Marketing Letters

, Volume 25, Issue 3, pp 269–280 | Cite as

Beware of black swans: Taking stock of the description–experience gap in decision under uncertainty

  • André de PalmaEmail author
  • Mohammed Abdellaoui
  • Giuseppe Attanasi
  • Moshe Ben-Akiva
  • Ido Erev
  • Helga Fehr-Duda
  • Dennis Fok
  • Craig R. Fox
  • Ralph Hertwig
  • Nathalie Picard
  • Peter P. Wakker
  • Joan L. Walker
  • Martin Weber


Uncertainty pervades most aspects of life. From selecting a new technology to choosing a career, decision makers rarely know in advance the exact outcomes of their decisions. Whereas the consequences of decisions in standard decision theory are explicitly described (the decision from description (DFD) paradigm), the consequences of decisions in the recent decision from experience (DFE) paradigm are learned from experience. In DFD, decision makers typically overrespond to rare events. That is, rare events have more impact on decisions than their objective probabilities warrant (overweighting). In DFE, decision makers typically exhibit the opposite pattern, underresponding to rare events. That is, rare events may have less impact on decisions than their objective probabilities warrant (underweighting). In extreme cases, rare events are completely neglected, a pattern known as the “Black Swan effect.” This contrast between DFD and DFE is known as a description–experience gap. In this paper, we discuss several tentative interpretations arising from our interdisciplinary examination of this gap. First, while a source of underweighting of rare events in DFE may be sampling error, we observe that a robust description–experience gap remains when these factors are not at play. Second, the residual description–experience gap is not only about experience per se but also about the way in which information concerning the probability distribution over the outcomes is learned in DFE. Econometric error theories may reveal that different assumed error structures in DFD and DFE also contribute to the gap.


Black swans Risk Ambiguity Fourfold pattern (Non)-expected utility Probabilistic choices Experience-based decision making Description-based decision making 



We thank two anonymous referees and an associate editor for their useful comments and suggestions. We also thank Benedict Dellaert and Bas Donkers for organizing this successful 9th Invitational Choice Symposium.


