Sensitivity of Numerate Individuals to Large Asymmetry in Outcomes : a Registered Replication of Traczyk et al. (2018)

• Autorzy: Supratik Mondal

Warianty tytułu

Wrażliwość na asymetrię w wypłatach wśród osób z wysokim poziomem zdolności numerycznych : prerejestrowana replikacja badania Traczyka i in. (2018)

• Języki publikacji: EN

Abstrakty

EN

The main aim of this study is to replicate the effect shown by Traczyk et al. (2018), where individuals with higher statistical numeracy, compared to individuals with lower statistical numeracy, employed a more effortful choice strategy when outcomes were meaningful. I hypothesize that participants with higher numeracy will be more likely to make choices predicted by Cumulative Prospect Theory and Expected Value theory (CPT/ EV) in high-payoff problems than in low-payoff problems. Data collection was done online by appointing 73 participants. Participants' preference, fl uid intelligence, objective and subjective numeracy were measured using thirteen high and eleven low payoff choice problems, International Cognitive Ability Resource (ICAR), Berlin Numeracy Test (BNT), and Subjective Numeracy Scale (SNS), respectively. All the measures mentioned above were presented randomly. Results showed that all participants, in high-payoff condition, on average maximized EV; however, participants with high BNT scores were more likely to make choices consistent with CPT/EV predictions than individuals with low BNT scores. Furthermore, compared to less numerate participants, highly numerate participants were less likely to make choices consistent with CPT/EV predictions in low-payoff condition. Highly numerate individuals adjusted their choice strategy by modulating their response time, indicating their discernible sensitivity towards large asymmetry in payoff. In conclusion, the effect shown by Traczyk et al. (2018) was successfully replicated. (original abstract)

Głównym celem tego badania była próba zreplikowania efektu wykazanego przez Traczyka i in. (2018), zgodnie z którym osoby z wyższym poziomem statystycznych zdolności numerycznych, w porównaniu do osób z niższym poziomem statystycznych zdolności numerycznych, angażują wymagające poznawczo strategie decyzyjne, gdy potencjalne konsekwencje wyboru są znaczące. Postawiłem hipotezę, że osoby z wysokim poziomem statystycznych zdolności numerycznych będą częściej dokonywały wyborów przewidywanych przez skumulowaną teorię perspektywy i model wartości oczekiwanej (CPT/EV) w problemach decyzyjnych z wysokimi wypłatami (tj. znaczącymi konsekwencjami) niż w problemach decyzyjnych z niskimi wypłatami. W badaniu online 73 ochotników podejmowało decyzje w 13 problemach z wysokimi wypłatami oraz w 11 problemach z niskimi wypłatami. Badani rozwiązywali testy mierzące inteligencję płynną, statystyczne zdolności numeryczne oraz subiektywne zdolności numeryczne. Wszystkie miary były prezentowane w losowej kolejności. Wyniki pokazały, że w warunkach wysokiej wypłaty osoby badane dokonywały wyborów maksymalizujących wartość oczekiwaną. Osoby z wysokimi wynikami w teście mierzącym statystyczne zdolności numeryczne częściej dokonywały jednak wyborów zgodnych z przewidywaniami CPT/EV niż osoby z niskimi wynikami w tym teście. Ponadto osoby z wysokim poziomem statystycznych zdolności numerycznych były mniej skłonne do dokonywania wyborów zgodnych z przewidywaniami CPT/EV w warunku niskiej wypłaty. Osoby te dostosowały swoją strategię wyboru do problemu decyzyjnego poprzez zarządzanie czasem przeznaczanym na podjęcie decyzji, co wskazuje, że mogą one charakteryzować się większą wrażliwością na asymetrię w wypłatach. Podsumowując, efekt opisany w badaniu Traczyka i in. (2018) został pomyślnie zreplikowany. (abstrakt oryginalny)

