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Chapter 3 - In Decisions from Experience What You See Is Up to Your Sampling of the World

from Part I - Historical Review of Sampling Perspectives and Major Paradigms

Published online by Cambridge University Press:  01 June 2023

By
Timothy J. Pleskac  and
Ralph Hertwig
Edited by
Klaus Fiedler ,
Peter Juslin  and
Jerker Denrell
Klaus Fiedler
Affiliation:
Universität Heidelberg
Peter Juslin
Affiliation:
Uppsala Universitet, Sweden
Jerker Denrell
Affiliation:
University of Warwick
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Summary

When faced with a choice under incomplete knowledge, people can turn to the practical option of actively collecting information and ultimately deciding from experience. Here we review the dynamic interplay between perceiving and acting that arises during these decisions: What the person sees and experiences depends on how the person acts, and how the person acts depends on what the person has seen and experienced. We also review how this interaction and choice can be crucial to understanding risk-taking and how it can help advance our understanding of human competence. Finally, we contend that a truly successful model of how people make decisions from experience will capture this dynamic interplay.

Keywords

decisions from experiencedescription–experience gap learningsmall samplesrisk-takingsamplingmere presentationsaliency
Type
Chapter
Information
Sampling in Judgment and Decision Making , pp. 66 - 89
Publisher: Cambridge University Press
Print publication year: 2023

