Utility Preference Robust Optimization with Moment-Type Information Structure

References

• Armbruster B, Delage E (2015) Decision making under uncertainty when preference information is incomplete. Management Sci. 61(1):111–128.Link, Google Scholar
• Bertsimas D, O’Hair A (2013) Learning preferences under noise and loss aversion: An optimization approach. Oper. Res. 61(5):1190–1199.Link, Google Scholar
• Brown DB, Sim M (2009) Satisficing measures for analysis of risky positions. Management Sci. 55(1):71–84.Link, Google Scholar
• Brown DB, Giorgi ED, Sim M (2012) Aspirational preferences and their representation by risk measures. Management Sci. 58(11):2095–2113.Link, Google Scholar
• Clemen RT, Reilly T (2013) Making Hard Decisions with DecisionTools (Cengage Learn, New York).Google Scholar
• Cucker F, Zhou DX (2007) Learning Theory: An Approximation Theory Viewpoint, vol. 24 (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Delage E, Li JYM (2018) Minimizing risk exposure when the choice of a risk measure is ambiguous. Management Sci. 64(1):327–344.Link, Google Scholar
• Delage E, Guo S, Xu H (2022) Shortfall risk models when information on loss function is incomplete. Oper. Res. 70(6):3035–3628.Link, Google Scholar
• Farquhar PH (1984) State of the art—Utility assessment methods. Management Sci. 30(11):1283–1300.Link, Google Scholar
• Föllmer H, Schied A (2002) Convex measures of risk and trading constraints. Finance Stoch. 6(4):429–447.Crossref, Google Scholar
• Galaabaatar T, Karni E (2013) Subjective expected utility with incomplete preferences. Econometrica 81(1):255–284.Crossref, Google Scholar
• Gibbs AL, Su FE (2002) On choosing and bounding probability metrics. Internat. Stat. Rev. 70(3):419–435.Crossref, Google Scholar
• Guo S, Xu H (2021) Utility preference robust optimization with moment-type information structure. Optim. Online. Accessed January 27, 2021, http://www.optimization-online.org/DB_HTML/2021/01/8233.html.Google Scholar
• Haskell WB, Fu L, Dessouky M (2016) Ambiguity in risk preferences in robust stochastic optimization. Eur. J. Oper. Res. 254(1):214–225.Crossref, Google Scholar
• Haskell WB, Huang W, Xu H (2018) Preference elicitation and robust optimization with multi-attribute quasi-concave choice functions. Preprint, submitted May 17, https://arxiv.org/abs/1805.06632.Google Scholar
• Haskell WB, Xu H, Huang W (2022) Preference robust optimization for choice functions on the space of cdfs. SIAM. J. Optim. 32(2):1446–1470.Crossref, Google Scholar
• Hu J, Mehrotra S (2012) Robust decision making using a risk-averse utility set. Optim. Online. Accessed March 29, 2012, http://www.optimization-online.org/DB_HTML/2012/03/3412.html.Google Scholar
• Hu J, Mehrotra S (2015) Robust decision making over a set of random targets or risk-averse utilities with an application to portfolio optimization. IIE Trans. 47(4):358–372.Crossref, Google Scholar
• Hu J, Stepanyan G (2017) Optimization with reference-based robust preference constraints. SIAM J. Optim. 27(4):2230–2257.Crossref, Google Scholar
• Hu J, Bansal M, Mehrotra S (2018) Robust decision making using a general utility set. Eur. J. Oper. Res. 269(2):699–714.Crossref, Google Scholar
• Hu J, Zhang D, Xu H, Zhang S (2022) Distributionally preference robust optimization in multi-attribute decision making. Preprint, submitted June 9, https://arxiv.org/abs/2206.04491.Google Scholar
• Johnson SG (2014) The nlopt nonlinear-optimization package [software]. Accessed May 21, 2014, http://github.com/stevengj/nlopt.Google Scholar
• Karmarkar US (1978) Subjectively weighted utility: A descriptive extension of the expected utility model. Organ. Behav. Human Perform. 21(1):61–72.Crossref, Google Scholar
• Maccheroni F (2002) Maxmin under risk. Econom. Theory 19(4):823–831.Crossref, Google Scholar
• Powell MJ (1994) A Direct Search Optimization Method that Models the Objective and Constraint Functions by Linear Interpolation. Advances in Optimization and Numerical Analysis (Spinger, Netherlands), 51–67.Google Scholar
• Robinson SM (1975) An application of error bounds for convex programming in a linear space. SIAM J. Control. 13(2):271–273.Crossref, Google Scholar
• Römisch W (2003) Stability of stochastic programming problems. Handb. Oper. Res. Management Sci. 10:483–554.Google Scholar
• Sauré D, Vielma JP (2019) Ellipsoidal methods for adaptive choice-based conjoint analysis. Oper. Res. 67(2):315–338.Abstract, Google Scholar
• Shapiro A, Xu H (2008) Stochastic mathematical programs with equilibrium constraints, modelling and sample average approximation. Optimization. 57(3):395–418.Crossref, Google Scholar
• Toubia O, Hauser J, Garcia R (2007) Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Sci. 26(5):596–610.Link, Google Scholar
• Toubia O, Hauser JR, Simester DI (2004) Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. 41(1):116–131.Crossref, Google Scholar
• Vayanos P, McElfresh D, Ye Y, Dickerson J, Rice E (2020) Robust active preference elicitation. Preprint, submitted March 4, https://arxiv.org/abs/2003.01899.Google Scholar
• Von Neumann J, Morgenstern O (1947) Theory of Games and Economic Behavior (Princeton University Press, Princeton, NJ).Google Scholar
• Wang W, Xu H (2020) Robust spectral risk optimization when information on risk spectrum is incomplete. SIAM J. Optim. 30(4):3198–3229.Crossref, Google Scholar
• Weber M (1987) Decision making with incomplete information. Eur. J. Oper. Res. 28(1):44–57.Crossref, Google Scholar