Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-x5gtn Total loading time: 0 Render date: 2024-05-31T20:49:45.565Z Has data issue: false hasContentIssue false

Chapter 5 - The J/DM Separation Paradox and the Reliance on the Small Samples Hypothesis

from Part II - Sampling Mechanisms

Published online by Cambridge University Press:  01 June 2023

Klaus Fiedler
Affiliation:
Universität Heidelberg
Peter Juslin
Affiliation:
Uppsala Universitet, Sweden
Jerker Denrell
Affiliation:
University of Warwick
Get access

Summary

Previous research demonstrates a large difference between decisions from description and decisions from experience, and also between decisions and probability judgment from experience. Comparison of decisions from description and from experience reveals a description–experience gap (Hertwig & Erev, 2009): higher sensitivity to rare events in decisions from description. Comparison of judgment and decisions from experience reveals the coexistence of overestimation and underweighting of rare events (Barron & Yechiam, 2009). The current review suggests that both sets of differences are examples of the J/DM separation paradox: While separated studies of judgment and decision making reveal oversensitivity to rare events, without the separation, these processes often lead to the opposite bias. Our analysis shows that the J/DM paradox can be the product of the fact that the separation of judgment from decisions making requires an explicit presentation of the rare events, and this mere presentation increases the apparent weighting of these events. In addition, our analysis suggests that feedback diminishes the mere presentation effect, but does not guarantee increase in rational behavior. When people can rely on accurate feedback, the main deviations from rational judgment and decision making can be captured with the reliance on the small samples hypothesis.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdellaoui, M., L’Haridon, O., & Paraschiv, C. (2011). Experienced vs described uncertainty: Do we need two prospect theory specifications? Management Science, 57(10), 18791895.Google Scholar
Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’école américaine. Econometrica: Journal of the Econometric Society, 21(4), 503546. www.jstor.org/stable/1907921CrossRefGoogle Scholar
Barron, G., & Erev, I. (2003). Small feedback-based decisions and their limited correspondence to description-based decisions. Journal of Behavioral Decision Making, 16(3), 215233.Google Scholar
Barron, G., & Yechiam, E. (2009). The coexistence of overestimation and underweighting of rare events and the contingent recency effect. Judgment and Decision Making, 4(6), 447460. www.sjdm.org/~baron/journal/9729b/jdm9729b.pdfGoogle Scholar
Cohen, D., Plonsky, O., & Erev, I. (2020). On the impact of experience on probability weighting in decisions under risk. Decision, 7(2), 153162.Google Scholar
Denrell, J., & March, J. G. (2001). Adaptation as information restriction: The hot stove effect. Organization Science, 12(5), 523538.CrossRefGoogle Scholar
Erev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017). From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience. Psychological Review, 124(4), 369409. https://doi.org/10.1037/rev0000062Google Scholar
Erev, I., Ert, E., & Roth, A. E. (2010a). A choice prediction competition for market entry games: An introduction. Games, 1(2), 117136. https://doi.org/10.3390/g1020117CrossRefGoogle Scholar
Erev, I., Ert, E., Roth, A. E., Haruvy, E., Herzog, S. M., Hau, R., Hertwig, R., Stewart, T., West, R., & Lebiere, C. (2010b). A choice prediction competition: Choices from experience and from description. Journal of Behavioral Decision Making, 23(1), 1547. https://doi.org/10.1002/bdm.683Google Scholar
Erev, I., Glozman, I., & Hertwig, R. (2008a). What impacts the impact of rare events. Journal of Risk and Uncertainty, 36(2), 153177. https://doi.org/10.1007/s11166–008-9035-zGoogle Scholar
Erev, I., & Roth, A. E. (2014). Maximization, learning, and economic behavior. Proceedings of the National Academy of Sciences, 111(Supplement 3), 1081810825.CrossRefGoogle ScholarPubMed
Erev, I., Shimonovich, D., Schurr, A., & Hertwig, R. (2008b). Base rates: How to make the intuitive mind appreciate or neglect them. In Intuition in judgment and decision making (pp. 135148). Hillsdale, NJ: Erlbaum.Google Scholar
Erev, I., Wallsten, T. S., & Budescu, D. V. (1994). Simultaneous over- and :underconfidence: The role of error in judgment processes. Psychological Review, 101(3), 519527.Google Scholar
Erev, I. Yakobi, O., Ashby, N. J. S., & Chater, N. (2022). The impact of experience on decisions based on pre-choice samples, and the face-or-cue hypothesis. Theory and Decisions, 92(3), 583–598.Google Scholar