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References

dAbdellaoui, M., L’Haridon, O., & Paraschiv, C. (2011). Experienced vs. Described uncertainty: Do we need two prospect theory specifications? Management Science, 57(10), 1879–1895. https://doi.org/10.1287/mnsc.1110.1368Google Scholar
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036–1060. https://doi.org/10.1037/0033-295X.111.4.1036Google Scholar
Armstrong, B., & Spaniol, J. (2017). Experienced probabilities increase understanding of diagnostic test results in younger and older adults. Medical Decision Making, 37(6), 670–679. https://doi.org/10.1177/027 2989X17691954Google Scholar
Banartzi, S., & Thaler, R. H. (1995). Myopic loss aversion and the equity premium puzzle. Quarterly Journal of Economics, 110(1), 73–92.Google Scholar
Barberis, Nicholas C. 2013. Thirty years of prospect theory in economics: A review and assessment. Journal of Economic Perspectives, 27(1): 173–196.CrossRefGoogle 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), 215–233. https://doi.org/10.1002/bdm.443Google Scholar
Berry, D., & Fristedt, B. (1985). Bandit problems. London: Chapman & Hall.CrossRefGoogle Scholar
Burnetas, A. N., & Katehakis, M. N. (1997). On the finite horizon one-armed bandit problem. Stochastic Analysis and Applications, 16, 845–859.Google Scholar
Bush, R. R., & Mosteller, F. (1955). Stochastic models for learning. New York: John Wiley.Google Scholar
Camilleri, A. R., & Newell, B. R. (2011). When and why rare events are underweighted: a direct comparison of the sampling, partial feedback, full feedback and description choice paradigms. Psychonomic Bulletin Review, 18(2), 377–384. https://doi.org/10.3758/s13423-010-0040-2CrossRefGoogle ScholarPubMed
Dai, J., Pachur, T., Pleskac, T. J., & Hertwig, R. (2019). What the future holds and when: A description–experience gap in intertemporal choice. Psychological Science, 30(8), 1218–1233. https://doi.org/10.1177/0956797619858969CrossRefGoogle ScholarPubMed
Denrell, J. (2005). Why most people disapprove of me: Experience sampling in impression formation. Psychological Review, 112(4), 951–978. https://doi.org/10.1037/0033-295X.112.4.951Google Scholar
Denrell, J. (2007). Adaptive learning and risk taking. Psychological Review, 114(1), 177–187. https://doi.org/10.1037/0033-295x.114.1.177Google Scholar
Denrell, J., & March, J. G. (2001). Adaptation as information restriction: The hot stove effect. Organization Science, 12(5), 523–538.CrossRefGoogle Scholar
Dewey, J. (1903). Studies in logical theory (Vol. 11). Chicago: University of Chicago Press.Google Scholar
Dougherty, M. R. P., Gettys, C. F., & Ogden, E. E. (1999). MINERVA-DM: A memory processes model for judgments of likelihood. Psychological Review, 106(1), 180–209. https://doi.org/10.1037/0033-295X.106.1.180Google Scholar
Dutt, V., Arlό-Costa, H., Helzner, J., & Gonzalez, C. (2014). The description–experience gap in risky and ambiguous gambles. Journal of Behavioral Decision Making, 27, 316–327. https://doi.org/10.1002/bdm.1808Google Scholar
Erev, I., & Barron, G. (2005). On adaptation, maximization, and reinforcement learning among cognitive strategies. Psychological Review, 112(4), 912–931. https://doi.org/10.1037/0033-295X.112.4.912CrossRefGoogle ScholarPubMed
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), 369–409. https://doi.org/10.1037/rev0000062Google Scholar
Erev, I., Glozman, I., & Hertwig, R. (2008). What impacts the impact of rare events. Journal of Risk and Uncertainty, 36, 153–177. https://doi.org/10.1007/s11166–008-9035-zCrossRefGoogle Scholar
Fraenkel, L., Peters, E., Tyra, S., & Oelberg, D. (2016). Shared medical decision making in lung cancer screening: Experienced versus descriptive risk formats. Medical Decision Making, 36(4), 518–525. https://doi.org/10.1177/0272989X15611083CrossRefGoogle ScholarPubMed
Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., & Krüger, L. (1989). The empire of chance: How probability changed science and everyday life. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Glöckner, A., Hilbig, B. E., Henninger, F., & Fiedler, S. (2016). The reversed description–experience gap: Disentangling sources of presentation format effects in risky choice. Journal of Experimental Psychology: General, 145(4), 486–508. https://doi.org/10.1037/a0040103Google Scholar
Gonzalez, C., & Dutt, V. (2011). Instance-based learning: Integrating sampling and repeated decisions from experience. Psychological Review, 118(4), 523.Google Scholar
Gonzalez, C., Lerch, J. F., & Lebiere, C. (2003). Instance-based learning in dynamic decision making. Cognitive Science, 27(4), 591–635.Google Scholar
Gould, S. J. (1992). Bully for Brontosaurus: Further reflections in natural history. Penguin.Google Scholar
Güney, S., & Newell, B. R. (2015). Overcoming ambiguity aversion through experience. Journal of Behavioral Decision Making, 28(2), 188–199. https://doi.org/10.1002/bdm.1840CrossRefGoogle Scholar
