Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters

Published Online:https://doi.org/10.1287/mnsc.1120.1570

References

  • Abdellaoui M, Bleichrodt H, L'Haridon O. A tractable method to measure utility and loss aversion under prospect theory. J. Risk Uncertainty (2008) 36(3):245–266CrossrefGoogle Scholar
  • Abdellaoui M, Bleichrodt H, Paraschiv C. Loss aversion under prospect theory: A parameter-free measurement. Management Sci. (2007) 53(10):1659–1674LinkGoogle Scholar
  • Abernethy J, Evgeniou T, Toubia O, Vert J-P. Eliciting consumer preferences using robust adaptive choice questionnaires. IEEE Trans. Knowledge Data Engrg. (2008) 20(2):145–155CrossrefGoogle Scholar
  • Allenby GM, Ginter JL. Using extremes to design products and segment markets. J. Marketing Res. (1995) 32(4):392–403CrossrefGoogle Scholar
  • Allenby GM, Rossi PE. Marketing models of consumer heterogeneity. J. Econometrics (1999) 89(1–2):57–78CrossrefGoogle Scholar
  • Allenby GM, Arora N, Ginter JL. Incorporating prior knowledge into the analysis of conjoint studies. J. Marketing Res. (1995) 32(2):152–162CrossrefGoogle Scholar
  • Andrews RL, Ainslie A, Currim IS. An empirical comparison of logit choice models with discrete versus continuous representations of heterogeneity. J. Marketing Res. (2002a) 39(4):479–487CrossrefGoogle Scholar
  • Andrews RL, Ansari A, Currim IS. Hierarchical Bayes versus finite mixture conjoint analysis models: A comparison of fit, prediction, and partworth recovery. J. Marketing Res. (2002b) 39(1):87–98CrossrefGoogle Scholar
  • Angeletos G-M, Laibson D, Repetto A, Tobacman J, Weinberg S. The hyperbolic consumption model: Calibration, simulation, and empirical evaluation. J. Econom. Perspect. (2001) 15(3):47–68CrossrefGoogle Scholar
  • Ansari A, Mela CF. E-customization. J. Marketing Res. (2003) 40(2):131–145CrossrefGoogle Scholar
  • Appelt KC, Johnson EJ, Knoll MAZ, Westfall JE. Time to retire: Why Americans claim benefits early and how to encourage them to delay. (2011) . Working paper, Graduate School of Business, Columbia University, New YorkGoogle Scholar
  • Ashraf N, Karlan D, Yin W. Tying Odysseus to the mast: Evidence from a commitment savings product in the Phillippines. Quart. J. Econom. (2006) 40(1):635–72CrossrefGoogle Scholar
  • Barberis N, Huang M, Santos T. Prospect theory and asset prices. Quart. J. Econom. (2001) 116(1):1–53CrossrefGoogle Scholar
  • Baucells M, Heukamp FH. Probability and time trade-off. Management Sci. (2012) 58(4):831–842LinkGoogle Scholar
  • Benhabib J, Bisin A, Schotter A. Present-bias, quasi-hyperbolic discounting, and fixed costs. Games Econom. Behav. (2010) 69(2):205–223CrossrefGoogle Scholar
  • Bruhin A, Fehr-Duda H, Epper T. Risk and rationality: Uncovering heterogeneity in probability distortion. Econometrica (2010) 78(4):1375–1412CrossrefGoogle Scholar
  • Buhrmester M, Kwang T, Gosling SD. Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspect. Psych. Sci. (2011) 6(1):3–5CrossrefGoogle Scholar
  • Camerer CF, Loewenstein G, Rabin M. Advances in Behavioral Economics (2003) (Princeton University Press, Princeton, NJ) Google Scholar
  • Carney D, Toubia O, Johnson EJ. Stable and transient influences of hormone levels on time and risk preferences. (2011) . Working paper, Columbia Business School, Columbia University, New YorkGoogle Scholar
  • Casey JT, Delquié P. Stated vs. implicit willingness to pay under risk. Organ. Behav. Human Decision Processes (1995) 61(2):123–137CrossrefGoogle Scholar
