Minimax Regret Robust Screening with Moment Information

References

• Allouah A, Bahamou A, Besbes O (2022) Pricing with samples. Oper. Res. 70(2):1088–1104.Link, Google Scholar
• Allouah A, Bahamou A, Besbes O (2023) Optimal pricing with a single point. Management Sci. 69(10):5866–5882.Link, Google Scholar
• Bandi C, Bertsimas D (2014) Optimal design for multi-item auctions: A robust optimization approach. Math. Oper. Res. 39(4):1012–1038.Link, Google Scholar
• Bergemann D, Schlag K (2011) Robust monopoly pricing. J. Econom. Theory 146(6):2527–2543.Crossref, Google Scholar
• Bergemann D, Schlag KH (2008) Pricing without priors. J. Eur. Econom. Assoc. 6(2–3):560–569.Crossref, Google Scholar
• Bertsimas D, Gupta V, Kallus N (2018) Data-driven robust optimization. Math. Programming 167:235–292.Crossref, Google Scholar
• Briest P, Chawla S, Kleinberg R, Weinberg SM (2010) Pricing randomized allocations. Proc. Twenty-First Annual ACM-SIAM Sympos. Discrete Algorithms (SIAM, Philadelphia), 585–597.Google Scholar
• Caldentey R, Liu Y, Lobel I (2016) Intertemporal pricing under minimax regret. Oper. Res. 65(1):104–129.Link, Google Scholar
• Carrasco V, Luz VF, Kos N, Messner M, Monteiro P, Moreira H (2018) Optimal selling mechanisms under moment conditions. J. Econom. Theory 177:245–279.Crossref, Google Scholar
• Carroll G (2017) Robustness and separation in multidimensional screening. Econometrica 85(2):453–488.Crossref, Google Scholar
• Carroll G (2019) Robustness in mechanism design and contracting. Annual Rev. Econom. 11:139–166.Crossref, Google Scholar
• Chawla S, Malec DL, Sivan B (2010) The power of randomness in Bayesian optimal mechanism design. Proc. 11th ACM Conf. Electronic Commerce (ACM, New York), 149–158.Google Scholar
• Chen H, Hu M, Perakis G (2022) Distribution-free pricing. Manufacturing Service Oper. Management 24(4):1939–1958.Link, Google Scholar
• Chen Z, Hu Z, Wang R (2023) Screening with limited information: A dual perspective. Oper. Res., ePub ahead of print November 23, https://doi.org/10.1287/opre.2022.0016.Link, Google Scholar
• Cole R, Roughgarden T (2014) The sample complexity of revenue maximization. Proc. Forty-Sixth Annual ACM Sympos. Theory Comput. (ACM, New York), 243–252.Google Scholar
• Delage E, Ye Y (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58(3):595–612.Link, Google Scholar
• Dhangwatnotai P, Roughgarden T, Yan Q (2010) Revenue maximization with a single sample. Proc. 11th ACM Conf. Electronic Commerce (ACM, New York), 129–138.Google Scholar
• Elmachtoub AN, Gupta V, Hamilton ML (2021) The value of personalized pricing. Management Sci. 67(10):6055–6070.Link, Google Scholar
• Eren SS, Maglaras C (2010) Monopoly pricing with limited demand information. J. Revenue Pricing Management 9(1–2):23–48.Crossref, Google Scholar
• Fu H, Immorlica N, Lucier B, Strack P (2015) Randomization beats second price as a prior-independent auction. Proc. Sixteenth ACM Conf. Econom. Comput. (ACM, New York), 323.Google Scholar
• Gonczarowski YA, Nisan N (2017) Efficient empirical revenue maximization in single-parameter auction environments. Proc. 49th Annual ACM SIGACT Sympos. Theory Comput. (ACM, New York), 856–868.Google Scholar
• Gravin N, Lu P (2018) Separation in correlation-robust monopolist problem with budget. Proc. Twenty-Ninth Annual ACM-SIAM Sympos. Discrete Algorithms (SIAM, Philadelphia), 2069–2080.Google Scholar
• Guo C, Huang Z, Zhang X (2019) Settling the sample complexity of single-parameter revenue maximization. Proc. 51st Annual ACM SIGACT Sympos. Theory Comput. (ACM, New York), 662–673.Google Scholar
• Hartline J, Karlin A (2007) Profit maximization in mechanism design. Nisan N, Roughgarden T, Tardos E, Vazirani VV, eds. Algorithmic Game Theory (Cambridge Press University, Cambridge, UK), 331–362.Crossref, Google Scholar
• Huang Z, Mansour Y, Roughgarden T (2018) Making the most of your samples. SIAM J. Comput. 47(3):651–674.Crossref, Google Scholar
• il Koçyiğit Ç, Rujeerapaiboon N, Kuhn D (2022) Robust multidimensional pricing: Separation without regret. Math. Programming 196(1):841–874.Crossref, Google Scholar
• Krishna V (2009) Auction Theory (Elsevier, Amsterdam).Google Scholar
• Levi R, Perakis G, Uichanco J (2015) The data-driven newsvendor problem: New bounds and insights. Oper. Res. 63(6):1294–1306.Link, Google Scholar
• Li Y, Lu P, Ye H (2019) Revenue maximization with imprecise distribution. Preprint, submitted March 3, https://arxiv.org/abs/1903.00836.Google Scholar
• Myerson RB (1981) Optimal auction design. Math. Oper. Res. 6(1):58–73.Link, Google Scholar
• Perakis G, Roels G (2008) Regret in the newsvendor model with partial information. Oper. Res. 56(1):188–203.Link, Google Scholar
• Pınar MÇ, Kızılkale C (2017) Robust screening under ambiguity. Math. Programming 163(1–2):273–299.Crossref, Google Scholar
• Riley J, Zeckhauser R (1983) Optimal selling strategies: When to haggle, when to hold firm. Quart. J. Econom. 98(2):267–289.Crossref, Google Scholar
• Shapiro A (2001) On duality theory of conic linear problems. Goberna MÁ, López MA, eds. Semi-Infinite Programming, Nonconvex Optimization and Its Applications, vol. 57 (Springer, Boston), 135–165.Crossref, Google Scholar
• Wiesemann W, Kuhn D, Sim M (2014) Distributionally robust convex optimization. Oper. Res. 62(6):1358–1376.Link, Google Scholar
• Yue J, Chen B, Wang M-C (2006) Expected value of distribution information for the newsvendor problem. Oper. Res. 54(6):1128–1136.Link, Google Scholar
• Zhu Z, Zhang J, Ye Y (2013) Newsvendor optimization with limited distribution information. Optim. Methods Software 28(3):640–667.Crossref, Google Scholar