Słowa kluczowe

EN

Numerical methods; Decision making; Heuristics methods; Prospect theory

PL

Metody numeryczne; Podejmowanie decyzji; Metody heurystyczne; Teoria perspektywy

• Czasopismo: Decyzje
• Rocznik: 2021
• Numer: nr 35
• Strony: 5--25

Twórcy

autor

Supratik Mondal

• SWPS University of Social Sciences and Humanities

Bibliografia

• Arnold, B.F., Hogan, D.R., Colford, J.M., & Hubbard, A.E. (2011). Simulation methods to estimate design power: An overview for applied research. BMC Medical Research Methodology, 11(1), 94. doi: 10.1186/1471-2288-11-94.
• Ashby, N.J.S. (2017). Numeracy predicts preference consistency: Deliberative search heuristics increase choice consistency for choices from description and experience. Judgment and Decision Making, 12(2), 128-139.
• Becker, A., Deckers, T., Dohmen, T., Falk, A., & Kosse, F. (2012). The Relationship Between Economic Preferences and Psychological Personality Measures. Annual Review of Economics, 4(1), 453-478. doi: 10.1146/annurev-economics-080511-110922.
• Bernoulli, D. (1954). Exposition of a New Theory on the Measurement of Risk. Econometrica, 22(1), 23-36. doi: 10.2307/1909829.
• Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006). The priority heuristic: Making choices without trade-offs. Psychological Review, 113(2), 409-432. doi: 10.1037/0033-295X.113.2.409.
• Brysbaert, M., & Stevens, M. (2018). Power Analysis and Effect Size in Mixed Effects Models: A Tutorial. Journal of Cognition, 1(1), 9. doi: 10.5334/joc.10.
• Cacioppo, J.T., & Petty, R.E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116-131. doi: 10.1037/0022-3514.42.1.116.
• Cirillo, P., & Taleb, N.N. (2016). On the statistical properties and tail risk of violent conflicts. Physica A: Statistical Mechanics and its Applications, 452, 29-45. doi: https://doi.org/10.1016/j.physa.2016.01.050.
• Cokely, E.T., Galesic, M., Schulz, E., Ghazal, S., & Garcia-Retamero, R. (2012). Measuring Risk Literacy: The Berlin Numeracy Test. Judgment and Decision Making, 7(1), 23.
• Cokely, E.T., & Kelley, C.M. (2009). Cognitive abilities and superior decision making under risk: A protocol analysis and process model evaluation. Judgment and Decision Making, 4(1), 20-33.
• Condon, D.M., & Revelle, W. (2014). The international cognitive ability resource: Development and initial validation of a public-domain measure. Intelligence, 43, 52-64. doi: 10.1016/j.intell.2014.01.004.
• Davids, S.L., Schapira, M.M., McAuliffe, T.L., & Nattinger, A.B. (2004). Predictors of pessimistic breast cancer risk perceptions in a primary care population. Journal of General Internal Medicine, 19(4), 310-315. doi: 10.1111/j.1525-1497.2004.20801.x.
• Estrada-Mejia, C., de Vries, M., & Zeelenberg, M. (2016). Numeracy and wealth. Journal of Economic Psychology, 54, 53-63. doi: 10.1016/j.joep.2016.02.011.
• Fagerlin, A., Zikmund-Fisher, B.J., Ubel, P.A., Jankovic, A., Derry, H.A., & Smith, D.M. (2007). Measuring Numeracy without a Math Test: Development of the Subjective Numeracy Scale. Medical Decision Making, 27(5), 672-680. doi: 10.1177/0272989X07304449.
• Ghazal, S., Cokely, E.T., & Garcia-Retamero, R. (2014). Predicting biases in very highly educated samples: Numeracy and metacognition. Judgment and Decision Making, 9(1), 15-34.
• Gigerenzer, G. (2007). Gut feelings: The intelligence of the unconscious. Penguin.
• Gigerenzer, G., & Goldstein, D.G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650-669. doi: 10.1037/0033-295X.103.4.650.
• Gurmankin, A.D., Baron, J., & Armstrong, K. (2004). The Effect of Numerical Statements of Risk on Trust and Comfort with Hypothetical Physician Risk Communication. Medical Decision Making, 24(3), 265-271. doi: 10.1177/0272989X04265482.
• Hands, D.W. (2015). Normative Rational Choice Theory: Past, Present, and Future (SSRN Scholarly Paper No. ID 1738671). Rochester, NY. doi: 10.2139/ssrn.1738671.
• Horn, S., & Freund, A.M. (2021). Adult age differences in monetary decisions with real and hypothetical reward. Journal of Behavioral Decision Making. doi: 10.1002/bdm.2253.
• Jasper, J.D., Bhattacharya, C., & Corser, R. (2017). Numeracy Predicts More Effortful and Elaborative Search Strategies in a Complex Risky Choice Context: A Process-Tracing Approach. Journal of Behavioral Decision Making, 30(2), 224-235. doi: 10.1002/bdm.1934.
• Jasper, J.D., Bhattacharya, C., Levin, I.P., Jones, L., & Bossard, E. (2013). Numeracy as a predictor of adaptive risky decision making. Journal of Behavioral Decision Making, 26(2), 164-173. doi: 10.1002/bdm.1748.