  • Cavagnaro DR, Gonzalez R, Myung JI, Pitt MA. Optimal decision stimuli for risky choice experiments: An adaptive approach. (2012) . Working paper, Ohio State University, ColumbusGoogle Scholar
  • Chaloner K, Verdinelli I. Bayesian experimental design: A review. Statist. Sci. (1995) 10(3):273–304CrossrefGoogle Scholar
  • De Groot M. Optimal Statistical Decisions (1970) (McGraw-Hill, New York) Google Scholar
  • Deutskens E, De Ruyter K, Wetzels M, Paul O. Response rate and response quality of Internet-based surveys: An experimental study. Marketing Lett. (2004) 15(1):21–36CrossrefGoogle Scholar
  • Ding M. An incentive-aligned mechanism for conjoint analysis. J. Marketing Res. (2007) 44(2):214–223CrossrefGoogle Scholar
  • Dzyabura D, Hauser JR. Active machine learning for consideration heuristics. Marketing Sci. (2011) 30(5):801–819LinkGoogle Scholar
  • Ebert JE, Prelec D. The fragility of time: Time-insensitivity and valuation of the near and far future. Management Sci. (2007) 53(9):1423–1438LinkGoogle Scholar
  • Evgeniou T, Pontil M, Toubia O. A convex optimization approach to modeling consumer heterogeneity in conjoint estimation. Marketing Sci. (2007) 26(6LinkGoogle Scholar
  • Ford I, Kitsos CP, Titterington DM. Recent advances in nonlinear experimental designs. Technometrics (1989) 31(1):49–60CrossrefGoogle Scholar
  • Frederick S, Loewenstein G, O'Donoghue T. Time discounting and time preference: A critical review. J. Econom. Literature (2002) 40(2):351–401CrossrefGoogle Scholar
  • Gaechter S, Johnson EJ, Herrmann A. Measuring individual-level loss aversion using simple experiments. (2011) . Working paper, Graduate School of Business, Columbia University, New YorkGoogle Scholar
  • Galesic M, Bosnjak M. Effects of questionnaire length on participation and indicators of response quality in a Web survey. Public Opinion Quart. (2009) 73(2):349–360CrossrefGoogle Scholar
  • Green PE, Rao VR. Configuration synthesis in multidimensional scaling. J. Marketing Res. (1972) 9(1):65–68CrossrefGoogle Scholar
  • Greene W. Econometric Analysis (2000) (Prentice-Hall, Upper Saddle River, NJ) Google Scholar
  • Harrison GW. An experimental test for risk aversion. Econom. Lett. (1986) 21(1):7–11CrossrefGoogle Scholar
  • Harrison GW, Lau MI, Williams MB. Estimating individual discount rates in Denmark: A field experiment. Amer. Econom. Rev. (2002) 92(5):1606–1617CrossrefGoogle Scholar
  • Hey J, Orme C. Investigating generalizations of expected utility theory using experimental data. Econometrica (1994) 62(6):1291–1326CrossrefGoogle Scholar
  • Hsee CK, Rottenstreich Y. Music, pandas, and muggers: On the affective psychology of value. J. Experiment. Psych. (2004) 133(1):23–30CrossrefGoogle Scholar
  • Huber J, Zwerina K. The importance of utility balance in efficient choice designs. J. Marketing Res. (1996) 33(3):307–317CrossrefGoogle Scholar
  • Jarnebrant P, Toubia O, Johnson E. The silver lining effect: Formal analysis and experiments. Management Sci. (2009) 55(11):1832–1841LinkGoogle Scholar
  • Johnson EJ, Atlas SA, Payne JW. Time preferences, mortgage choice, and strategic default. (2011) . Working paper, Columbia Business School, Columbia University, New YorkGoogle Scholar
  • Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk. Econometrica (1979) 47(2):263–292CrossrefGoogle Scholar
  • Kamakura WA, Russell GJ. A probabilistic choice model for market segmentation and elasticity structure. J. Marketing Res. (1989) 26(4):379–390CrossrefGoogle Scholar
