Abstract
To better introduce the behavioral economics approach and reinforce the theoretical basis for supporting bias-aware user modeling and evaluation, we need to have a deeper understanding of the concepts, theories, recent progress, and empirical findings on users and their biased decisions in varying scenarios. To achieve this, this chapter takes a step back from specific computational IR models and focuses on explaining the fundamental frameworks (e.g., theories of two systems), research progress, and practical implications of behavioral economics research on boundedly rational decision-making activities. Our review focuses on the major human biases and heuristics that are both widely examined in behavioral economics studies and also clearly contradict one or more assumptions that are explicitly or implicitly made in formal IR models. Although the theories on bounded rationality may not be able to match the precision and quantifiability of formal computational models, as argued by Kahneman, this statement of limitation from the classic economics side is “just another way of saying that rational models are psychologically unrealistic” [Kahneman (American Economic Review 93(5):1449, 2003)]. This argument also serves as part of the motivations for this book and the author’s broad research agenda on IR research.
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Notes
- 1.
After preparing the raw list of all individual biases, Benson (2016) further processed the list by removing duplicates, grouping biases that are similar in nature, and putting together complementary biases (e.g., optimism bias and pessimism bias). After this preprocessing, Benson (2016) obtains a more condensed list with around 20 unique human biases associated with specific mental strategies that decision makers used under different scenarios.
- 2.
The correct answer is 5 cents.
References
Abd Mutalib, N. S., Soh, Y. C., Wong, T. W., Yee, S. M., Yang, Q., Murugiah, M. K., & Ming, L. C. (2017). Online narratives about medical tourism in Malaysia and Thailand: A qualitative content analysis. Journal of Travel & Tourism Marketing, 34(6), 821–832. https://doi.org/10.1080/10548408.2016.1250697
Akerlof, G. A., & Dickens, W. T. (1982). The economic consequences of cognitive dissonance. The American Economic Review, 72(3), 307–319. http://www.jstor.org/stable/1831534
Alaybek, B., Dalal, R. S., Fyffe, S., Aitken, J. A., Zhou, Y., Qu, X., Roman, A., & Baines, J. I. (2022). All’s well that ends (and peaks) well? A meta-analysis of the peak-end rule and duration neglect. Organizational Behavior and Human Decision Processes, 170, 104149. https://doi.org/10.1016/j.obhdp.2022.104149
Alesina, A., & Passarelli, F. (2019). Loss aversion in politics. American Journal of Political Science, 63(4), 936–947. https://doi.org/10.1111/ajps.12440
Apesteguia, J., & Ballester, M. A. (2009). A theory of reference-dependent behavior. Economic Theory, 40(3), 427–455. https://doi.org/10.1007/s00199-008-0387-z
Arguello, J. (2014). Predicting search task difficulty. In European Conference on Information Retrieval (pp. 88–99). Springer.
Azzopardi, L. (2014). Modelling interaction with economic models of search. In Proceedings of the 37th ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 3–12). ACM. https://doi.org/10.1145/2600428.2609574
Azzopardi, L. (2021). Cognitive biases in search: A review and reflection of cognitive biases in information retrieval. In Proceedings of the 2021 ACM SIGIR Conference on Human Information Interaction and Retrieval (pp. 27–37). ACM. https://doi.org/10.1145/3406522.3446023
Barac-Cikoja, D., & Turvey, M. T. (1995). Does perceived size depend on perceived distance? An argument from extended haptic perception. Perception & Psychophysics, 57(2), 216–224. https://doi.org/10.3758/BF03206508
Bateman, I., Munro, A., Rhodes, B., Starmer, C., & Sugden, R. (1997). A test of the theory of reference-dependent preferences. The Quarterly Journal of Economics, 112(2), 479–505. https://doi.org/10.1162/003355397555262
Battaglio, R. P., Jr., Belardinelli, P., Bellé, N., & Cantarelli, P. (2019). Behavioral public administration ad fontes: A synthesis of research on bounded rationality, cognitive biases, and nudging in public organizations. Public Administration Review, 79(3), 304–320. https://doi.org/10.1111/puar.12994
Belkin, N. J., Dumais, S., Kando, N., & Sanderson, M. (2012). Whole-session evaluation of interactive information retrieval systems. In NII Shonan Meeting Report (Vol. 7).
