Speaker: Dr. Peng, Xuefeng (The University of Hong Kong)

Date & Time: Wed. 24, September 2025, from 10:30 to 12:00 (Beijing Time)

Place: Tongji Building A2101

ABSTRACT

Algorithmic screening plays an increasingly central role in modern recruitment, yet how firms should select model complexity to improve hiring outcomes remains unclear. This study reframes model selection as a strategic design choice shaped by two foundational trade-offs: the classical bias–variance trade-off from statistical learning and a benefit–penalty trade-off that links classification outcomes—true and false positives and negatives—to operational payoffs. We develop a theoretical framework in which firms estimate candidate expected quality using models that may be overspecified (including extraneous features) or underspecified (omitting relevant ones) relative to the ground-truth model. We show that overspecification can improve outcomes in moderately selective environments by amplifying quality dispersion and increasing true positives, while underspecification can reduce false positives and false negatives in high-selectivity settings by acting as a conservative filter. We further identify conditions under which a simpler or more complex model outperforms the other, regardless of the ground-truth specification. Specifically, a simpler model is preferred when misclassification costs are high; a more complex model is preferred when the benefit of accurate identification of qualified candidates is high; and the correctly specified model is optimal when both misclassification costs and the benefit of accurate identification of qualified candidates are high. These findings overturn the classical presumption that correct specification is always optimal and demonstrate that model complexity should be treated as a context-sensitive design choice—one that links statistical estimation to operational payoff.

Key words: Model selection; model misspecification; algorithmic hiring; data analytics; bias–variance trade-off; benefit–penalty trade–off

GUEST BIO:

彭雪丰，现任香港大学商学院博士后研究员，于中国科学技术大学管理学院获得博士学位，博士期间获CSC资助赴多伦多大学罗特曼管理学院联合培养。主要研究方向为运作&营销交叉、AI-driven决策。目前有1篇一作论文POM发表，2篇一作返修于MS和POM，3篇一作在审于MS、MSOM及POM，另有4篇一作或导师外一作发表在 DSS、JORS 和《系统工程理论与实践》等期刊，3篇在研论文。获得首届中国科协青年人才托举博士生专项计划、国家自然科学基金重点项目骨干成员、博士国家奖学金、POMS-China年会最优论文提名奖等