Irrationality-Aware Human Machine Collaboration: Mitigating Alterfactual Irrationality in Copy Trading

Published Online:https://doi.org/10.1287/isre.2023.0591

References

  • Abbasi A, Parsons J, Pant G, Sheng ORL, Sarker S (2024) Pathways for design research on artificial intelligence. Inform. Systems Res. 35(2):441–459.LinkGoogle Scholar
  • Agrawal A, Catalini C, Goldfarb A (2015) Crowdfunding: Geography, social networks, and the timing of investment decisions. Economics Management Strategy 24(2):253–274.CrossrefGoogle Scholar
  • Agrawal A, Gans JS, Goldfarb A (2019) Artificial intelligence: The ambiguous labor market impact of automating prediction. J. Econom. Perspective 33(2):31–50.CrossrefGoogle Scholar
  • Ahmed AS, Safdar I (2017) Evidence on the presence of representativeness bias in investor interpretation of consistency in sales growth. Management Sci. 63(1):97–113.LinkGoogle Scholar
  • Alok S, Kumar N, Wermers R (2020) Do fund managers misestimate climatic disaster risk. Rev. Financial Stud. 33(3):1146–1183.CrossrefGoogle Scholar
  • Apesteguia J, Oechssler J, Weidenholzer S (2020) Copy trading. Management Sci. 66(12):5608–5622.LinkGoogle Scholar
  • Becker GS (1962) Irrational behavior and economic theory. J. Political Econom. 70(1):1–13.CrossrefGoogle Scholar
  • Ben-David I, Li J, Rossi A, Song Y (2022) What do mutual fund investors really care about? Rev. Financial Stud. 35(4):1723–1774.CrossrefGoogle Scholar
  • Bergman S, Marchal N, Mellor J, Mohamed S, Gabriel I, Isaac W (2024) Stela: A community-centred approach to norm elicitation for ai alignment. Sci. Rep. 14(1):6616.CrossrefGoogle Scholar
  • Bordalo P, Gennaioli N, Shleifer A (2012) Salience theory of choice under risk. Quart. J. Econom. 127(3):1243–1285.CrossrefGoogle Scholar
  • Bubeck S, Chandrasekaran V, Eldan R, Gehrke J, Horvitz E, Kamar E, Lee P, et al. (2023) Sparks of artificial general intelligence: Early experiments with gpt-4. Preprint, submitted March 22, https://arxiv.org/abs/2303.12712.Google Scholar
  • Caliskan A, Bryson JJ, Narayanan A (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356(6334):183–186.CrossrefGoogle Scholar
  • Caton S, Haas C (2024) Fairness in machine learning: A survey. ACM Comput. Survey 56(7):1–38.CrossrefGoogle Scholar
  • Chan L, Critch A, Dragan A (2021) Human irrationality: Both bad and good for reward inference. Preprint, submitted November 12, https://arxiv.org/abs/2111.06956.Google Scholar
  • Chatzis SP, Siakoulis V, Petropoulos A, Stavroulakis E, Vlachogiannakis N (2018) Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems Appl. 112:353–371.CrossrefGoogle Scholar
  • Chen Z (2023) Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanity Soc. Sci. Comm. 10(1):1–12.Google Scholar
  • Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. Krishnapuram B, Shah M, Smola A, Aggarwal C, Shen D, Rastogi R, eds. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 785–794.Google Scholar
  • Chen G, Huang L, Xiao S, Zhang C, Zhao H (2024) Attending to customer attention: A novel deep learning method for leveraging multimodal online reviews to enhance sales prediction. Inform. Systems Res. 35(2):829–849.LinkGoogle Scholar
  • Choudhury P, Starr E, Agarwal R (2020) Machine learning and human capital complementarities: Experimental evidence on bias mitigation. Strategic Management J. 41(8):1381–1411.CrossrefGoogle Scholar
  • Cowgill B (2020) Bias and productivity in humans and algorithms: Theory and evidence from resume screening. Columbia Business School Research paper, Columbia University, New York.Google Scholar
  • De Cremer D, Kasparov G (2021) AI should augment human intelligence, not replace it. Harvard Bus. Rev. 18(1):1–8.Google Scholar
  • DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44(3):837–845.CrossrefGoogle Scholar
  • Dessaint O, Matray A (2017) Do managers overreact to salient risks? Evidence from hurricane strikes. J. Financial Econom. 126(1):97–121.CrossrefGoogle Scholar
  • Dietvorst BJ, Simmons JP, Massey C (2018) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.LinkGoogle Scholar