Bem, D. J. (1967). Self-perception: An alternative interpretation of cognitive dissonance phenomena. Psychological Review, 74(3), 183–200. https://doi.org/10.1037/h0024835
Benson, B. (2016). Cognitive bias cheat sheet. https://betterhumans.pub/cognitive-bias-cheat-sheet-55a472476b18.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Bindra, S., Sharma, D., Parameswar, N., Dhir, S., & Paul, J. (2022). Bandwagon effect revisited: A systematic review to develop future research agenda. Journal of Business Research, 143, 305–317. https://doi.org/10.1016/j.jbusres.2022.01.085
Bonnichsen, O. L. E., & Ladenburg, J. (2015). Reducing status quo bias in choice experiments. Nordic Journal of Health Economics, 3(1), 47–67. https://doi.org/10.5617/njhe.645
Canessa, N., Crespi, C., Motterlini, M., Baud-Bovy, G., Chierchia, G., Pantaleo, G., Tettamanti, M., & Cappa, S. F. (2013). The functional and structural neural basis of individual differences in loss aversion. Journal of Neuroscience, 33(36), 14307–14317. https://doi.org/10.1523/JNEUROSCI.0497-13.2013
Capra, R., Arguello, J., O’Brien, H., Li, Y., & Choi, B. (2018). The effects of manipulating task determinability on search behaviors and outcomes. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 445–454). ACM. https://doi.org/10.1145/3209978.3210047
Caputo, A. (2014). Relevant information, personality traits and anchoring effect. International Journal of Management and Decision Making, 13(1), 62–76. http://eprints.lincoln.ac.uk/id/eprint/18553/
Chapman, G. B., & Johnson, E. J. (1994). The limits of anchoring. Journal of Behavioral Decision Making, 7(4), 223–242. https://doi.org/10.1002/bdm.3960070402
Choi, B., Ward, A., Li, Y., Arguello, J., & Capra, R. (2019). The effects of task complexity on the use of different types of information in a search assistance tool. ACM Transactions on Information Systems (TOIS), 38(1), 1–28. https://doi.org/10.1145/3371707
Cooper, J. (2019). Cognitive dissonance: Where we’ve been and where we’re going. International Review of Social Psychology, 32(1), 7. https://doi.org/10.5334/irsp.277
Croskerry, P. (2003). The importance of cognitive errors in diagnosis and strategies to minimize them. Academic Medicine, 78(8), 775–780. https://doi.org/10.1097/00001888-200308000-00003
Czajkowski, M., Zagórska, K., & Hanley, N. (2019). Social norm nudging and preferences for household recycling. Resource and Energy Economics, 58, 101110. https://doi.org/10.1016/j.reseneeco.2019.07.004
Dennis, A. R., Yuan, L., Feng, X., Webb, E., & Hsieh, C. J. (2020). Digital nudging: Numeric and semantic priming in e-commerce. Journal of Management Information Systems, 37(1), 39–65. https://doi.org/10.1080/07421222.2019.1705505
Draws, T., Rieger, A., Inel, O., Gadiraju, U., & Tintarev, N. (2021). A checklist to combat cognitive biases in crowdsourcing. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (Vol. 9, pp. 48–59). https://ojs.aaai.org/index.php/HCOMP/article/view/18939
Eickhoff, C. (2018). Cognitive biases in crowdsourcing. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (pp. 162–170). ACM. https://doi.org/10.1145/3159652.3159654
Erev, I., Ert, E., & Yechiam, E. (2008). Loss aversion, diminishing sensitivity, and the effect of experience on repeated decisions. Journal of Behavioral Decision Making, 21(5), 575–597. https://doi.org/10.1002/bdm.602
Evans, J. S. B. (1989). Bias in human reasoning: Causes and consequences. Erlbaum.
Evans, J. S. B. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 7(10), 454–459. https://doi.org/10.1016/j.tics.2003.08.012
Fadel, K. J., Meservy, T. O., & Kirwan, C. B. (2022). Information filtering in electronic networks of practice: An fMRI investigation of expectation (dis) confirmation. Journal of the Association for Information Systems, 23(2), 491–520. https://doi.org/10.17705/1jais.00731
Fleming, S. M., Thomas, C. L., & Dolan, R. J. (2010). Overcoming status quo bias in the human brain. Proceedings of the national Academy of Sciences, 107(13), 6005–6009. https://doi.org/10.1073/pnas.0910380107
Frederick, S. F., & Fischhoff, B. (1998). Scope (in) sensitivity in elicited valuations. Risk Decision and Policy, 3(2), 109–123.