  1. Abdellaoui, M., Baillon, A., Placido, L., & Wakker, P. P. (2011a). The rich domain of uncertainty: source functions and their experimental implementation. American Economic Review, 101, 699–727.Google Scholar
  2. Abdellaoui, M., l’Haridon, O., & Paraschiv, C. (2011b). Experienced vs. described uncertainty: do we need two prospect theory specifications? Management Science, 57, 1879–1895.CrossRefGoogle Scholar
  3. Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’école américaine. Econometrica, 21, 503–546.CrossRefGoogle Scholar
  4. Barron, G., & Erev, I. (2003). Small feedback-based decisions and their limited correspondence to description-based decisions. Journal of Behavioral Decision Making, 16, 215–233.CrossRefGoogle Scholar
  5. Ben-Akiva, M., de Palma, A., McFadden, D., Abou-Zeid, M., Chiappori, P. A., de Lapparent, M., et al. (2012). Process and context in choice models. Marketing Letters, 23, 439–456.CrossRefGoogle Scholar
  6. Camerer, C., & Weber, M. (1992). Recent development in modeling preferences: uncertainty and ambiguity. Journal of Risk and Uncertainty, 5, 325–370.CrossRefGoogle Scholar
  7. de Palma, A., & Picard, N. (2010). Measuring individual-specific risk aversion, loss aversion and probability weighting. Unpublished manuscript, Paris Ecole Polytechnique.Google Scholar
  8. de Palma, A., Picard, N., & Ziegelmeyer, A. (2011). Individual and couple decision behavior under risk: evidence on the dynamics of power balance. Theory and Decision, 70, 45–64.CrossRefGoogle Scholar
  9. Dimmock, S. G., Kouwenberg, R., Mitchell, O. S., & Peijnenburg, K. (2013). Ambiguity aversion and household portfolio choice: empirical evidence. Unpublished manuscript, NBER.Google Scholar
  10. Ellsberg, D. (1961). Risk, ambiguity and the Savage axioms. Quarterly Journal of Economics, 75, 643–669.CrossRefGoogle Scholar
  11. Erev, I., Glozman, I., & Hertwig, R. (2008). What impacts the impact of rare events. Journal of Risk and Uncertainty, 36, 153–177.CrossRefGoogle Scholar
  12. Erev, I., Ert, E., & Roth, A. E. (2010a). A choice prediction competition for market entry games: an introduction. Games, 1, 117–136.CrossRefGoogle Scholar
  13. Erev, I., Ert, E., Roth, A. E., Haruvy, E., Herzog, S. M., Hau, R., et al. (2010b). A choice prediction competition: choices from experience and from description. Journal of Behavioral Decision Making, 23, 15–47.CrossRefGoogle Scholar
  14. Fehr-Duda, H., & Epper, T. (2012). Probability and risk: foundations and economic implications of probability-dependent risk preferences. Annual Review of Economics, 4, 567–593.CrossRefGoogle Scholar
  15. Fehr-Duda, H., Bruhin, A., Epper, T., & Schubert, R. (2010). Rationality on the rise: why relative risk aversion increases with stake size. Journal of Risk and Uncertainty, 40, 147–180.CrossRefGoogle Scholar
  16. Fox, C. R., & Hadar, L. (2006). “Decisions from experience” = sampling error plus prospect theory: reconsidering Hertwig, Barron, Weber & Erev (2004). Judgment and Decision Making, 2, 159–161.Google Scholar
  17. Fox, C. R., & See, K. E. (2003). Belief and preference in decision under uncertainty. In D. Hardman & L. Macchi (Eds.), Thinking: current perspectives on reasoning, judgment, and decision making (pp. 273–314). Hoboken: Wiley.Google Scholar
  18. Fox, C. R., & Tversky, A. (1995). Ambiguity aversion and comparative ignorance. Quarterly Journal of Economics, 110, 585–603.CrossRefGoogle Scholar
  19. Fox, C. R., & Tversky, A. (1998). A belief-based account of decision under uncertainty. Management Science, 44, 879–895.CrossRefGoogle Scholar
  20. Fox, C. R., & Weber, M. (2002). Ambiguity aversion, comparative ignorance and decision context. Organizational Behavior and Human Decision Processes, 88, 476–498.CrossRefGoogle Scholar
  21. Fox, C. R., Long, A., Hadar, L., & Erner, C. (2013). Unpacking decisions from description and experience. Unpublished manuscript, UCLA Anderson School of Management.Google Scholar
  22. Gonzalez, C., & Gutt, V. (2011). Instance-based learning: integrating sampling and repeated decisions form experience. Psychological Review, 118, 523–551.CrossRefGoogle Scholar
  23. Hadar, L., & Fox, C. R. (2009). Information asymmetry in decisions from description versus decisions from experience. Judgment and Decision Making, 4, 317–325.Google Scholar
  24. Hasher, L., & Zachs, L. T. (1984). Automatic processing of fundamental information: the case of frequency of occurrence. American Psychologist, 39, 1372–1388.CrossRefGoogle Scholar