  • Dorfleitner G, Scheckenbach I (2022) Trading activity on social trading platforms: A behavioral approach. J. Risk Finance 23(1):32–54.CrossrefGoogle Scholar
  • Dou ZY, Xu Y, Gan Z, Wang J, Wang S, Wang L, Zhu C, et al. (2022) An empirical study of training end-to-end vision-and-language transformers. Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognition, 18166–18176.Google Scholar
  • Dung L (2023) Current cases of ai misalignment and their implications for future risks. Synthese 202(5):138.CrossrefGoogle Scholar
  • Fu R, Huang Y, Singh PV (2021) Crowds, lending, machine, and bias. Inform. Systems Res. 32(1):72–92.LinkGoogle Scholar
  • Fügener A, Grahl J, Gupta A, Ketter W (2021) Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. MIS Quart. 45(3):1527–1556.CrossrefGoogle Scholar
  • Gemayel R, Preda A (2018) Does a scopic regime produce conformism? Herding behavior among trade leaders on social trading platforms. Eur. J. Finance 24(14):1144–1175.CrossrefGoogle Scholar
  • Gorishniy Y, Rubachev I, Khrulkov V, Babenko A (2021) Revisiting deep learning models for tabular data. Adv. Neural Inform. Processing Systems 34:18932–18943.Google Scholar
  • Gorman R, Armstrong S (2022) The dangers in algorithms learning humans’ values and irrationalities. Preprint, submitted February 28, https://arxiv.org/abs/2202.13985.Google Scholar
  • Graham JR, Harvey CR, Huang H (2009) Investor competence, trading frequency, and home bias. Management Sci. 55(7):1094–1106.LinkGoogle Scholar
  • Graham JR, Hanlon M, Shevlin T, Shroff N (2017) Tax rates and corporate decision-making. Rev. Financial Stud. 30(9):3128–3175.CrossrefGoogle Scholar
  • Grinsztajn L, Oyallon E, Varoquaux G (2022) Why do tree-based models still outperform deep learning on typical tabular data? Adv. Neural Inform. Processing Systems 35:507–520.Google Scholar
  • Gu S, Kelly B, Xiu D (2020) Empirical asset pricing via machine learning. Rev. Financial Stud. 33(5):2223–2273.CrossrefGoogle Scholar
  • Guo L (2016) Contextual deliberation and preference construction. Management Sci. 62(10):2977–2993.LinkGoogle Scholar
  • Guo X, Grushka-Cockayne Y, De Reyck B (2022) Forecasting airport transfer passenger flow using real-time data and machine learning. Manufacturing Systems Oper. Management 24(6):3193–3214.LinkGoogle Scholar
  • Hartung J, Knapp G, Sinha BK (2011) Statistical Meta-Analysis with Applications (John Wiley & Sons).Google Scholar
  • Heeb F, Kölbel JF, Paetzold F, Zeisberger S (2023) Do investors care about impact? Rev. Financial Stud. 36(5):1737–1787.CrossrefGoogle Scholar
  • Heimer RZ (2016) Peer pressure: Social interaction and the disposition effect. Rev. Financial Stud. 29(11):3177–3209.CrossrefGoogle Scholar
  • Herrmann PN, Kundisch DO, Rahman MS (2015) Beating irrationality: Does delegating to it alleviate the sunk cost effect? Management Sci. 61(4):831–850.LinkGoogle Scholar
  • Hovland CI, Weiss W (1951) The influence of source credibility on communication effectiveness. Public Opinion Quart. 15(4):635–650.CrossrefGoogle Scholar
  • Jena SD, Lodi A, Sole C (2022) On the estimation of discrete choice models to capture irrational customer behaviors. INFORMS J. Comput. 34(3):1606–1625.LinkGoogle Scholar
  • Ji J, Qiu T, Chen B, Zhang B, Lou H, Wang K, Duan Y, et al. (2023) AI alignment: A comprehensive survey. Preprint, submitted October 30, https://arxiv.org/abs/2310.19852.Google Scholar
  • Kahneman D, Tversky A (1972) Subjective probability: A judgment of representativeness. Cognitive Psych. 3(3):430–454.CrossrefGoogle Scholar
  • Kahneman D, Tversky A (1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2):263–291.Google Scholar
  • Kamishima T, Akaho S, Sakuma J (2011) Fairness-aware learning through regularization approach. Proc. IEEE 11th Internat. Conf. Data Mining Workshops (IEEE, Piscataway, NJ), 643–650.Google Scholar
  • Kleinberg J, Mullainathan S, Raghavan M (2023) The challenge of understanding what users want: Inconsistent preferences and engagement optimization. Management Sci. 70(9):6336–6355.LinkGoogle Scholar
  • Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S (2018) Human decisions and machine predictions. Quart. J. Econom. 133(1):237–293.CrossrefGoogle Scholar
  • Koch A (2017) Herd behavior and mutual fund performance. Management Sci. 63(11):3849–3873.LinkGoogle Scholar
  • Krogh A, Hertz J (1991) A simple weight decay can improve generalization. Moody JE, Hanson SJ, Lippmann RP, eds. Proc. 5th Internat. Conf. Neural Inform. Processing Systems (Morgan Kaufmann Publishers Inc., San Francisco), 950–957.Google Scholar
  • Lakkaraju H, Rudin C (2016) Learning cost-effective treatment regimes using Markov decision processes. Preprint, submitted October 21, https://arxiv.org/abs/1610.06972.Google Scholar
  • Lakkaraju H, Kleinberg J, Leskovec J, Ludwig J, Mullainathan S (2017) The selective labels problem: Evaluating algorithmic predictions in the presence of unobservables. Proc. 23rd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 275–284.Google Scholar
  • Lakonishok J, Shleifer A, Vishny RW (1992) The impact of institutional trading on stock prices. J. Financial Econom. 32(1):23–43.CrossrefGoogle Scholar
  • Lee YJ, Hosanagar K, Tan Y (2015) Do I follow my friends or the crowd? Information cascades in online movie ratings. Management Sci. 61(9):2241–2258.LinkGoogle Scholar
  • Lin X, Baweja H, Kantor G, Held D (2019) Adaptive auxiliary task weighting for reinforcement learning. Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 32 (Curran Associates, Inc., New York), 4773–4784.Google Scholar
  • Liu M (2022) Assessing human information processing in lending decisions: A machine learning approach. J. Accounting Res. 60(2):607–651.CrossrefGoogle Scholar
  • Liu Y, Li X, Zheng Z (2023) Smart natural disaster relief: Assisting victims with artificial intelligence in lending. Inform. Systems Res. 35(2):489–504.Google Scholar
  • Liu YY, Nacher JC, Ochiai T, Martino M, Altshuler Y (2014) Prospect theory for online financial trading. PLoS One 9(10):e109458.CrossrefGoogle Scholar
  • Logg JM (2019) Using algorithms to understand the biases in your organization. Harvard Bus. Rev. (August 9), https://hbr.org/2019/08/using-algorithms-to-understand-the-biases-in-your-organization.Google Scholar
  • Loomes G, Pogrebna G (2017) Do preference reversals disappear when we allow for probabilistic choice? Management Sci. 63(1):166–184.LinkGoogle Scholar
  • Lu J, Yang J, Batra D, Parikh D (2016) Hierarchical question-image co-attention for visual question answering. Lee DD, von Luxburg U, Garnett R, Sugiyama M, Guyon I, eds. Proc. 30th Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Inc., New York), 289–297.Google Scholar
  • Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. von Luxburg U, Guyon I, Bengio S, Wallach H, Fergus R, eds. Proc. 31st Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Inc., New York), 4768–4777.Google Scholar
  • Macmillan-Scott O, Musolesi M (2023) (Ir)rationality in AI: State of the art, research challenges and open questions. Preprint, submitted November 28, https://arxiv.org/abs/2311.17165.Google Scholar
  • Medina PC (2021) Side effects of nudging: Evidence from a randomized intervention in the credit card market. Rev. Financial Stud. 34(5):2580–2607.CrossrefGoogle Scholar
  • Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2021) A survey on bias and fairness in machine learning. ACM Comput. Survey 54(6):1–35.CrossrefGoogle Scholar
  • Mertes S, Huber T, Karle C, Weitz K, Schlagowski R, Conati C, André E (2024) Relevant irrelevance: Generating alterfactual explanations for image classifiers. Preprint, submitted May 8, https://arxiv.org/abs/2405.05295.Google Scholar
  • Moradi R, Berangi R, Minaei B (2020) A survey of regularization strategies for deep models. Artificial Intelligence Rev. 53(6):3947–3986.CrossrefGoogle Scholar
  • Morewedge CK, Mullainathan S, Naushan HF, Sunstein CR, Kleinberg J, Raghavan M, Ludwig JO (2023) Human bias in algorithm design. Natural Human Behav. 7(11):1822–1824.CrossrefGoogle Scholar