Fredrickson, B. L., & Kahneman, D. (1993). Duration neglect in retrospective evaluations of affective episodes. Journal of Personality and social Psychology, 65(1), 45–55. https://doi.org/10.1037/0022-3514.65.1.45
Gächter, S., Johnson, E. J., & Herrmann, A. (2022). Individual-level loss aversion in riskless and risky choices. Theory and Decision, 92(3), 599–624. https://doi.org/10.1007/s11238-021-09839-8
Gilder, T. S., & Heerey, E. A. (2018). The role of experimenter belief in social priming. Psychological Science, 29(3), 403–417. https://doi.org/10.1177/0956797617737128
Gonçalves, D., Coelho, P., Martinez, L. F., & Monteiro, P. (2021). Nudging consumers toward healthier food choices: A field study on the effect of social norms. Sustainability, 13(4), 1660. https://doi.org/10.3390/su13041660
Goswami, I., & Urminsky, O. (2016). When should the ask be a nudge? The effect of default amounts on charitable donations. Journal of Marketing Research, 53(5), 829–846. https://doi.org/10.1509/jmr.15.0001
Gonzalez-Prieto, D., Sallan, J. M., Simo, P., & Carrion, R. (2013). Effects of the addition of simple and double decoys on the purchasing process of airline tickets. Journal of Air Transport Management, 29, 39–45. https://doi.org/10.1016/j.jairtraman.2013.02.002
Hands, D. S., & Avons, S. E. (2001). Recency and duration neglect in subjective assessment of television picture quality. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition, 15(6), 639–657. https://doi.org/10.1002/acp.731
Harris, L. R., & Mander, C. (2014). Perceived distance depends on the orientation of both the body and the visual environment. Journal of Vision, 14(12), 17–17. https://doi.org/10.1167/14.12.17
Harrison, G. W. (1994). Expected utility theory and the experimentalists. In Experimental economics (pp. 43–73). Physica.
He, Y., & Cunha, M., Jr. (2020). Love leads to action: Short-term mating mindset mitigates the status-quo bias by enhancing promotion focus. Journal of Consumer Psychology, 30(4), 631–651. https://doi.org/10.1002/jcpy.1174
Highhouse, S. (1996). Context-dependent selection: The effects of decoy and phantom job candidates. Organizational Behavior and Human Decision Processes, 65(1), 68–76. https://doi.org/10.1006/obhd.1996.0006
Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American Economic Review, 92(5), 1644–1655. https://doi.org/10.1257/000282802762024700
Hu, J., & Yu, R. (2014). The neural correlates of the decoy effect in decisions. Frontiers in Behavioral Neuroscience, 8, 271. https://doi.org/10.3389/fnbeh.2014.00271
Hubbeling, D. (2020). Rationing decisions and the endowment effect. Journal of the Royal Society of Medicine, 113(3), 98–100. https://doi.org/10.1177/0141076819893541
Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives? Science, 302(5649), 1338–1339. https://doi.org/10.1126/science.1091721
Josiam, B. M., & Hobson, J. P. (1995). Consumer choice in context: The decoy effect in travel and tourism. Journal of Travel Research, 34(1), 45–50. https://doi.org/10.1177/004728759503400106
Jung, A. K., Stieglitz, S., Kissmer, T., Mirbabaie, M., & Kroll, T. (2022). Click me…! The influence of clickbait on user engagement in social media and the role of digital nudging. PLoS One, 17(6), e0266743. https://doi.org/10.1371/journal.pone.0266743
Kahneman, D., Fredrickson, B. L., Schreiber, C. A., & Redelmeier, D. A. (1993). When more pain is preferred to less: Adding a better end. Psychological Science, 4(6), 401–405. https://doi.org/10.1111/j.1467-9280.1993.tb00589.x
Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93(5), 1449–1475. https://doi.org/10.1257/000282803322655392
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kahneman, D., Knetsch, J. L., & Thaler, R. (1986). Fairness as a constraint on profit seeking: Entitlements in the market. American Economic Review, 728–741. https://www.jstor.org/stable/1806070
Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk. In Handbook of the Fundamentals of Financial Decision Making: Part I (pp. 99–127).
