Designing a Data Science Simulation with MERITS: A Primer
本文提出模拟研究应满足的六个MERITS标准（模块化、效率、现实性、稳定性、直观性、透明性），并给出设计框架和指南，帮助数据科学从业者构建高质量、可信的模拟研究。
Simulations play a crucial role in the modern scientific process. Yet despite (or due to) this ubiquity, the Data Science community shares neither a comprehensive definition for a “high-quality” study nor a consolidated guide to designing one. Inspired by the Predictability-Computability-Stability (PCS) framework for ‘veridical’ Data Science, we propose six MERITS that a simulation study should satisfy. ( M odularity and E fficiency support the computability of a study, encouraging clean and flexible implementation. R ealism and S tability address the conceptualization of the research problem: How well does a study predict reality, such that its conclusions generalize to new data/contexts? Finally, I ntuitiveness and T ransparency encourage good communication and trustworthiness of study design and results.) Drawing an analogy between simulation and cooking, we moreover offer (a) a conceptual framework for thinking about the anatomy of a simulation ‘recipe’; (b) a baker’s dozen in guidelines to aid the Data Science practitioner in designing one; and (c) a case study demonstrating the practical utility of our framework by using it to autopsy a preexisting simulation study. With this “PCS primer” for high-quality Data Science simulation, we seek to distill and enrich the best practices of simulation across disciplines into a cohesive recipe for trustworthy, veridical Data Science.