  • Mullainathan S, Obermeyer Z (2022) Diagnosing physician error: A machine learning approach to low-value health care. Quart. J. Econom. 137(2):679–727.CrossrefGoogle Scholar
  • Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, et al. (2022) Training language models to follow instructions with human feedback. Adv. Neural Inform. Processing Systems 35:27730–27744.Google Scholar
  • Pavlou PA (2018) Internet of things: Will humans be replaced or augmented? NIM Marketing Intelligence Rev. 10(2):42–47.CrossrefGoogle Scholar
  • Ralekar C, Gandhi TK, Chaudhury S (2021) Collaborative human machine attention module for character recognition. Proc. 25th Internat. Conf. Pattern Recognition (IEEE, Piscataway, NJ), 9874–9880.Google Scholar
  • Saifee DH, Zheng Z, Bardhan IR, Lahiri A (2020) Are online reviews of physicians reliable indicators of clinical outcomes? A focus on chronic disease management. Inform. Systems Res. 31(4):1282–1300.LinkGoogle Scholar
  • Samra S (2022) Finvasia group acquires zulutrade, the world’s largest social trading network. Accessed November 17, 2022, https://www.businesswire.com/news/home/20211213006074/en/Finvasia-Group-Acquires-ZuluTrade-the-Worlds-Largest-Social-Trading-Network.Google Scholar
  • Santos CFGD, Papa JP (2022) Avoiding overfitting: A survey on regularization methods for convolutional neural networks. ACM Comput. Survey 54(10s):1–25.CrossrefGoogle Scholar
  • Schweisfurth TG, Schöttl CP, Raasch C, Zaggl MA (2023) Distributed decision-making in the shadow of hierarchy: How hierarchical similarity biases idea evaluation. Strategic Management J. 44(9):2255–2282.CrossrefGoogle Scholar
  • Shapley LS (1953) A value for n-person games. Kuhn HW, Tucker AW, eds. Contributions to the Theory of Games, Annals of Mathematics Studies (Princeton University Press, Princeton, NJ), 307–317.Google Scholar
  • Simon HA (1955) A behavioral model of rational choice. Quart. J. Econom. 69(1):99–118.CrossrefGoogle Scholar
  • Simonsohn U, Ariely D (2008) When rational sellers face nonrational buyers: Evidence from herding on ebay. Management Sci. 54(9):1624–1637.LinkGoogle Scholar
  • Sweller J (1988) Cognitive load during problem solving: Effects on learning. Cognitive Sci. 12(2):257–285.CrossrefGoogle Scholar
  • Tantri P (2021) Fintech for the poor: Financial intermediation without discrimination. Rev. Finance 25(2):561–593.CrossrefGoogle Scholar
  • 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.CrossrefGoogle Scholar
  • Von Neumann J, Morgenstern O (2007) Theory of games and economic behavior. Theory of Games and Economic Behavior (Princeton University Press, Princeton, NJ).Google Scholar
  • Wang Y, Lewis M, Schweidel DA (2018) A border strategy analysis of ad source and message tone in senatorial campaigns. Marketing Sci. 37(3):333–355.LinkGoogle Scholar
  • Watanabe S (2023) Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance. Preprint, submitted April 21, https://arxiv.org/abs/2304.11127.Google Scholar
  • Wei X, Zhang Z, Zhang M, Chen W, Zeng DD (2022) Combining crowd and machine intelligence to detect false news on social media. MIS Quart. 46(2):977–1008.CrossrefGoogle Scholar
  • Zeng Z, Dai H, Zhang DJ, Zhang H, Zhang R, Xu Z, Shen ZJM (2023) The impact of social nudges on user-generated content for social network platforms. Management Sci. 69(9):5189–5208.LinkGoogle Scholar
  • Zhang J, Liu P (2012) Rational herding in microloan markets. Management Sci. 58(5):892–912.LinkGoogle Scholar
  • Zhang Y, Yang Q (2021) A survey on multi-task learning. IEEE Trans. Knowledge Data Engrg. 34(12):5586–5609.CrossrefGoogle Scholar
  • Zhang CY, Hemmeter J, Kessler JB, Metcalfe RD, Weathers R (2023) Nudging timely wage reporting: Field experimental evidence from the us supplemental security income program. Management Sci. 69(3):1341–1353.LinkGoogle Scholar
  • Zignaly (2022) Why people lose money with copy or social trading. Accessed July 24, 2022, https://zignaly.com/crypto-knowledge-base/5-reasons-of-failures-with-copy-trading.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.