Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach

Published Online:https://doi.org/10.1287/ijoc.2023.1292

References

  • Aggarwal M, Fallah Tehrani A (2019) Modelling human decision behaviour with preference learning. INFORMS J. Comput. 31(2):318–334.LinkGoogle Scholar
  • Alvarez PA, Ishizaka A, Martinez L (2021) Multiple-criteria decision-making sorting methods: A survey. Expert Systems Appl. 183:115368.CrossrefGoogle Scholar
  • Ansari A, Li Y, Zhang JZ (2018) Probabilistic topic model for hybrid recommender systems: A stochastic variational Bayesian approach. Marketing Sci. 37(6):987–1008.LinkGoogle Scholar
  • Aouad A, Farias V, Levi R (2021) Assortment optimization under consider-then-choose choice models. Management Sci. 67(6):3368–3386.LinkGoogle Scholar
  • Beach LR, Mitchell TR (1978) A contingency model for the selection of decision strategies. Acad. Management Rev. 3(3):439–449.CrossrefGoogle Scholar
  • Bernstein F, Modaresi S, Sauré D (2019) A dynamic clustering approach to data-driven assortment personalization. Management Sci. 65(5):2095–2115.AbstractGoogle Scholar
  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learning Res. 3(Jan):993–1022.Google Scholar
  • Bordalo P, Gennaioli N, Shleifer A (2015) Salience theory of judicial decisions. J. Legal Stud. 44(S1):S7–S33.CrossrefGoogle Scholar
  • Boughanmi K, Ansari A (2021) Dynamics of musical success: A machine learning approach for multimedia data fusion. J. Marketing Res. 58(6):1034–1057.CrossrefGoogle Scholar
  • Choquet G (1954) Theory of capacities. Ann. Inst. Fourier Grenoble 5:131–295.CrossrefGoogle Scholar
  • Cinelli M, Kadziński M, Gonzalez M, Słowiński R (2020) How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega 96:102261.CrossrefGoogle Scholar
  • Cinelli M, Kadziński M, Miebs G, Gonzalez M, Słowiński R (2022) Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system. Eur. J. Oper. Res. 302(2):633–651.CrossrefGoogle Scholar
  • Corrente S, Greco S, Kadziński M, Słowiński R (2013) Robust ordinal regression in preference learning and ranking. Machine Learning 93(2–3):381–422.CrossrefGoogle Scholar
  • Del Vasto-Terrientes L, Fernández-Cavia J, Huertas A, Moreno A, Valls A (2015) Official tourist destination websites: Hierarchical analysis and assessment with ELECTRE-III-H. Tourism Management Perspect. 15:16–28.CrossrefGoogle Scholar
  • Delquié P (1993) Inconsistent trade-offs between attributes: New evidence in preference assessment biases. Management Sci. 39(11):1382–1395.LinkGoogle Scholar
  • Dietrich F, List C (2016) Reason-based choice and context-dependence: An explanatory framework. Econom. Philos. 32(2):175–229.CrossrefGoogle Scholar
  • Doumpos M, Zopounidis C (2002) Multicriteria Decision Aid Classification Methods (Kluwer Academic Publishers, Dordrecht, Netherlands).Google Scholar
  • Doumpos M, Zopounidis C (2019) Preference Disaggregation for Multicriteria Decision Aiding: An Overview and Perspectives (Springer International Publishing, Cham, Switzerland), 115–130.Google Scholar
  • Einhorn HJ (1971) Use of nonlinear, noncompensatory models as a function of task and amount of information. Organ. Behav. Human Performance 6(1):1–27.CrossrefGoogle Scholar
  • Farias VF, Jagabathula S, Shah D (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.LinkGoogle Scholar
  • Ferguson TS (1973) A Bayesian analysis of some nonparametric problems. Ann. Statist. 1(2):209–230.CrossrefGoogle Scholar
  • Fishburn PC, Kochenberger GA (1979) Two-piece von Neumann-Morgenstern utility functions. Decision Sci. 10(4):503–518.CrossrefGoogle Scholar
  • Gelman A, Carlin JB, Stern HS, Rubin DB (1995) Bayesian Data Analysis (Chapman and Hall/CRC, London).CrossrefGoogle Scholar