  • Kim JG, Menzefricke U, Feinberg FM. Assessing heterogeneity in discrete choice models using a Dirichlet process prior. Rev. Marketing Sci. (2004) 2(1):1–39CrossrefGoogle Scholar
  • Kuhfeld WF, Tobias RD, Garratt M. Efficient experimental design with marketing applications. J. Marketing Res. (1994) 31(4):545–557CrossrefGoogle Scholar
  • Laibson D. Golden eggs and hyperbolic discounting. Quart. J. Econom. (1997) 112(2):443–477CrossrefGoogle Scholar
  • Laskey KB, Fischer GW. Estimating utility functions in the presence of response error. Management Sci. (1987) 33(8):965–980LinkGoogle Scholar
  • Lenk PJ, DeSarbo WS, Green PE, Young MR. Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. (1996) 15(2):173–191LinkGoogle Scholar
  • Levav J, Heitmann M, Herrmann A, Iyengar SS. Order in product customization decisions: Evidence from field experiments. J. Political Econom. (2010) 118(2):274–299CrossrefGoogle Scholar
  • Liu EM, Huang J. Risk preferences and pesticide use by cotton farmers in China. (2011) . Working paper, University of Houston, HoustonGoogle Scholar
  • Loewenstein G, Prelec D. Negative time preference. Amer. Econom. Rev. (1991) 81(2):347–352Google Scholar
  • Luce RD. A probabilistic theory of utility. Econometrica (1958) 26(2):193–224CrossrefGoogle Scholar
  • McFadden D, Zarembka P. Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics (1974) (Academic Press, New York) 105–142Google Scholar
  • Meier S, Sprenger C. Discounting and defaulting: Evidence from time preference experiments and administrative credit card data. (2009) . Working paper, University of California, San Diego, La JollaGoogle Scholar
  • Meier S, Sprenger C. Present-biased preferences and credit card borrowing. Amer. Econom. J.—Appl. Econom. (2010) 2(1):193–210CrossrefGoogle Scholar
  • Newey WK, McFadden D, Engle RF, McFadden DL. Large sample estimation and hypothesis testing. Handbook of Econometrics (1994) IV(Elsevier Science, Amsterdam) 2111–2245Google Scholar
  • Nilsson H, Rieskamp J, Wagenmakers E-J. Hierarchical Bayesian parameter estimation for cumulative prospect theory. J. Math. Psych. (2011) 55(1):84–93CrossrefGoogle Scholar
  • O'Donoghue T, Rabin M. Doing it now or later. Amer. Econom. Rev. (1999) 89(1):103–124CrossrefGoogle Scholar
  • Paolacci G, Chandler J, Ipeirotis PG. Running experiments on Amazon Mechanical Turks. Judgment Decision Making (2010) 5(5):411–419Google Scholar
  • Phelps ES, Pollak RA. On second-best national saving and game-equilibrium growth. Rev. Econom. Stud. (1968) 35(2):185–199CrossrefGoogle Scholar
  • Prelec D. The probability weighing function. Econometrica (1998) 66(3):497–527CrossrefGoogle Scholar
  • Qiu J, Steiger E-M. Understanding the two components of risk attitudes: An experimental analysis. Management Sci. (2011) 57(1):193–199LinkGoogle Scholar
  • Rossi PE, Allenby GM. Bayesian statistics and marketing. Marketing Sci. (2003) 22(3):304–328LinkGoogle Scholar
  • Rossi PE, Allenby GM, McCulloch R. Bayesian Statistics and Marketing (2005) (John Wiley & Sons, New York) CrossrefGoogle Scholar
  • Sándor Z, Wedel M. Designing conjoint choice experiments using managers' prior beliefs. J. Marketing Res. (2001) 38(4):430–444CrossrefGoogle Scholar
  • Sapienza P, Zingales L, Maestripieri D. Gender differences in financial risk aversion and career choices are affected by testosterone. Proc. Natl. Acad. Sci. USA (2009) 106(36):15268–15273CrossrefGoogle Scholar
  • Sawtooth Software ACA system for adaptive conjoint analysis. (1996) . Technical paper, Sawtooth Software, Sequim, WA. Accessed September 7, 2012, http://www.sawtoothsoftware.com/download/techpap/acatech.pdfGoogle Scholar
