Human and Machine: The Impact of Machine Input on Decision Making Under Cognitive Limitations

References

• Abis S (2020) Man vs. machine: Quantitative and discretionary equity management. Preprint, submitted December 21, https://dx.doi.org/10.2139/ssrn.3717371.Google Scholar
• Agrawal AK, Gans JS, Goldfarb A (2018) Prediction, judgment and complexity: A theory of decision making and artificial intelligence. Technical report, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Alizamir S, de Véricourt F, Sun P (2013) Diagnostic accuracy under congestion. Management Sci. 59(1):157–171.Link, Google Scholar
• Alizamir S, de Véricourt F, Sun P (2019) Search under accumulated pressure. Oper. Res. 70(3):1393–1409.Link, Google Scholar
• Autor DH (2015) Why are there still so many jobs? The history and future of workplace automation. J. Econom. Perspective 29(3):3–30.Crossref, Google Scholar
• Ayvaci MU, Mobini Z, Özer Ö (2021) To catch a killer: A data-driven personalized and compliance-aware sepsis alert system. Preprint, submitted April 6, https://dx.doi.org/10.2139/ssrn.3805931.Google Scholar
• Bansal G, Nushi B, Kamar E, Weld DS, Lasecki WS, Horvitz E (2019) Updates in human-AI teams: Understanding and addressing the performance/compatibility tradeoff. Proc. Conf. AAAI Artificial Intelligence 33:2429–2437.Crossref, Google Scholar
• Bordt S, von Luxburg U (2020) When humans and machines make joint decisions: A non-symmetric bandit model. Preprint, submitted July 9, https://arxiv.org/abs/2007.04800.Google Scholar
• Boyacı T, Akçay Y (2018) Pricing when customers have limited attention. Management Sci. 64(7):2995–3014.Link, Google Scholar
• Brosset P, Jain A, Khemka Y, Buvat J, Thieullent A, Khadikar A, Patsko S (2019) Scaling AI in manufacturing operations: A practitioners’ perspective. Accessed March 10, 2023, https://www.capgemini.com/be-en/wp-content/uploads/sites/17/2019/12/AI-in-manufacturing-operations.pdf.Google Scholar
• Canyakmaz C, Boyaci T (2021) Queueing systems with rationally inattentive customers. Manufacturing Service Oper. Management. 25(1):266–287.Google Scholar
• Caplin A, Dean M (2013) Behavioral implications of rational inattention with shannon entropy. Technical report, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Caplin A, Dean M, Leahy J (2019) Rational inattention, optimal consideration sets, and stochastic choice. Rev. Econom. Stud. 86(3):1061–1094.Crossref, Google Scholar
• Case N (2018) How to become a centaur. J. Design Sci. https://doi.org/10.21428/61b2215c.Google Scholar
• Che YK, Mierendorff K (2019) Optimal dynamic allocation of attention. Amer. Econom. Rev. 109(8):2993–3029.Crossref, Google Scholar
• Cowgill B (2018) The impact of algorithms on judicial discretion: Evidence from regression discontinuities. Working paper.Google Scholar
• DARPA (2018) Darpa announces $2 billion campaign to develop next wave of AI technologies. Accessed September 20, 2019, www.darpa.mil/news-events/2018-09-07.Google Scholar
• de Véricourt F, Gurkan H (2022) Is your machine better than you? you may never know. Working paper, ESMT Berlin.Google Scholar
• DeGroot M (1970) Optimal Statistical Decisions (McGraw-Hill, New York).Google Scholar
• Diamond A (2013) Executive functions. Annu. Rev. Psych. 64:135–168.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2016) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.Link, Google Scholar
• Donohue K, Ozer O, Zheng Y (2020) Behavioral operations: Past, present and future. Manufacturing Service Oper. Management 11(22):191–202.Link, Google Scholar
• Doshi-Velez F, Kim B (2017) Toward a rigorous science of interpretable machine learning. Preprint, submitted February 28, https://arxiv.org/abs/1702.08608.Google Scholar
• Fabozzi FJ, Focardi SM, Jonas CL (2008) On the challenges in quantitative equity management. Quant. Finance 8(7):649–665.Crossref, Google Scholar
• Felten EW, Raj M, Seamans R (2019) The variable impact of artificial intelligence on labor: The role of complementary skills and technologies. Preprint, submitted May 7, https://dx.doi.org/10.2139/ssrn.3368605.Google Scholar
• Frankel A, Kamenica E (2019) Quantifying information and uncertainty. Amer. Econom. Rev. 109(10):3650–3680.Crossref, Google Scholar
• Gee K (2017) In unilever’s radical hiring experiment, resumes are out, algorithms are in. Accessed March 10, 2023, https://www.wsj.com/articles/in-unilevers-radical-hiring-experiment-resumes-are-out-algorithms-are-in-1498478400.Google Scholar
• Hoffman M, Kahn LB, Li D (2017) Discretion in hiring. Quart. J. Econom. 133(2):765–800.Crossref, Google Scholar
• Huettner F, Boyacı T, Akçay Y (2019) Consumer choice under limited attention when alternatives have different information costs. Oper. Res. 67(3):671–699.Link, Google Scholar
• IBM (2017) Welcome to the cognitive supply chain, executive report. Accessed March 10, 2023, https://www.ibm.com/downloads/cas/DGP9YPZV.Google Scholar
• Ibrahim R, Kim SH, Tong J (2021) Eliciting human judgment for prediction algorithms. Management Sci. 67(4):2314–2325.Link, Google Scholar
