報告主題：Modelling Contingent Decision Behavior: A Bayesian Nonparametric Preference Learning Approach

報告時間：1月6日（周四）晚上18：00

報告地點：騰訊會議395-263-942

主持人：吳勇 博士

報告人：劉佳鵬 博士

報告簡介：

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 DM's 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. The experimental results indicate that our approach performs favorably in both interpreting DM's contingent decision behavior and recommending decisions on new alternatives.

報告人簡介：

 劉佳鵬博士，西安交通大學管理學院信息管理與電子商務系、智能決策與機器學習研究中心副教授、博士生導師。研究方向包括決策分析、機器學習、貝葉斯方法、大數據模型。主持過國家自然科學基金青年項目、面上項目以及博士後科學基金項目。研究工作發表在INFORMS Journal on Computing、European Journal of Operational Research、Omega、系統工程理論與實踐等國内外重要學術期刊上。