Fiedler, K., Brinkmann, B., Betsch, T., & Wild, B. (2000). A sampling approach to biases in conditional probability judgments: Beyond base rate neglect and statistical format. Journal of Experimental Psychology: General, 129(3), 399.CrossRefGoogle ScholarPubMed
Fischhoff, B., Slovic, P., & Lichtenstein, S. (1978). Fault trees: Sensitivity of estimated failure probabilities to problem representation. Journal of Experimental Psychology: Human Perception and Performance, 4(2), 330344.Google Scholar
Fox, C. R., & Tversky, A. (1998). A belief-based account of decision under uncertainty. Management Science, 44(7), 879895.Google Scholar
Gonzalez, C., Lerch, J. F., & Lebiere, C. (2003). Instance-based learning in dynamic decision making. Cognitive Science, 27(4), 591635. https://doi.org/10.1016/S0364-0213(03)00031-4Google Scholar
Hau, R., Pleskac, T. J., Kiefer, J., & Hertwig, R. (2008). The description–experience gap in risky choice: The role of sample size and experienced probabilities. Journal of Behavioral Decision Making, 21(5), 493518.CrossRefGoogle Scholar
Hertwig, R., Barron, G., Weber, E., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15(8), 534539. https://doi.org/10.1111/j.0956-7976.2004.00715.xCrossRefGoogle ScholarPubMed
Hertwig, R., & Erev, I. (2009). The description–experience gap in risky choice. Trends in Cognitive Sciences, 13(12), 517523. https://doi.org/10.1016/j.tics.2009.09.004Google Scholar
Hertwig, R., & Pleskac, T. J. (2010). Decisions from experience: Why small samples? Cognition, 115(2), 225237. https://doi.org/10.1016/j.cognition.2009.12.009CrossRefGoogle ScholarPubMed
Jessup, R. K., Bishara, A. J., & Busemeyer, J. R. (2008). Feedback produces divergence from prospect theory in descriptive choice. Psychological Science, 19(10), 10151022. https://doi.org/10.1111/j.1467-9280.2008.02193.xGoogle Scholar
Juslin, P., & Olsson, H. (1997). Thurstonian and Brunswikian origins of uncertainty in judgment: A sampling model of confidence in sensory discrimination. Psychological Review, 104(2), 344366.Google Scholar
Juslin, P., Winman, A., & Hansson, P. (2007). The naïve intuitive statistician: A naïve sampling model of intuitive confidence intervals. Psychological Review, 114(3), 678703.CrossRefGoogle Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263292.Google Scholar
Kareev, Y. (2000). Seven (indeed, plus or minus two) and the detection of correlations. Psychological Review, 107(2), 397402. https://doi.org/10.1037/TO33-295X.107.2.397CrossRefGoogle ScholarPubMed
Lejarraga, T., & Gonzalez, C. (2011). Effects of feedback and complexity on repeated decisions from description. Organizational Behavior and Human Decision Processes, 116(2), 286295. https://doi.org/10.1016/j.obhdp.2011.05.001Google Scholar
Marchiori, D., Di Guida, S., & Erev, I. (2015). Noisy retrieval models of over-and undersensitivity to rare events. Decision, 2(2), 82106.Google Scholar
Phillips, L. D., & Edwards, W. (1966). Conservatism in a simple probability inference task. Journal of Experimental Psychology, 72(3), 346354.Google Scholar
Plonsky, O., Apel, R., Ert, E., et al. (2019). Predicting human decisions with behavioral theories and machine learning. ArXiv Preprint ArXiv:1904.06866.Google Scholar
Plonsky, O., & Teodorescu, K. (2020). The influence of biased exposure to foregone outcomes. Journal of Behavioral Decision Making, 33(3), 393407.CrossRefGoogle Scholar
Plonsky, O., Teodorescu, K., & Erev, I. (2015). Reliance on small samples, the wavy recency effect, and similarity-based learning. Psychological Review, 122(4), 621647.Google Scholar
Rapoport, A., Wallsten, T. S., Erev, I., & Cohen, B. L. (1990). Revision of opinion with verbally and numerically expressed uncertainties. Acta Psychologica, 74(1), 6179.CrossRefGoogle Scholar
Savage, L. J. (1954). The foundations of statistics. New York: John Wiley.Google Scholar
Schurr, A. (2006). Peak or freq? The effect of unpleasant extreme experiences. Haifa, Israel: Technion-Israel Institute of Technology.Google Scholar
Skinner, B. (1953). Science and human behavior. Free Press.Google Scholar
Teodorescu, K., Amir, M., & Erev, I. (2013). The experience–description gap and the role of the inter decision interval. In Srinivasan, N. & Pamni, V. S. C. (Eds.), Progress in brain research (1st ed., Vol. 202, pp. 99115). Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-444-62604-2.00006-XGoogle Scholar
Teodorescu, K., & Erev, I. (2014). Learned helplessness and learned prevalence: Exploring the causal relations among perceived controllability, reward prevalence, and exploration. Psychological Science, 25(10), 18611869.Google Scholar
Wulff, D. U., Mergenthaler-Canseco, M., & Hertwig, R. (2018). A meta-analytic review of two modes of learning and the description–experience gap. Psychological Bulletin, 144(2), 140176.Google Scholar
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(3), 430439. https://doi.org/10.1177/0022002704270847Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×