  • Srinivasan V, Shocker AD. Estimating the weights for multiple attributes in a composite criterion using pairwise judgments. Psychometrika (1973) 38(4):473–493CrossrefGoogle Scholar
  • Stanton SJ, Mullette-Gillman O', McLaurin ED, Kuhn CM, LaBar KS, Platt ML, Huettel SA. Low- and high-testosterone individuals exhibit decreased aversion to economic risk. Psych. Sci. (2011) 22(4):447–453CrossrefGoogle Scholar
  • Starmer C, Sugden R. Does the random-lottery incentive system elicit true preferences? An experimental investigation. Amer. Econom. Rev. (1991) 81(4):971–978Google Scholar
  • Steinberg DM, Hunter WG. Experimental design: Review and comment. Technometrics (1984) 26(2):71–97CrossrefGoogle Scholar
  • Stott HP. Cumulative prospect theory's functional menagerie. J. Risk Uncertainty (2006) 32(2):101–130CrossrefGoogle Scholar
  • Tanaka T, Camerer CF, Nguyen Q. Risk and time preferences: Linking experimental and household survey data for Vietnam. Amer. Econom. Rev. (2010) 100(1):557–571CrossrefGoogle Scholar
  • Thaler R. Mental accounting and consumer choice. Marketing Sci. (1985) 4(3):199–214LinkGoogle Scholar
  • Tom SM, Fox CR, Trepel C, Poldrack RA. The neural basis of loss aversion in decision making under risk. Science (2007) 315(5811):515–518CrossrefGoogle Scholar
  • Toubia O, Evgeniou T, Hauser J, Gustafsson A, Herrmann A, Huber F. Optimization-based and machine-learning methods for conjoint analysis: Estimation and question design. Conjoint Measurement: Methods and Applications (2007a) 4th ed.(Springer-Verlag, New York) 231–258CrossrefGoogle Scholar
  • Toubia O, Hauser JR, Garcia R. Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Sci. (2007b) 26(5):596–610LinkGoogle Scholar
  • Toubia O, Hauser JR, Simester DI. Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. (2004) 41(1):116–131CrossrefGoogle Scholar
  • Toubia O, Simester D, Hauser JR, Dahan E. Fast polyhedral conjoint estimation. Marketing Sci. (2003) 22(3):274–303LinkGoogle Scholar
  • Trope Y, Liberman N, Wakslak C. Construal levels and psychological distance: Effects on representation, prediction, evaluation, and behavior. J. Consumer Psych. (2007) 17(2):83–95CrossrefGoogle Scholar
  • Tversky A, Kahneman D. Advances in prospect theory: Cumulative representation of uncertainty. J. Risk Uncertainty (1992) 5(4):297–323CrossrefGoogle Scholar
  • Vohs K, Baumeister RF, Schmeichel BJ, Twenge JM, Nelson NM, Tice DM. Making choices impairs subsequent self-control: A limited-resource account of decision making, self-regulation, and active initiative. J. Personality Soc. Psych. (2008) 94(5):883–898CrossrefGoogle Scholar
  • Wakker P, Deneffe D. Eliciting von Neumann-Morgenstern utilities when probabilities are distorted or unknown. Management Sci. (1996) 42(8):1131–1150LinkGoogle Scholar
  • Wang SW, Filiba M, Camerer CF. Dynamically optimized sequential experimentation (DOSE) for estimating economic preference parameters. (2010) . Working paper, California Institute of Technology, PasadenaGoogle Scholar
  • Weber EU, Johnson EJ, Milch KF, Brodscholl HCJC, Goldstein DG. Asymmetric discounting in intertemporal choice—A query-theory account. Psych. Sci. (2007) 18(6):516–523CrossrefGoogle Scholar
  • Wu G, Gonzalez R. Curvature of the probability weighting function. Management Sci. (1996) 42(12):1676–1690LinkGoogle Scholar
  • Zauberman G. The intertemporal dynamics of consumer lock-in. J. Consumer Res. (2003) 30(3):405–419CrossrefGoogle Scholar
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