chris Starmer, 2015. "On preference imprecision," Journal of Risk and Uncertainty, Springer, vol. 50(1), pages 1-34, February.
    20. Pavlo R. Blavatskyy & Ganna Pogrebna, 2010. "Models of stochastic choice and decision theories: why both are important for analyzing decisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 963-986.

    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:inm:ormnsc:v:59:y:2013:i:2:p:358-375. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.