• Jerath K, Ren Q (2021) Consumer rational (in) attention to favorable and unfavorable product information, and firm information design. J. Marketing Res. 58(2):343–362.Crossref, Google Scholar
• Kacperczyk M, Van Nieuwerburgh S, Veldkamp L (2016) A rational theory of mutual funds’ attention allocation. Econometrica 84(2):571–626.Crossref, Google Scholar
• Kaminsky P, Schonert J (2017) The neglected art of risk detection. Accessed March 10, 2023, https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/the-neglected-art-of-risk-detection.Google Scholar
• Karlinsky-Shichor Y, Netzer O (2019) Automating the b2b salesperson pricing decisions: Can machines replace humans and when. Preprint, submitted May 6, https://dx.doi.org/10.2139/ssrn.3368402.Google Scholar
• Katz M (2017) Welcome to the era of the AI cowork. Accessed September 20, 2019, www.wired.com/story/welcome-to-the-era-of-the-ai-coworker/.Google Scholar
• Kesavan S, Kushwaha T (2020) Field experiment on the profit implications of merchants’ discretionary power to override data-driven decision-making tools. Management Sci. 66(11):5182–5190.Link, Google Scholar
• Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S (2017) Human decisions and machine predictions. Quart. J. Econom. 133(1):237–293.Crossref, Google Scholar
• Laureiro-Martínez D, Brusoni S (2018) Cognitive flexibility and adaptive decision-making: Evidence from a laboratory study of expert decision makers. Strategic Management J. 39(4):1031–1058.Crossref, Google Scholar
• Lebovitz S, Lifshitz-Assaf H, Levina N (2020) To Incorporate or Not to Incorporate AI for Critical Judgments: The Importance of Ambiguity in Professionals’ Judgment Process (NYU Stern School of Business, New York).Google Scholar
• Lipton ZC (2016) The mythos of model interpretability. Preprint, submitted June 10, https://arxiv.org/abs/1606.03490.Google Scholar
• Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, et al. (2019) A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. Lancet Digital Health 1(6):e271–e297.Crossref, Google Scholar
• Mackowiak B, Matejka F, Wiederholt M (2021) Rational inattention: A review. Working paper, European Central Bank, Frankfurt, Germany.Google Scholar
• Marcus G (2018) Deep learning: A critical appraisal. Preprint, submitted January 2, https://arxiv.org/abs/1801.00631.Google Scholar
• Matějka F (2015) Rigid pricing and rationally inattentive consumer. J. Econom. Theory 158:656–678.Crossref, Google Scholar
• Matějka F, McKay A (2015) Rational inattention to discrete choices: A new foundation for the multinomial logit model. Amer. Econom. Rev. 105(1):272–298.Crossref, Google Scholar
• McCardle KF, Tsetlin I, Winkler RL (2018) When to abandon a research project and search for a new one. Oper. Res. 66(3):799–813.Link, Google Scholar
• Mims C (2017) Without humans, artificial intelligence is still pretty stupid. Wall Street Journal. Accessed March 10, https://www.wsj.com/amp/articles/without-humans-artificial-intelligence-is-still-pretty-stupid-1510488000.Google Scholar
• Nwankpa C, Eze S, Ijomah W, Gachagan A, Marshall S (2021) Achieving remanufacturing inspection using deep learning. J. Remanufacturing 11(2):89–105.Crossref, Google Scholar
• Olszewski W, Wolinsky A (2016) Search for an object with two attributes. J. Econom. Theory 161:145–160.Crossref, Google Scholar
• Özer Ö, Zheng Y, Chen KY (2011) Trust in forecast information sharing. Management Sci. 57(6):1111–1137.Link, Google Scholar
• Patel BN, Rosenberg L, Willcox G, Baltaxe D, Lyons M, Irvin J, Rajpurkar P, et al. (2019) Human–machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digital Medicine 2(1):1–10.Google Scholar
• Payne JW, Bettman JR, Johnson EJ (1993) The Adaptive Decision Maker (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Petropoulos F, Kourentzes N, Nikolopoulos K, Siemsen E (2018) Judgmental selection of forecasting models. J. Oper. Management 60:34–46.Crossref, Google Scholar
• Saenz MJ, Revilla E, Simón C (2020) Designing AI systems with human-machine teams. MIT Sloan Management Rev. 61(3):1–5.Google Scholar
• Sanjurjo A (2017) Search with multiple attributes: Theory and empirics. Games Econom. Behav. 104:535–562.Crossref, Google Scholar
• Sims CA (2003) Implications of rational inattention. J. Monetary Econom. 50(3):665–690.Crossref, Google Scholar
• Sims CA (2006) Rational inattention: Beyond the linear-quadratic case. Amer. Econom. Rev. 96(2):158–163.Crossref, Google Scholar
• Stoffel E, Becker AS, Wurnig MC, Marcon M, Ghafoor S, Berger N, Boss A (2018) Distinction between phyllodes tumor and fibroadenoma in breast ultrasound using deep learning image analysis. Eur. J. Radiology Open 5:165–170.Crossref, Google Scholar
• Sun J, Zhang DJ, Hu H, Van Mieghem JA (2021) Predicting human discretion to adjust algorithmic prescription: A large-scale field experiment in warehouse operations. Management Sci. 68(2):846–865.Link, Google Scholar
• Van Donselaar KH, Gaur V, Van Woensel T, Broekmeulen RA, Fransoo JC (2010) Ordering behavior in retail stores and implications for automated replenishment. Management Sci. 56(5):766–784.Link, Google Scholar