Hau, R., Pleskac, T. J., & Hertwig, R. (2010). Decisions from experience and statistical probabilities: Why they trigger different choices than a priori probabilities. Journal of Behavioral Decision Making, 23(1), 48–68. https://doi.org/10.1002/bdm.665CrossRefGoogle 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), 493–518. https://doi.org/10.1002/bdm.598Google Scholar
Hertwig, R. (2015). Decisions from experience. In Keren, G. & Wu, G. (Eds.), Blackwell’s handbook of judgment & decision making (Vol. 1, pp. 240–267). Hoboken, NJ :Wiley Blackwell.Google Scholar
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(8), 534–539. https://doi.org/10.1111/j.0956-7976.2004.00715.xGoogle Scholar
Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2006). Decisions from experience: Sampling and updating of information. Cambridge, UK: Cambridge University Press.Google Scholar
Hertwig, R., & Erev, I. (2009). The description–experience gap in risky choice. Trends in Cognitive Sciences, 13, 517–523. 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, 225–237.Google Scholar
Hertwig, R., & Pleskac, T. J. (2018). The construct–behavior gap and the description–experience gap: Comment on Regenwetter and Robinson (2017). Psychological Review, 125(5), 844–849. https://doi.org/10.1037/rev0000121Google Scholar
Hertwig, R., Pleskac, T. J., Pachur, T., & Center for Adaptive Rationality (2019). Taming uncertainty. Cambridge, MA: MIT. https://doi.org/10.7551/mitpress/11114.001.000Google Scholar
Hertwig, R., & Wullf, D. (2022). A description–experience framework of the dynamic response to risk. Perspectives on Psychological Science, 17(3), 631–651 https://doi.org/10.1177/17456916211026896CrossRefGoogle Scholar
Hintze, A., Phillips, N., & Hertwig, R. (2015). The Janus face of Darwinian competition. Scientific Reports, 5, 13662. https://doi.org/10.1038/srep13662CrossRefGoogle ScholarPubMed
Hotaling, J. M., Jarvstad, A., Donkin, C., & Newell, B. R. (2019). How to change the weight of rare events in decisions from experience. Psychological Science, 30(12), 1767–1779. https://doi.org/10.1177/0956797619884324Google Scholar
Huber, J., Payne, J. W., & Puto, C. (1982). Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis. Journal of Consumer Research, 9(1), 90–98.CrossRefGoogle Scholar
Hurley, S. L. (1998). Consciousness in action. Cambridge, MA: Harvard University Press.Google Scholar
Hurley, S. (2008). The shared circuits model (SCM): How control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences, 31(1), 1–22. https://doi.org/10.1017/S0140525X07003123CrossRefGoogle ScholarPubMed
Jessup, R. K., Bishara, A. J., & Busemeyer, J. R. (2008). Feedback produces divergence from prospect theory in descriptive choice. Psychological Science, 19(10), 1015–1022. https://doi.org/10.1111/j.1467-9280.2008.02193.xGoogle Scholar
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence, 4, 237–285. https://doi.org/10.1613/jair.301Google Scholar
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus & Giroux.Google Scholar
Kahneman, D., & Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39(4), 341–350.CrossRefGoogle Scholar
Kellen, D., Pachur, T., & Hertwig, R. (2016). How (in) variant are subjective representations of described and experienced risk and rewards? Cognition, 157, 126–138. https://doi.org/10.1016/j.cognition.2016.08.020CrossRefGoogle ScholarPubMed
Lejarraga, T., & Gonzales, C. (2011). Effects of feedback and complexity on repeated decisions from description. Organizational Behavior & Human Decision Processes, 116, 286–295. https://doi.org/10.1016/j .obhdp.2011.05.001Google Scholar
Lejarraga, T., & Hertwig, R. (2021). How experimental methods shaped views on human competence and rationality. Psychological Bulletin, 147(6), 535–564. https://doi.org/10.1037/bul0000324Google Scholar
Lejarraga, T., Pachur, T., Frey, R., & Hertwig, R. (2016). Decisions from experience: From monetary to medical gambles. Journal of Behavioral Decision Making, 29(1), 67–77. https://doi.org/10.1002/bdm.1877Google Scholar
Lejarraga, T., Woike, J. K., & Hertwig, R. (2016). Description and experience: How experimental investors learn about booms and busts affects their financial risk taking. Cognition, 157, 365–383. https://doi.org/10.1016/j.cognition.2016.10.001Google Scholar
Lejarraga, T., Woike, J. K., & Hertwig, R. (2019). Experiences and descriptions of financial uncertainty: Are they equivalent? In Hertwig, Ralph, Pleskac, Timothy J. & Pachur, Thorsten (Eds.), Taming uncertainty, 191–205. Cambridge, MA: MIT. https://doi.org/10.7551/mitpress/ 11114.003.0015Google Scholar
Locke, J. (1959/1690). An essay concerning human understanding. New York: Dover.Google Scholar
Lotto, R. B., & Purves, D. (2000). An empirical explanation of color contrast. Proceedings of the National Academy of Sciences USA, 97(23), 12834–12839. https://doi.org/10.1073/pnas.210369597Google Scholar