  • Ghaderi M, Kadziński M (2021) Incorporating uncovered structural patterns in value functions construction. Omega 99:102203.CrossrefGoogle Scholar
  • Greco S, Ehrgott M, Figueira J (2016) Multiple Criteria Decision Analysis: State of the Art Surveys, International Series in Operations Research & Management Science (Springer, New York).CrossrefGoogle Scholar
  • Greco S, Mousseau V, Słowiński R (2010) Multiple criteria sorting with a set of additive value functions. Eur. J. Oper. Res. 207(3):1455–1470.CrossrefGoogle Scholar
  • Greco S, Mousseau V, Słowiński R (2014) Robust ordinal regression for value functions handling interacting criteria. Eur. J. Oper. Res. 239(3):711–730.CrossrefGoogle Scholar
  • Gutiérrez PA, Perez-Ortiz M, Sanchez-Monedero J, Fernandez-Navarro F, Hervas-Martinez C (2016) Ordinal regression methods: Survey and experimental study. IEEE Trans. Knowledge Data Engrg. 28(1):127–146.CrossrefGoogle Scholar
  • He J, Fang X, Liu H, Li X (2019) Mobile app recommendation: An involvement-enhanced approach. MIS Quart. 43(3):827–849.CrossrefGoogle Scholar
  • Hoffman MD, Gelman A (2014) The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. J. Machine Learning Res. 15(1):1593–1623.Google Scholar
  • Honhon D, Jonnalagedda S, Pan XA (2012) Optimal algorithms for assortment selection under ranking-based consumer choice models. Manufacturing Service Oper. Management 14(2):279–289.LinkGoogle Scholar
  • Jacobs B, Fok D, Donkers B (2021) Understanding large-scale dynamic purchase behavior. Marketing Sci. 40(5):844–870.LinkGoogle Scholar
  • Jacobs BJ, Donkers B, Fok D (2016) Model-based purchase predictions for large assortments. Marketing Sci. 35(3):389–404.LinkGoogle Scholar
  • Jagabathula S, Rusmevichientong P (2017) A nonparametric joint assortment and price choice model. Management Sci. 63(9):3128–3145.LinkGoogle Scholar
  • Jagabathula S, Vulcano G (2018) A partial-order-based model to estimate individual preferences using panel data. Management Sci. 64(4):1609–1628.LinkGoogle Scholar
  • Kadziński M, Ciomek K (2021) Active learning strategies for interactive elicitation of assignment examples for threshold-based multiple criteria sorting. Eur. J. Oper. Res. 293(2):658–680.CrossrefGoogle Scholar
  • Kadziński M, Ghaderi M, Dabrowski M (2020) Contingent preference disaggregation model for multiple criteria sorting problem. Eur. J. Oper. Res. 281(2):369–387.CrossrefGoogle Scholar
  • Kadziński M, Ghaderi M, Wasikowski J, Agell N (2017) Expressiveness and robustness measures for the evaluation of an additive value function in multiple criteria preference disaggregation methods: An experimental analysis. Comput. Oper. Res. 87:146–164.CrossrefGoogle Scholar
  • Kadziński M, Stamenković M, Uniejewski M (2022) Stepwise benchmarking for multiple criteria sorting. Omega 108:102579.CrossrefGoogle Scholar
  • Kahneman D (2011) Thinking, Fast and Slow (Farrar, Straus and Giroux, New York).Google Scholar
  • Kawaguchi K, Uetake K, Watanabe Y (2021) Designing context-based marketing: Product recommendations under time pressure. Management Sci. 67(9):5642–5659.LinkGoogle Scholar
  • Keeney RL, Raiffa H (1976) Decisions with Multiple Objectives: Preferences and Value Tradeoffs (J. Wiley, New York).Google Scholar
  • Koller D, Friedman N (2009) Probabilistic Graphical Models: Principles and Techniques (MIT Press, Cambridge, MA).Google Scholar
  • Lex E, Kowald D, Seitlinger P, Tran TNT, Felfernig A, Schedl M (2021) Psychology-Informed Recommender Systems (Now Publishers, Boston).CrossrefGoogle Scholar
