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

Jiapeng Liu (), Miłosz Kadziński () and Xiuwu Liao ()
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Jiapeng Liu: Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, The People’s Republic of China
Miłosz Kadziński: Faculty of Computing and Telecommunications, Poznan University of Technology, 60-965 Poznań, Poland
Xiuwu Liao: Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, The People’s Republic of China

INFORMS Journal on Computing, 2023, vol. 35, issue 4, 764-785

Abstract: We propose a preference-learning algorithm for uncovering Decision Makers’ (DMs’) contingent evaluation strategies in the context of multiple criteria sorting. We assume the preference information in the form of holistic assignment examples derived from the analysis of alternatives’ performance vectors and textual descriptions. We characterize the decision policies using a mixture of threshold-based, value-driven preference models and associated latent topics. The latter serve as the stimuli underlying the contingency in decision behavior. Such a probabilistic model is constructed by using a flexible and nonparametric Bayesian framework. The proposed method adopts a hierarchical Dirichlet process as the prior so that a group of DMs can share a countably infinite number of contingent models and topics. For all DMs, it automatically identifies the components representing their evaluation strategies adequately. The posterior is summarized by using the Hamiltonian Monte Carlo sampling method. We demonstrate the method’s practical usefulness in a real-world recruitment problem considered by a Chinese IT company. We also compare the approach with counterparts that use a single preference model, implement the parametric framework, or consider each DM’s preferences individually. The results indicate that our approach performs favorably in both interpreting DMs’ contingent decision behavior and recommending decisions on new alternatives. Furthermore, the approach’s performance and robustness are investigated through a computational experiment involving real-world data sets.

Keywords: preference learning; decision analysis; probabilistic sorting; probabilistic topic model; Bayesian nonparametrics; hierarchical Dirichlet process (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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