The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations

References

• Auer P, Jaksch T, Ortner R (2008) Near-optimal regret bounds for reinforcement learning. Koller D, Schuurmans D, Bengio Y, Bottou L, eds. Adv. Neural Inform. Processing Systems, vol. 21 (Curran Associates, Inc., Red Hook, NY), 89–96.Google Scholar
• Bastani H, Bastani O, Sinchaisri WP (2021) Improving human decision-making with machine learning. Preprint, submitted August 19, https://arxiv.org/abs/2108.08454.Google Scholar
• Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust Optimization, vol. 28 (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Bertsimas D, Sim M (2004) The price of robustness. Oper. Res. 52(1):35–53.Link, Google Scholar
• Bertsimas D, Farias VF, Trichakis N (2013) Fairness, efficiency, and flexibility in organ allocation for kidney transplantation. Oper. Res. 61(1):73–87.Link, Google Scholar
• Bertsimas D, Nohadani O, Teo KM (2010) Nonconvex robust optimization for problems with constraints. INFORMS J. Comput. 22(1):44–58.Link, Google Scholar
• Bertsimas D, Pauphilet J, Stevens J, Tandon M (2022) Predicting inpatient flow at a major hospital using interpretable analytics. Manufacturing Service Oper. Management 24(6):2809–2824.Link, Google Scholar
• Besbes O, Gur Y, Zeevi A (2014) Stochastic multi-armed-bandit problem with non-stationary rewards. Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems, vol. 27 (Curran Associates, Inc., Red Hook, NY), 199–207.Google Scholar
• Besbes O, Gur Y, Zeevi A (2015) Non-stationary stochastic optimization. Oper. Res. 63(5):1227–1244.Link, Google Scholar
• Blanchet J, Gallego G, Goyal V (2016) A Markov chain approximation to choice modeling. Oper. Res. 64(4):886–905.Link, Google Scholar
• Boyacı T, Canyakmaz C, de Véricourt F (2024) Human and machine: The impact of machine input on decision-making under cognitive limitations. Management Sci. 70(2):1258–1275.Link, Google Scholar
• Bravo F, Shaposhnik Y (2020) Mining optimal policies: A pattern recognition approach to model analysis. INFORMS J. Optim. 2(3):145–166.Link, Google Scholar
• Caro F, de Tejada Cuenca AS (2023) Believing in analytics: Managers’ adherence to price recommendations from a DSS. Manufacturing Service Oper. Management 25(2):524–542.Link, Google Scholar
• Cheung WC, Simchi-Levi D, Zhu R (2023) Non-stationary reinforcement learning: The blessing of (more) optimism. Management Sci. 69(10):5722–5739.Link, Google Scholar
• Ciocan DF, Mišić VV (2022) Interpretable optimal stopping. Management Sci. 68(3):1616–1638.Link, Google Scholar
• de Véricourt F, Gurkan H (2023) Is your machine better than you? You may never know. Management Sci., ePub ahead of print May 25, https://doi.org/10.1287/mnsc.2023.4791.Link, Google Scholar
• Delage E, Mannor S (2010) Percentile optimization for Markov decision processes with parameter uncertainty. Oper. Res. 58(1):203–213.Link, Google Scholar
• Derman C (1970) Finite State Markovian Decision Processes (Academic Press, New York).Google Scholar
• Désir A, Goyal V, Segev D, Ye C (2020) Constrained assortment optimization under the Markov chain–based choice model. Management Sci. 66(2):698–721.Link, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych. General 144(1):114.Crossref, Google 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.Link, Google Scholar
• Feinberg EA, Shwartz A (2012) Handbook of Markov Decision Processes: Methods and Applications, vol. 40 (Springer Science & Business Media, Boston).Google Scholar
• Fildes R, Goodwin P, Lawrence M, Nikolopoulos K (2009) Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply chain planning. Internat. J. Forecasting 25(1):3–23.Crossref, Google Scholar
• Garivier A, Moulines E (2011) On upper-confidence bound policies for switching bandit problems. Internat. Conf. Algorithmic Learn. Theory (Springer, Berlin, Heidelberg), 174–188.Google Scholar