  • Lichtenstein S, Slovic P (2006) The Construction of Preference (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Liou JJ, Yen L, Tzeng G-H (2010) Using decision rules to achieve mass customization of airline services. Eur. J. Oper. Res. 205(3):680–686.CrossrefGoogle Scholar
  • Liu J, Kadziński M, Liao X (2023) Data and codes for “Modeling contingent decision behavior: A Bayesian nonparametric preference learning approach”, Version v2023.0206 URL http://dx.doi.org/10.5281/zenodo.7608750, https://github.com/INFORMSJoC/2021.0328.Google Scholar
  • Liu J, Kadziński M, Liao X, Mao X (2021b) Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria. INFORMS J. Comput. 33(2):586–606.AbstractGoogle Scholar
  • Liu J, Liao X, Kadziński M, Słowiński R (2019) Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria. Eur. J. Oper. Res. 276(3):1071–1089.CrossrefGoogle Scholar
  • Liu J, Toubia O, Hill S (2021a) Content-based model of web search behavior: An application to TV show search. Management Sci. 67(10):6378–6398.LinkGoogle Scholar
  • Loewenstein G, Lerner JS (2003) The role of affect in decision making. Davidson R, Goldsmith H, Scherer K, eds. Handbook of Affective Science (Oxford University Press, Oxford, UK), 619–642.Google Scholar
  • Montibeller G, von Winterfeldt D (2015) Cognitive and motivational biases in decision and risk analysis. Risk Anal. 35(7):1230–1251.CrossrefGoogle Scholar
  • Payne JW, Bettman JR, Coupey E, Johnson EJ (1992) A constructive process view of decision making: Multiple strategies in judgment and choice. Acta Psych. 80(1–3):107–141.CrossrefGoogle Scholar
  • Payne JW, Bettman JR, Johnson EJ (1993) The Adaptive Decision Maker (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Peterson JC, Bourgin DD, Agrawal M, Reichman D, Griffiths TL (2021) Using large-scale experiments and machine learning to discover theories of human decision-making. Science 372(6547):1209–1214.CrossrefGoogle Scholar
  • Roy B (1996) Multicriteria Methodology for Decision Aiding, Nonconvex Optimization and its Applications, vol. 12 (Kluwer Academic Publishers, Dordrecht, Netherlands).CrossrefGoogle Scholar
  • Sethuraman J (1994) A constructive definition of Dirichlet priors. Statist. Sinica 4(2):639–650.Google Scholar
  • Settles B (2012) Active Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning (Morgan & Claypool, Kentfield, CA).Google Scholar
  • Simon HA (1966) Theories of decision-making in economics and behavioural science. Surveys of Economic Theory (Palgrave Macmillan, London), 1–28.CrossrefGoogle Scholar
  • Stanovich KE, West RF (1998) Individual differences in rational thought. J. Experiment. Psych. General 127(2):161–188.CrossrefGoogle Scholar
  • Stillwell WG, Von Winterfeldt D, John RS (1987) Comparing hierarchical and nonhierarchical weighting methods for eliciting multiattribute value models. Management Sci. 33(4):442–450.LinkGoogle Scholar
  • Teh Y, Jordan M, Beal M, Blei D (2004) Sharing clusters among related groups: Hierarchical Dirichlet processes. Adv. Neural Inform. Processing Systems 17:1385–1392.Google Scholar
  • Teh YW, Jordan MI, Beal MJ, Blei DM (2006) Hierarchical Dirichlet processes. J. Amer. Statist. Assoc. 101(476):1566–1581.CrossrefGoogle Scholar
  • Tehrani AF, Cheng W, Dembczyński K, Hüllermeier E (2012) Learning monotone nonlinear models using the Choquet integral. Machine Learning 89(1–2):183–211.CrossrefGoogle Scholar
  • Thaler R (1980) Toward a positive theory of consumer choice. J. Econom. Behav. Organ. 1(1):39–60.CrossrefGoogle Scholar
  • Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–1131.CrossrefGoogle Scholar
  • Tversky A, Simonson I (1993) Context-dependent preferences. Management Sci. 39(10):1179–1189.LinkGoogle 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.