Ludvig, E. A., Madan, C. R., & Spetch, M. L. (2014). Extreme outcomes sway risky decisions from experience. Journal of Behavioral Decision Making, 27(2), 146–156. https://doi.org/10.1002/bdm.1792CrossRefGoogle Scholar
Ludvig, E. A., & Spetch, M. L. (2011). Of black swans and tossed coins: Is the description–experience gap in risky choice limited to rare events? PLoS ONE, 6(6), e20262. https://doi.org/10.1371/journal.pone.0020262Google Scholar
Malmendier, U., & Nagel, S. (2011). Depression babies: Do macroeconomic experiences affect risk-taking? Quarterly Journal of Economics, 126, 373–416. https://doi.org/10.1093/qje/qjq004Google Scholar
Markant, D., Pleskac, T. J., Diederich, A., Pachur, T., & Hertwig, R. (2015). Modeling choice and search in decisions from experience: A sequential sampling approach. In Dale, R., Jennings, C., Maglio, P., Matlock, T., Noell, D., Warlaumont, A. et al. (Eds.), Proceedings of the 37th annual conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.Google Scholar
March, J. G. (1996). Learning to be risk averse. Psychological Review, 103(2), 309–319. doi:10.1037/0033-295X.103.2.309Google Scholar
Martin, J. M., Gonzalez, C., Juvina, I., & Lebiere, C. (2014). A description–experience gap in social interactions: Information about interdependence and its effects on cooperation. Journal of Behavioral Decision Making, 27, 349–362. https://doi.org/10.1002/bdm.1810Google Scholar
Mata, R., Josef, A. K., Samanez-Larkin, G. R., & Hertwig, R. (2011). Age differences in risky choice: A meta-analysis. Annals of the New York Academy of Sciences, 1235, 18–29. https://doi.org/10.1111/j.1749-6632.2011.06200.xGoogle Scholar
Müller-Lyer, F. C. (1889). Optische Urteilstäuschungen. Archiv für Physiologie, Supp, 263–370.Google Scholar
Nelson, J. D., McKenzie, C. R. M., Cottrell, G. W., & Sejnowski, T. J. (2010). Experience matters: Information acquisition optimizes probability gain. Psychological Science, 21(7), 960–969. https://doi.org/10.1177/0956797610372637Google Scholar
Pachur, T., Schulte-Mecklenbeck, M., Murphy, R. O., & Hertwig, R. (2018). Prospect theory reflects selective allocation of attention. Journal of Experimental Psychology: General, 147(2), 147.Google Scholar
Peterson, C. R., & Beach, L. R. (1967). Man as an intuitive statistician. Psychological Bulletin, 68(1), 29–46. https://doi.org/10.1037/h0024722Google Scholar
Phillips, N. D., Hertwig, R., Kareev, Y., & Avrahami, J. (2014). Rivals in the dark: how competition influences search in decisions under uncertainty. Cognition, 133(1), 104–119. https://doi.org/10.1016/j.cognition.2014.06.006Google Scholar
Pleskac, T. J. (2008). Decision making and learning while taking sequential risks. Journal of Experimental Psychology: Learning Memory and Cognition, 34(1), 167–185. https://doi.org/10.1037/0278-7393.34.1.167Google Scholar
Pleskac, T. J. (2015). Learning models in decision making. In Keren, G. & Wu, G. (Eds.), Blackwell’s handbook of judgment & decision making, 629–657. Chichester, UK: Wiley Blackwell.Google Scholar
Pleskac, T. J., Diederich, A., & Wallsten, T. (2015). Models of decision making under risk and uncertainty. In Busemeyer, J., Wang, J., Townsend, J., & Eidels, A. (Eds.), The Oxford handbook of computational and mathematical psychology (pp. 209–231). Oxford: Oxford University Press.Google Scholar
Pleskac, T. J., Yu, S., Hopwood, C., & Liu, T. (2019). Mechanisms of deliberation during preferential choice: Perspectives from computational modeling and individual differences. Decision, 6(1), 77–107. https://doi.org/10.1037/dec0000092Google Scholar
Plonsky, O., Teodorescu, K., & Erev, I. (2015). Reliance on small samples, the wavy recency effect, and similarity-based learning. Psychological Review, 122(4), 621–647. https://doi.org/10.1037/a0039413Google Scholar
Regenwetter, M., & Robinson, M. M. (2017). The construct–behavior gap in behavioral decision research: A challenge beyond replicability. Psychological Review, 124(5), 533–550. https://doi.org/10.1037/rev0000067Google Scholar
Rehder, B., & Waldmann, M. R. (2017). Failures of explaining away and screening off in described versus experienced causal learning scenarios. Memory & Cognition, 45(2), 245–260. https://doi.org/10.3758/s13421– 016-0662-3CrossRefGoogle ScholarPubMed
Schulze, C., & Hertwig, R. (2021). A description–experience gap in statistical intuitions: Of smart babies, risk-savvy chimps, intuitive statisticians, and stupid grown-ups. Cognition, 210, https://doi.org/10.1016/j.cognition.2020.104580Google Scholar
Simonson, I., & Tversky, A. (1992). Choice in context: Tradeoff contrast and extremeness aversion. Journal of Marketing Research, 29(3), 281–295.Google Scholar
Skinner, B. F. (1953). Science and human behavior. New York, NY: Macmillan.Google Scholar
Slovic, P., Fischhoff, B., & Lichtenstein, S. (1976). Cognitive processes and societal risk taking. In Carol, J. S. & Payne, J. W. (Eds.), Cognition and social behavior, 7–36. Potomac, MA: Erlbaum.Google Scholar