Kaissi, A. A., & Begun, J. W. (2008). Fads, fashions, and bandwagons in health care strategy. Health Care Management Review, 33(2), 94–102. https://doi.org/10.1097/01.HMR.0000304498.97308.40
Kappes, A., Harvey, A. H., Lohrenz, T., Montague, P. R., & Sharot, T. (2020). Confirmation bias in the utilization of others’ opinion strength. Nature Neuroscience, 23(1), 130–137. https://doi.org/10.1038/s41593-019-0549-2
Kastanakis, M. N., & Balabanis, G. (2012). Between the mass and the class: Antecedents of the “bandwagon” luxury consumption behavior. Journal of Business Research, 65(10), 1399–1407. https://doi.org/10.1016/j.jbusres.2011.10.005
Kaufman, B. E. (1999). Emotional arousal as a source of bounded rationality. Journal of Economic Behavior & Organization, 38(2), 135–144. https://doi.org/10.1016/S0167-2681(99)00002-5
Kessous, A., & Valette-Florence, P. (2019). “From Prada to Nada”: Consumers and their luxury products: A contrast between second-hand and first-hand luxury products. Journal of Business Research, 102, 313–327. https://doi.org/10.1016/j.jbusres.2019.02.033
Kim, H. W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567–582. https://doi.org/10.2307/20650309
Knetsch, J. L., & Wong, W. K. (2009). The endowment effect and the reference state: Evidence and manipulations. Journal of Economic Behavior & Organization, 71(2), 407–413. https://doi.org/10.1016/j.jebo.2009.04.015
Knutson, B., Wimmer, G. E., Rick, S., Hollon, N. G., Prelec, D., & Loewenstein, G. (2008). Neural antecedents of the endowment effect. Neuron, 58(5), 814–822. https://doi.org/10.1016/j.neuron.2008.05.018
Kőszegi, B., & Rabin, M. (2006). A model of reference-dependent preferences. The Quarterly Journal of Economics, 121(4), 1133–1165. https://doi.org/10.1093/qje/121.4.1133
Kreuter, M. W., Chheda, S. G., & Bull, F. C. (2000). How does physician advice influence patient behavior? Evidence for a priming effect. Archives of Family Medicine, 9(5), 426–433.
Kristjánsson, Á., & Ásgeirsson, Á. G. (2019). Attentional priming: Recent insights and current controversies. Current Opinion in Psychology, 29, 71–75. https://doi.org/10.1016/j.copsyc.2018.11.013
Langer, T., Sarin, R., & Weber, M. (2005). The retrospective evaluation of payment sequences: Duration neglect and peak-and-end effects. Journal of Economic Behavior & Organization, 58(1), 157–175. https://doi.org/10.1016/j.jebo.2004.01.001
Lankton, N. K., & McKnight, H. D. (2012). Examining two expectation disconfirmation theory models: Assimilation and asymmetry effects. Journal of the Association for Information Systems, 13(2), 88–115. https://doi.org/10.17705/1jais.00285
Lau, A. Y., & Coiera, E. W. (2007). Do people experience cognitive biases while searching for information? Journal of the American Medical Informatics Association, 14(5), 599–608. https://doi.org/10.1197/jamia.M2411
Lehner, P. E., Adelman, L., Cheikes, B. A., & Brown, M. J. (2008). Confirmation bias in complex analyses. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 38(3), 584–592. https://doi.org/10.1109/TSMCA.2008.918634
Liu, C., Liu, J., & Belkin, N. J. (2014). Predicting search task difficulty at different search stages. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (pp. 569–578). ACM. https://doi.org/10.1145/2661829.2661939
Liu, J. (2021). Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors. Information Processing & Management, 58(3), 102522. https://doi.org/10.1016/j.ipm.2021.102522
Liu, J., & Han, F. (2020). Investigating reference dependence effects on user search interaction and satisfaction: A behavioral economics perspective. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1141–1150). ACM. https://doi.org/10.1145/3397271.3401085
Liu, J., & Shah, C. (2019). Investigating the impacts of expectation disconfirmation on web search. In Proceedings of the 2019 ACM SIGIR Conference on Human Information Interaction and Retrieval (pp. 319–323). ACM. https://doi.org/10.1145/3295750.3298959
Ludolph, R., & Schulz, P. J. (2018). Debiasing health-related judgments and decision making: A systematic review. Medical Decision Making, 38(1), 3–13. https://doi.org/10.1177/0272989X17716672
Mainolfi, G. (2020). Exploring materialistic bandwagon behaviour in online fashion consumption: A survey of Chinese luxury consumers. Journal of Business Research, 120, 286–293. https://doi.org/10.1016/j.jbusres.2019.11.038
Mankiw, N. G. (2014). Principles of economics. Cengage Learning.
