The Rashomon effect -- the existence of multiple, distinct models that achieve nearly equivalent predictive performance -- has emerged as a fundamental phenomenon in modern machine learning and statistics. In this paper, we explore the causes underlying the Rashomon effect, organizing them into three categories: statistical sources arising from finite samples and noise in the data-generating process; structural sources arising from non-convexity of optimization objectives and unobserved variables that create fundamental non-identifiability; and procedural sources arising from limitations of optimization algorithms and deliberate restrictions to suboptimal model classes. We synthesize insights from machine learning, statistics, and optimization literature to provide a unified framework for understanding why the multiplicity of good models arises. A key distinction emerges: statistical multiplicity diminishes with more data, structural multiplicity persists asymptotically and cannot be resolved without different data or additional assumptions, and procedural multiplicity reflects choices made by practitioners. Beyond characterizing causes, we discuss both the challenges and opportunities presented by the Rashomon effect, including implications for inference, interpretability, fairness, and decision-making under uncertainty.

翻译：罗夏效应——即存在多个不同模型却能达到近乎同等预测性能的现象——已成为现代机器学习和统计学中的一个基本现象。本文深入探讨了罗夏效应的成因，并将其归纳为三类：源于有限样本和数据生成过程中噪声的统计性成因；源于优化目标非凸性及未观测变量导致根本不可识别性的结构性成因；以及源于优化算法局限性和人为限定于次优模型类别的过程性成因。我们综合机器学习、统计学和优化理论领域的见解，构建了一个理解优质模型多重性成因的统一框架。一个关键区别在于：统计多重性随数据量增加而减弱，结构多重性在渐近意义上持续存在且无法通过不同数据或额外假设消除，而过程多重性则反映了实践者的选择。除成因分析外，我们还探讨了罗夏效应带来的挑战与机遇，包括其对统计推断、可解释性、公平性及不确定性决策的影响。