Designing Pareto-Optimal Systems for Complex Selection Decisions
提出一种分析方法，为多岗位、多申请者的复杂选拔情境设计帕累托最优预测组合，帮助人事决策者在选拔质量与多样性之间找到最佳平衡。
The current practice of personnel selection faces the challenge of reconciling the often competing goals of obtaining a high-quality as well as a diverse workforce. To address this challenge for simple selections, De Corte, Sackett, and Lievens (2011) propose using Pareto-optimal predictor composites. These composites yield trade-offs between selection quality and selection diversity levels that cannot be improved simultaneously by any other composite. The current article describes how these Pareto-optimal composites and trade-offs can be developed in complex selection situations, which are characterized by vacancies for at least two different positions and applicants that apply either for one or several of these open positions simultaneously. An analytic method that estimates the selection quality and adverse impact of these complex selection decisions is presented and implemented in a multi-objective optimization program, so as to obtain Pareto-optimal predictor composites. The resulting decision aid is subsequently used to illustrate how it can lead to substantive contributions when considering the quality-diversity dilemma in a complex selection context.