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【明理讲堂2021年第64期】12-6西安交通大学刘佳鹏副教授: Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference Learning Approach


腾讯会议号:117 564 951

报告人:西安交通大学刘佳鹏 副教授


刘佳鹏博士,西安交通大学管理学院智能决策与机器学习研究中心副教授、博士生导师。目前的研究方向包括:决策分析、机器学习、贝叶斯方法、大数据模型。主持过国家自然科学基金青年项目及面上项目、国家重点研发计划项目子课题以及博士后科学基金项目。研究成果发表在INFORMS Journal on Computing、European Journal of Operational Research、Omega、Knowledge-based Systems、系统工程理论与实践、系统工程学报等国内外重要学术期刊。现担任中国优选法统筹法与经济数学研究会智能决策与博弈分会理事、中国系统工程学会数据科学与知识系统工程专委会委员。


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, providing a transparent and interpretable way to explore and understand DMs’ contingent preferences. Such a probabilistic model is constructed using a flexible and nonparametric Bayesian framework. The proposed method adopts a hierarchical Dirichlet process 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 using the Hamiltonian Monte Carlo sampling method. We demonstrate the method’s practical usefulness on a real-world recruitment problem considered by a Chinese IT company. We discuss the contingent models and topics and illustrate their employment for classifying the job applicants. We also compare the approach with counterparts that use just a single preference model, implement the parametric framework, or consider each DM’s preferences individually.