McGrath, A. (2017). Dealing with dissonance: A review of cognitive dissonance reduction. Social and Personality Psychology Compass, 11(12), e12362. https://doi.org/10.1111/spc3.12362
McKinney, V., Yoon, K., & Zahedi, F. M. (2002). The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13(3), 296–315. https://doi.org/10.1287/isre.13.3.296.76
Molyneux, L., & Coddington, M. (2020). Aggregation, clickbait and their effect on perceptions of journalistic credibility and quality. Journalism Practice, 14(4), 429–446. https://doi.org/10.1080/17512786.2019.1628658
Morewedge, C. K., & Giblin, C. E. (2015). Explanations of the endowment effect: An integrative review. Trends in Cognitive Sciences, 19(6), 339–348. https://doi.org/10.1016/j.tics.2015.04.004
Neys, W. D. (2006). Dual processing in reasoning: Two systems but one reasoner. Psychological Science, 17(5), 428–433. https://doi.org/10.1111/j.1467-9280.2006.01723.x
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220. https://doi.org/10.1037/1089-2680.2.2.175
O’Brien, H. L., Arguello, J., & Capra, R. (2020). An empirical study of interest, task complexity, and search behaviour on user engagement. Information Processing & Management, 57(3), 102226. https://doi.org/10.1016/j.ipm.2020.102226
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
O’Sullivan, E. D., & Schofield, S. J. (2018). Cognitive bias in clinical medicine. Journal of the Royal College of Physicians of Edinburgh, 48(3), 225–232. https://doi.org/10.4997/jrcpe.2018.306
Pirolli, P., & Card, S. (1999). Information foraging. Psychological Review, 106(4), 643–675. https://doi.org/10.1037/0033-295X.106.4.643
Redelmeier, D. A., & Kahneman, D. (1996). Patients’ memories of painful medical treatments: Real-time and retrospective evaluations of two minimally invasive procedures. Pain, 66(1), 3–8. https://doi.org/10.1016/0304-3959(96)02994-6
Redelmeier, D. A., Katz, J., & Kahneman, D. (2003). Memories of colonoscopy: A randomized trial. Pain, 104(1–2), 187–194. https://doi.org/10.1016/S0304-3959(03)00003-4
Saab, F., Elhajj, I. H., Kayssi, A., & Chehab, A. (2019). Modelling cognitive bias in crowdsourcing systems. Cognitive Systems Research, 58, 1–18. https://doi.org/10.1016/j.cogsys.2019.04.004
Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1(1), 7–59. https://doi.org/10.1007/BF00055564
Schneider, S., Stone, A. A., Schwartz, J. E., & Broderick, J. E. (2011). Peak and end effects in patients’ daily recall of pain and fatigue: A within-subjects analysis. The Journal of Pain, 12(2), 228–235. https://doi.org/10.1016/j.jpain.2010.07.001
Scholer, F., Kelly, D., Wu, W. C., Lee, H. S., & Webber, W. (2013). The effect of threshold priming and need for cognition on relevance calibration and assessment. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 623–632). ACM. https://doi.org/10.1145/2484028.2484090
Schreiber, C. A., & Kahneman, D. (2000). Determinants of the remembered utility of aversive sounds. Journal of Experimental Psychology: General, 129(1), 27–42. https://doi.org/10.1037/0096-3445.129.1.27
Schumm, G. F. (1987). Transitivity, preference and indifference. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition, 52(3), 435–437. https://www.jstor.org/stable/4319930
Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83(5), 1178. https://doi.org/10.1037/0022-3514.83.5.1178
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119(1), 3–22. https://doi.org/10.1037/0033-2909.119.1.3
Sprenger, C. (2015). An endowment effect for risk: Experimental tests of stochastic reference points. Journal of Political Economy, 123(6), 1456–1499. https://doi.org/10.1086/683836
Spruyt, A., Hermans, D., Houwer, J. D., & Eelen, P. (2002). On the nature of the affective priming effect: Affective priming of naming responses. Social Cognition, 20(3), 227–256.