Spektor, M., Gluth, S., Fontanesi, L., & Rieskamp, J. (2019). How similarity between choice options affects decisions from experience: The accentuation-of-differences model. Psychological Review, 126, 52–88. https://doi.org/10.1037/rev0000122Google Scholar
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT.Google Scholar
Thorndike, E. L. (1927). The law of effect. American Journal of Psychology, 39(1/4), 212–222.CrossRefGoogle Scholar
Tylén, K., Fusaroli, R., Rojo, S., Heimann, K., Fay, N., Johannsen, N. N. et al. (2020). The evolution of early symbolic behavior in Homo sapiens. Proceedings of the National Academy of Sciences, 117(9), 4578–4584.CrossRefGoogle ScholarPubMed
Trueblood, J. S. (2012). Multialternative context effects obtained using an inference task. Psychonomic Bulletin and Review, 19(5), 962–968. https://doi.org/10.3758/s13423-012-0288-9CrossRefGoogle ScholarPubMed
Trueblood, J. S., Brown, S. D., Heathcote, A., & Busemeyer, J. R. (2013). Not just for consumers: Context effects are fundamental to decision making. Psychological Science, 24(6), 901–908. https://doi.org/10.1177/0956797612464241Google Scholar
Tsetsos, K., Chater, N., & Usher, M. (2012). Salience driven value integration explains decision biases and preference reversal. Proceedings of the National Academy of Sciences, 109(24), 9659–9664.Google Scholar
Tversky, A., & Fox, C. R. (1995). Weighing risk and uncertainty. Psychological Review, 102(2), 269–283. https://doi.org/10.1037//0033-295X.102.2.269Google Scholar
Tversky, A., & Kahneman, D. (1971). Belief in law of small numbers. Psychological Bulletin, 76(2), 105–110. https://doi.org/10.1037/h0031322Google Scholar
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124Google Scholar
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323. https://doi.org/10.1007/BF00122574Google Scholar
Tversky, A., & Simonson, I. (1993). Context-dependent preferences. Management Science, 39(10), 1179–1189.Google Scholar
Ungemach, C., Chater, N., & Stewart, N. (2009). Are probabilities overweighted or underweighted when rare outcomes are experienced (rarely)? Psychological Science, 20(4), 473–479. https://doi.org/10.1111/j.1467-9280.2009.02319.xGoogle Scholar
Van den Bos, W., & Hertwig, R. (2017). Adolescents display distinctive tolerance to ambiguity and to uncertainty during risky decision making. Scientific Reports, 7, 40962. https://doi.org/10.1038/srep40962Google Scholar
Van den Bos, W., Laube, C., & Hertwig, R. (2019). How the adaptive adolescent mind navigates uncertainty. In Hertwig, Ralph, Pleskac, Timothy J. & Pachur, Thorsten (Eds.), Taming uncertainty, 305–324. Cambridge, MA: MIT. https://doi.org/10.7551/mitpress/11114.003.0023Google Scholar
Vul, E., Goodman, N., Griffiths, T. L., & Tenenbaum, J. B. (2014). One and done? Optimal decisions from very few samples. Cognitive Science, 38(4), 599–637. https://doi.org/10.1111/cogs.12101Google Scholar
Wakker, P. (2010). Prospect theory for risk and ambiguity. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Weber, E., & Kirsner, B. (1997). Reasons for rank-dependent utility evaluation. Journal of Risk and Uncertainty, 14(1), 41–61.Google Scholar
Weber, E. U., Shafir, S., & Blais, A. R. (2004). Predicting risk sensitivity in humans and lower animals: Risk as variance or coefficient of variation. Psychological Review, 111(2), 430–445.Google Scholar
Wegier, P., & Shaffer, V. A. (2017). Aiding risk information learning through simulated experience (ARISE): Using simulated outcomes to improve understanding of conditional probabilities in prenatal Down syndrome screening. Patient Education and Counseling, 100(10), 1882–1889. https://doi.org/10.1016/j.pec.2017.04.016Google Scholar
Wulff, D. U., Hills, T. T., & Hertwig, R. (2015). Online product reviews and the description–experience gap. Journal of Behavioral Decision Making, 28(3), 214–223. https://doi.org/10.1002/bdm.1841Google Scholar
Wulff, D., Markant, D., Pleskac, T. J., & Hertwig, R. (2019). Adaptive exploration: What you see is up to you. In Hertwig, Ralph, Pleskac, Timothy J. & Pachur, Thorsten (Eds.), Taming uncertainty, 131–152. Cambridge, MA: MIT. https://doi.org/10.7551/mitpress/11114.003.0012Google 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), 140–176. https://doi.org/10.1037/bul0000115Google Scholar
Yechiam, E., & Busemeyer, J. (2006). The effect of foregone payoffs on underweighting small probability events. Journal of Behavioral Decision Making, 19, 1–16.CrossRefGoogle Scholar
Zeigenfuse, M. D., Pleskac, T. J., & Liu, T. (2014). Rapid decisions from experience. Cognition, 131(2), 181–194. https://doi.org/10.1016/j.cognition.2013.12.012Google Scholar
Zhang, H., & Houpt, J. W. (2020). Exaggerated prevalence effect with the explicit prevalence information: The description–experience gap in visual search. Attention, Perception & Psychophysics, 82(7), 3340–3356. https://doi.org/10.3758/s13414–020-02045-8Google Scholar

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