Sage Journals: Discover world-class research

Abstract

How can we nudge people to not engage in unethical behaviors, such as hoarding and violating social-distancing guidelines, during the COVID-19 pandemic? Because past research on antecedents of unethical behavior has not provided a clear answer, we turned to machine learning to generate novel hypotheses. We trained a deep-learning model to predict whether or not World Values Survey respondents perceived unethical behaviors as justifiable, on the basis of their responses to 708 other items. The model identified optimism about the future of humanity as one of the top predictors of unethicality. A preregistered correlational study (N = 218 U.S. residents) conceptually replicated this finding. A preregistered experiment (N = 294 U.S. residents) provided causal support: Participants who read a scenario conveying optimism about the COVID-19 pandemic were less willing to justify hoarding and violating social-distancing guidelines than participants who read a scenario conveying pessimism. The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.

Keywords

COVID-19 machine learning optimism neural network unethical behavior open data open materials preregistered

Get full access to this article

View all access options for this article.

References

Alpaydin

(2020). Introduction to machine learning. Cambridge, MA: MIT Press.

Baldi

Sadowski

Whiteson

(2014). Searching for exotic particles in high-energy physics with deep learning. Nature Communications, 5, 4308–4316.

Biecek

(2018). DALEX: Explainers for complex predictive models in R. The Journal of Machine Learning Research, 19, 3245–3249.

Bleidorn

Hopwood

C. J.

(2019). Using machine learning to advance personality assessment and theory. Personality and Social Psychology Review, 23, 190–203.

Boehmke

Greenwell

B. M.

(2019). Hands-on machine learning with R. Boca Raton, FL: CRC Press.

Celisse

(2014). Optimal cross-validation in density estimation with the L²-loss. The Annals of Statistics, 42, 1879–1910.

Chollet

(2015). Keras [Computer software]. Retrieved from https://github.com/keras-team

Cullen

J. B.

Parboteeah

K. P.

Hoegl

(2004). Cross-national differences in managers’ willingness to justify ethically suspect behaviors: A test of institutional anomie theory. Academy of Management Journal, 47, 411–421.

Deng

Chang

Ido

M. S.

Long

(2016). Multiple imputation for general missing data patterns in the presence of high-dimensional data. Scientific Reports, 6, Article 21689. doi:10.1038/srep21689

10.

Detert

J. R.

Treviño

L. K.

Sweitzer

V. L.

(2008). Moral disengagement in ethical decision making: A study of antecedents and outcomes. Journal of Applied Psychology, 93, 374–391.

11.

Dong

Torgler

(2009). Corruption and political interest: Empirical evidence at the micro level. Journal of Interdisciplinary Economics, 21, 295–325.

12.

Ellemers

van der Toorn

Paunov

van Leeuwen

(2019). The psychology of morality: A review and analysis of empirical studies published from 1940 through 2017. Personality and Social Psychology Review, 23, 332–336.

13.

Gerlach

Teodorescu

Hertwig

(2019). The truth about lies: A meta-analysis on dishonest behavior. Psychological Bulletin, 145, 1–44.

14.

Goldstone

R. L.

Lupyan

(2016). Discovering psychological principles by mining naturally occurring data sets. Topics in Cognitive Science, 8, 548–568.

15.

Hansel

Mato

Meunier

(1992). Memorization without generalization in a multilayered neural network. EPL (Europhysics Letters), 20, 471–476.

16.

Hastie

Tibshirani

Friedman

(2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York, NY: Springer Science & Business Media.

17.

Henrich

Heine

S. J.

Norenzayan

(2010). The weirdest people in the world? Behavioral and Brain Sciences, 33, 61–83.

18.

Hinton

G. E.

Srivastava

Krizhevsky

Sutskever

Salakhutdinov

R. R.

(2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv. Retrieved from http://arxiv.org/abs/1207.0580

19.

Inglehart

Haerpfer

Moreno

Welzel

Kizilova

Diez-Medrano

. . . Puranen

(Eds.). (2014). World Values Survey: All rounds - country-pooled datafile. Retrieved from https://www.worldvaluessurvey.org/WVSDocumentationWVL.jsp

20.

Jha

Ward

Paul

Liao

W.-K.

Choudhary

Wolverton

Agrawal

(2018). ElemNet: Deep learning the chemistry of materials from only elemental composition. Scientific Reports, 8, Article 17593. doi:10.1038/s41598-018-35934-y

21.

Karp

R. M.

(1975). On the computational complexity of combinatorial problems. Networks, 5, 45–68.

22.

Kong

D. T.

Drew

(2016). Meta-analyzing the differential effects of emotions on disengagement from unethical behavior: An asymmetric self-regulation model. In Kong

D. T.

Forsyth

D. R.

(Eds.), Leading through conflict: Into the fray (pp. 23–44). New York, NY: Palgrave Macmillan.

23.

Marshall

G. N.

Wortman

C. B.

Kusulas

J. W.

Hervig

L. K.

Vickers

R. R.

Jr. (1992). Distinguishing optimism from pessimism: Relations to fundamental dimensions of mood and personality. Journal of Personality and Social Psychology, 62, 1067–1074.

24.

Martin

N. D.

Rigoni

Vohs

K. D.

(2017). Free will beliefs predict attitudes toward unethical behavior and criminal punishment. Proceedings of the National Academy of Sciences, USA, 114, 7325–7330.

25.

Molnar

(2020). Interpretable machine learning: A guide for making black box models explainable. Retrieved from https://christophm.github.io/interpretable-ml-book/

26.

Norenzayan

Shariff

A. F.

(2008). The origin and evolution of religious prosociality. Science, 322, 58–62.

27.

Reyzin

(2019). Unprovability comes to machine learning. Nature, 565, 166–167.

28.

Salovey

Birnbaum

(1989). Influence of mood on health-relevant cognitions. Journal of Personality and Social Psychology, 57, 539–551.

29.

Scheier

M. F.

Carver

C. S.

(1985). Optimism, coping, and health: Assessment and implications of generalized outcome expectancies. Health Psychology, 4, 219–247.

30.

Segerstrom

S. C.

Taylor

S. E.

Kemeny

M. E.

Fahey

J. L.

(1998). Optimism is associated with mood, coping, and immune change in response to stress. Journal of Personality and Social Psychology, 74, 1646–1655.

31.

Simha

Parboteeah

K. P.

(2019). The Big 5 personality traits and willingness to justify unethical behavior—A cross-national examination. Journal of Business Ethics. Advance online publication. doi:10.1007/s10551-019-04142-7

32.

Sommer

Bloom

P. B. N.

Arikan

(2013). Does faith limit immorality? The politics of religion and corruption. Democratization, 20, 287–309.

33.

Stekhoven

D. J.

Bühlmann

(2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28, 112–118.

34.

Stokes

J. M.

Yang

Swanson

Jin

Cubillos-Ruiz

Donghia

N. M.

Tran

V. M.

(2020). A deep learning approach to antibiotic discovery. Cell, 180, 688–702.

35.

Tsang

Cheng

Liu

(2018). Detecting statistical interactions from neural network weights. arXiv. Retrieved from https://arxiv.org/abs/1705.04977

36.

Vincent

L. C.

Emich

K. J.

Goncalo

J. A.

(2013). Stretching the moral gray zone: Positive affect, moral disengagement, and dishonesty. Psychological Science, 24, 595–599.

37.

Yarkoni

Westfall

(2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12, 1100–1122.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.61 MB

0.09 MB