Stoffel, S. T., Yang, J., Vlaev, I., & von Wagner, C. (2019). Testing the decoy effect to increase interest in colorectal cancer screening. PLoS One, 14(3), e0213668. https://doi.org/10.1371/journal.pone.0213668
Thaler, R. H. (2016). Behavioral economics: Past, present, and future. American Economic Review, 106(7), 1577–1600. https://doi.org/10.1257/aer.106.7.1577
Tipper, S. P. (1985). The negative priming effect: Inhibitory priming by ignored objects. The Quarterly Journal of Experimental Psychology, 37(4), 571–590. https://doi.org/10.1080/14640748508400920
Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515–518. https://doi.org/10.1126/science.1134239
Tuchman, B. W. (1984). The march of folly: From Troy to Vietnam. Ballantine Books.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
Tversky, A., & Kahneman, D. (1985). The framing of decisions and the psychology of choice. In Behavioral Decision Making (pp. 25–41). Springer.
Tversky, A., & Kahneman, D. (1986). The framing of decisions and the evaluation of prospects. Studies in Logic and the Foundations of Mathematics, 114, 503–520.
Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039–1061. https://doi.org/10.2307/2937956
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/BF00122574
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., & Goyal, S. (2010). Expectation disconfirmation and technology adoption: Polynomial modeling and response surface analysis. MIS Quarterly, 34(2), 281–303. https://doi.org/10.2307/20721428
Wang, W., Feng, F., He, X., Zhang, H., & Chua, T. S. (2021). Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1288–1297). ACM. https://doi.org/10.1145/3404835.3462962
Wedell, D. H., & Pettibone, J. C. (1996). Using judgments to understand decoy effects in choice. Organizational Behavior and Human Decision Processes, 67(3), 326–344. https://doi.org/10.1006/obhd.1996.0083
Wesslen, R., Santhanam, S., Karduni, A., Cho, I., Shaikh, S., & Dou, W. (2019). Investigating effects of visual anchors on decision-making about misinformation. In Computer Graphics Forum (Vol. 38, No. 3, pp. 161–171). https://doi.org/10.1111/cgf.13679
Wisniewski, P. J., Knijnenburg, B. P., & Lipford, H. R. (2017). Making privacy personal: Profiling social network users to inform privacy education and nudging. International Journal of Human-Computer Studies, 98, 95–108. https://doi.org/10.1016/j.ijhcs.2016.09.006
Wu, C., & Cosguner, K. (2020). Profiting from the decoy effect: A case study of an online diamond retailer. Marketing Science, 39(5), 974–995. https://doi.org/10.1287/mksc.2020.1231
Wu, L., Liu, P., Chen, X., Hu, W., Fan, X., & Chen, Y. (2020). Decoy effect in food appearance, traceability, and price: Case of consumer preference for pork hindquarters. Journal of Behavioral and Experimental Economics, 87, 101553. https://doi.org/10.1016/j.socec.2020.101553
Yu, R., Mobbs, D., Seymour, B., & Calder, A. J. (2010). Insula and striatum mediate the default bias. Journal of Neuroscience, 30(44), 14702–14707. https://doi.org/10.1523/JNEUROSCI.3772-10.2010
Zhang, Y., Liu, X., & Zhai, C. (2017). Information retrieval evaluation as search simulation: A general formal framework for IR evaluation. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval (pp. 193–200). ACM. https://doi.org/10.1145/3121050.3121070
Zhang, T., & Zhang, D. (2007). Agent-based simulation of consumer purchase decision-making and the decoy effect. Journal of Business Research, 60(8), 912–922. https://doi.org/10.1016/j.jbusres.2007.02.006
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Liu, J. (2023). Bounded Rationality in Decision-Making Under Uncertainty. In: A Behavioral Economics Approach to Interactive Information Retrieval. The Information Retrieval Series, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-031-23229-9_4
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