Designing a Data Science simulation with MERITS: A Primer
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912376281890816 |
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| author | Elliott, Corrine F Duncan, James PC Tang, Tiffany M Behr, Merle Kumbier, Karl Yu, Bin |
| author_facet | Elliott, Corrine F Duncan, James PC Tang, Tiffany M Behr, Merle Kumbier, Karl Yu, Bin |
| contents | 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. (Modularity and Efficiency support the computability of a study, encouraging clean and flexible implementation. Realism and Stability address the conceptualization of the research problem: How well does a study predict reality, such that its conclusions generalize to new data/contexts? Finally, Intuitiveness and Transparency 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08971 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Designing a Data Science simulation with MERITS: A Primer Elliott, Corrine F Duncan, James PC Tang, Tiffany M Behr, Merle Kumbier, Karl Yu, Bin Computation 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. (Modularity and Efficiency support the computability of a study, encouraging clean and flexible implementation. Realism and Stability address the conceptualization of the research problem: How well does a study predict reality, such that its conclusions generalize to new data/contexts? Finally, Intuitiveness and Transparency 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. |
| title | Designing a Data Science simulation with MERITS: A Primer |
| topic | Computation |
| url | https://arxiv.org/abs/2403.08971 |