  25. Heath, C., & Tversky, A. (1991). Preference and belief: ambiguity and competence in choice under uncertainty. Journal of Risk and Uncertainty, 4, 5–28.CrossRefGoogle Scholar
  26. Hertwig, R., & Erev, I. (2009). The description-experience gap in risky choice. Trends in Cognitive Science, 13, 517–523.CrossRefGoogle Scholar
  27. Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15, 534–539.CrossRefGoogle Scholar
  28. Hills, T., & Hertwig, R. (2010). Information search and decisions from experience: do our patterns of sampling foreshadow our decisions? Psychological Science, 21, 1787–1792.CrossRefGoogle Scholar
  29. Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47, 263–291.CrossRefGoogle Scholar
  30. Rabin, M. (2000). Risk aversion and expected-utility theory: a calibration theorem. Econometrica, 68, 1281–1292.CrossRefGoogle Scholar
  31. Rottenstreich, Y., & Hsee, C. (2001). Money, kisses, and electric shocks: on the affective psychology of risk. Psychological Science, 12, 185–190.CrossRefGoogle Scholar
  32. Rottenstreich, Y., & Tversky, A. (1997). Unpacking, repacking, and anchoring: advances in support theory. Psychological Review, 104, 406–415.CrossRefGoogle Scholar
  33. Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.Google Scholar
  34. Train, K. (2009). Discrete choice methods with simulation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  35. Tversky, A., & Fox, C. R. (1995). Weighing risk and uncertainty. Psychological Review, 102, 269–283.CrossRefGoogle Scholar
  36. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.CrossRefGoogle Scholar
  37. Tversky, A., & Koehler, D. J. (1994). Support theory: a nonextensional representation of subjective probability. Psychological Review, 101, 547–567.CrossRefGoogle Scholar
  38. Tversky, A., & Wakker, P. P. (1995). Risk attitudes and decision weights. Econometrica, 63, 1255–1280.CrossRefGoogle Scholar
  39. Ungemach, C., Chater, N., & Stewart, N. (2009). Are probabilities overweighted or underweighted, when rare outcomes are experienced (rarely)? Psychological Science, 20, 473–479.CrossRefGoogle Scholar
  40. Wakker, P. P. (2010). Prospect theory: For risk and ambiguity. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  41. Walker, J. L., & Ben-Akiva, M. (2011). Advances in discrete choice: mixtures models. In A. de Palma, R. Lindsey, E. Quinet, & R. Vickerman (Eds.), Handbook in transport economics (pp. 160–187). Cheltenham: Edward Elgar.Google Scholar
  42. Wilcox, N. T. (2008). Stochastic models for binary discrete choice under risk: a critical primer and econometric comparison. In J. C. Cox & G. W. Harrison (Eds.), Risk aversion in experiments (research in experimental economics 12) (pp. 197–292). Bingley: Emerald.CrossRefGoogle Scholar
  43. Yechiam, E., Barron, G., & Erev, I. (2005). The role of personal experience in contributing to different patterns of response to rare terrorist attacks. Journal of Conflict Resolution, 49, 430–439.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • André de Palma
    • 1
    Email author
  • Mohammed Abdellaoui
    • 2
  • Giuseppe Attanasi
    • 3
  • Moshe Ben-Akiva
    • 4
  • Ido Erev
    • 5
  • Helga Fehr-Duda
    • 6
  • Dennis Fok
    • 7
  • Craig R. Fox
    • 8
  • Ralph Hertwig
    • 9
  • Nathalie Picard
    • 10
  • Peter P. Wakker
    • 7
  • Joan L. Walker
    • 11
  • Martin Weber
    • 12
  1. 1.Economics and Management DepartmentEcole Normale Supérieure de CachanCachan CedexFrance
  2. 2.GREGHEC, HEC-ParisJouy-en-JosasFrance
  3. 3.BETA, Université de StrasbourgStrasbourg CedexFrance
  4. 4.Department of Civil and Environmental EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  5. 5.William Davidson Faculty of Industrial Engineering and Management, TechnionIsrael Institute of TechnologyTechnion CityIsrael
  6. 6.Chair of Economics, Swiss Federal Institute of Technology ZurichZurichSwitzerland
  7. 7.Erasmus School of EconomicsErasmus University RotterdamRotterdamThe Netherlands
  8. 8.Department of PsychologyUCLA Anderson School of ManagementLos AngelesUSA
  9. 9.Center for Adaptive Rationality, Max Planck Institute of Human DevelopmentBerlinGermany
  10. 10.THEMA, University of Cergy-PontoiseCergy-Pontoise CedexFrance
  11. 11.Department of Civil and Environmental EngineeringUniversity of California BerkeleyBerkeleyUSA
  12. 12.School of BusinessUniversity of MannheimMannheimGermany

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