• John, & Raven, J. (2003). Raven Progressive Matrices. In R. S. McCallum (Ed.), Handbook of Nonverbal Assessment (pp. 223-237). Boston, MA: Springer US. doi: 10.1007/978-1-4615-0153-4_11.
• Johnson, P.C.D., Barry, S.J.E., Ferguson, H.M., & Müller, P. (2015). Power analysis for generalized linear mixed models in ecology and evolution. Methods in Ecology and Evolution, 6(2), 133-142. doi: 10.1111/2041-210X.12306.
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. doi: 10.2307/1914185.
• Lipkus, I.M., Samsa, G., & Rimer, B.K. (2001). General Performance on a Numeracy Scale among Highly Educated Samples. Medical Decision Making, 21(1), 37-44. doi: 10.1177/0272989X0102100105.
• Lüdecke, D. (2018). Ggeffects: Tidy Data Frames of Marginal Effects from Regression Models. Journal of Open Source Software, 3(26), 772. doi: 10.21105/joss.00772.
• Lusardi, A. (2012). Numeracy, financial literacy, and financial decision-making (Tech. Rep. No. w17821). National Bureau of Economic Research. doi: 10.3386/w17821.
• Małecka, M. (2020). The normative decision theory in economics: A philosophy of science perspective. The case of the expected utility theory. Journal of Economic Methodology, 27(1), 36-50. doi: 10.1080/1350178X.2019.1640891.
• McElreath, R. (2018). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman and Hall/CRC. doi: 10.1201/9781315372495.
• McGrew, K.S. (2021). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1-10. doi: 10.1016/j.intell.2008.08.004.
• Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133-142. doi: 10.1111/j.2041-210x.2012.00261.x.
• Pachur, T., Hertwig, R., Gigerenzer, G., & Brandstätter, E. (2013). Testing process predictions of models of risky choice: A quantitative model comparison approach. Frontiers in Psychology, 4. doi: 10.3389/fpsyg.2013.00646.
• Peters, E., & Bjalkebring, P. (2015). Multiple numeric competencies: When a number is not just a number. Journal of Personality and Social Psychology, 108(5), 802-822. doi: 10.1037/pspp0000019.
• Reyna, V.F., Nelson, W.L., Han, P.K., & Dieckmann, N. F. (2009). How numeracy influences risk comprehension and medical decision making. Psychological Bulletin, 135(6), 943-973. doi: 10.1037/a0017327.
• Rothman, R.L., Housam, R., Weiss, H., Davis, D., Gregory, R., Gebretsadik, T., ... Elasy, T.A. (2006). Patient Understanding of Food Labels: The Role of Literacy and Numeracy. American Journal of Preventive Medicine, 31(5), 391-398. doi: 10.1016/j.amepre.2006.07.025.
• Russell, S., & Norvig, P. (2002). Artificial intelligence: A modern approach. Schönbrodt, F.D., & Wagenmakers, E.-J. (2018). Bayes factor design analysis: Planning for compelling evidence. Psychonomic Bulletin & Review, 25(1), 128-142. doi: 10.3758/s13423-017-1230-y.
• Schwartz, L.M., Woloshin, S., Black, W.C., & Welch, H.G. (1997, December). The Role of Numeracy in Understanding the Benefit of Screening Mammography. Annals of Internal Medicine, 127(11), 966-972. doi: 10.7326/0003-4819-127-11-199712010-00003.
• Sherry, A., & Henson, R.K. (2005). Conducting and Interpreting Canonical Correlation Analysis in Personality Research: A User-Friendly Primer. Journal of Personality Assessment, 84(1), 37-48. doi: 10.1207/s15327752jpa8401_09.
• Sobkow, A., Garrido, D., & Garcia-Retamero, R. (2020). Cognitive Abilities and Financial Decision Making. In T. Zaleskiewicz & J. Traczyk (Eds.), Psychological Perspectives on Financial Decision Making (pp. 71-87). Springer, Cham. doi: 10.1007/978-3-030-45500-2_4.
• Sobkow, A., Olszewska, A., & Traczyk, J. (2020). Multiple numeric competencies predict decision outcomes beyond fluid intelligence and cognitive reflection. Intelligence, 80, 101452. doi: 10.1016/j.intell.2020.101452.
• Taleb, N. N. (2020). Statistical Consequences of Fat Tails: Real World preasymptotics, epistemology, and applications. arXiv:2001.10488.
• Thaler, R. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior & Organization, 1(1), 39-60. doi: 10.1016/0167-2681(80)90051-7.
• Traczyk, J., & Fulawka, K. (2016). Numeracy moderates the influence of task-irrelevant affect on probability weighting. Cognition, 151, 37-41. doi: 10.1016/j.cognition.2016.03.002.
• Traczyk, J., Sobkow, A., Fulawka, K., Kus, J., Petrova, D., & Garcia-Retamero, R. (2018). Numerate decision makers don't use more effortful strategies unless it pays: A process tracing investigation of skilled and adaptive strategy selection in risky decision making. Judgment and Decision Making, 13(4), 372-381.
• Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323. doi: 10.1007/BF00122574.

Identyfikatory

DOI

10.7206/DEC.1733-0092.150a