• Goh J, Bayati M, Stefanos A Zenios SS, Moore D (2018) Data uncertainty in Markov chains: Application to cost-effectiveness analyses of medical innovations. Oper. Res. 66(3):697–715.Link, Google Scholar
• Goodman B, Flaxman S (2017) European union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine 38(3):50–57.Crossref, Google Scholar
• Goyal V, Grand-Clement J (2023) Robust Markov decision processes: Beyond rectangularity. Math. Oper. Res. 48(1):203–226.Link, Google Scholar
• Grand-Clément J, Petrik M (2022) On the convex formulations of robust Markov decision processes. Preprint, submitted September 21, https://arxiv.org/abs/2209.10187.Google Scholar
• Grand-Clément J, Chan CW, Goyal V, Escobar G (2022) Robustness of proactive intensive care unit transfer policies. Oper. Res. 71(5):1653–1688.Link, Google Scholar
• Ibanez MR, Clark JR, Huckman RS, Staats BR (2018) Discretionary task ordering: Queue management in radiological services. Management Sci. 64(9):4389–4407.Link, Google Scholar
• Iyengar GN (2005) Robust dynamic programming. Math. Oper. Res. 30(2):257–280.Link, Google Scholar
• Jacq A, Ferret J, Pietquin O, Geist M (2022) Lazy-MDPs: Toward interpretable reinforcement learning by learning when to act. Faliszewski P, Mascardi V, Pelachaud C, Taylor ME, eds. Proc. 21st Internat. Conf. Autonomous Agents Multiagent Systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC), 669–677.Google Scholar
• Kallus N (2017) Recursive partitioning for personalization using observational data. Precup D, Teh YW, eds. Proc. 34th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 70 (PMLR, New York), 1789–1798.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
• Komorowski M, Celi LAOB, Gordon AC, Faisal AA (2018) The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine 24(11):1716–1720.Crossref, Google Scholar
• Kremer M, Moritz B, Siemsen E (2011) Demand forecasting behavior: System neglect and change detection. Management Sci. 57(10):1827–1843.Link, Google Scholar
• Lin W, Kim S-H, Tong J (2021) Does algorithm aversion exist in the field? An empirical analysis of algorithm use determinants in diabetes self-management. Preprint, submitted August 25, https://dx.doi.org/10.2139/ssrn.3891832.Google Scholar
• Logg JM, Minson JA, Moore DA (2019) Algorithm appreciation: People prefer algorithmic to human judgment. Organ. Behav. Human Decision Processes 151:90–103.Crossref, Google Scholar
• Men H, Freund RM, Nguyen NC, Saa-Seoane J, Peraire J (2014) Fabrication-adaptive optimization with an application to photonic crystal design. Oper. Res. 62(2):418–434.Link, Google Scholar
• Meresht VB, De A, Singla A, Gomez-Rodriguez M (2020) Learning to switch between machines and humans. Preprint, submitted February 11, https://arxiv.org/abs/2002.04258.Google Scholar
• Nilim A, El Ghaoui L (2005) Robust control of Markov decision processes with uncertain transition probabilities. Oper. Res. 53(5):780–798.Link, Google Scholar
• Puterman ML (2014) Markov Decision Processes: Discrete Stochastic Dynamic Programming (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Sabaté E (2003) Adherence to Long-Term Therapies: Evidence for Action (World Health Organization, Geneva).Google Scholar
• Shani L, Efroni Y, Mannor S (2019) Exploration conscious reinforcement learning revisited. Chaudhuri K, Salakhutdinov R, eds. Proc. 36th Internat. Conf. Machine Learn., vol. 97 (PMLR, New York), 5680–5689.Google Scholar
• Steimle LN, Denton BT (2017) Markov decision processes for screening and treatment of chronic diseases. Markov Decision Processes in Practice (Springer, New York), 189–222.Crossref, Google Scholar
• Sun J, Zhang DJ, Hu H, Van Mieghem JA (2022) 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
• Wiesemann W, Kuhn D, Rustem B (2013) Robust Markov decision processes. Math. Oper. Res. 38(1):153–183.Link, Google Scholar