真理、证明和复制：没有代码的反击
Truth, Proof, and Reproducibility: There's no counter-attack for the codeless
摘要：
目前许多研究团体对再现性的关注可以追溯到对科学实验和观察的物理细节的经验再现性的高度重视。例如，17世纪科学家罗伯特·波义耳（Robert Boyle）对他的真空泵实验的详细描述通常被认为是科学实践的基石，是理想的可重复性。维多利亚·斯托登声称，计算机是波义耳泵的模拟物--另一种需要详细描述它如何产生结果的科学仪器。在Boyle的手写笔记的地方，我们现在期待开源编程语言的代码可以让其他人复制和扩展计算实验。在本文中，我们表明，至少从欧几里得开始，在数学证明的生产中，有另一个谱系的再现性。证明有一个独特的性质，那就是必须是可复制的，并且是数学科学的基石。然而，现代数学科学家的任务已经从黑板数学家的任务（在黑板数学家的任务中，证明技巧占主导地位）转移到了一个更接近实验科学家的科学工作流程。现代数学中的证明是什么？而且，如果在其他领域无法证明，那么在计算实验环境中，什么是应有的科学勤奋？我们如何在不确定性的背景下衡量真理？采用两个数学家之间的Lakatosian熟悉猜想的方式，我们研究如何证明通知我们的计算统计调查的实践。我们建议，重新定位数学科学是必要的，使其重现性可以很容易地评估。
英文摘要：
Current concerns about reproducibility in many research communities can be traced back to a high value placed on empirical reproducibility of the physical details of scientific experiments and observations. For example, the detailed descriptions by 17th century scientist Robert Boyle of his vacuum pump experiments are often held to be the ideal of reproducibility as a cornerstone of scientific practice. Victoria Stodden has claimed that the computer is an analog for Boyle's pump -- another kind of scientific instrument that needs detailed descriptions of how it generates results. In the place of Boyle's hand-written notes, we now expect code in open source programming languages to be available to enable others to reproduce and extend computational experiments. In this paper we show that there is another genealogy for reproducibility, starting at least from Euclid, in the production of proofs in mathematics. Proofs have a distinctive quality of being necessarily reproducible, and are the cornerstone of mathematical science. However, the task of the modern mathematical scientist has drifted from that of blackboard rhetorician, where the craft of proof reigned, to a scientific workflow that now more closely resembles that of an experimental scientist. So, what is proof in modern mathematics? And, if proof is unattainable in other fields, what is due scientific diligence in a computational experimental environment? How do we measure truth in the context of uncertainty? Adopting a manner of Lakatosian conversant conjecture between two mathematicians, we examine how proof informs our practice of computational statistical inquiry. We propose that a reorientation of mathematical science is necessary so that its reproducibility can be readily assessed.
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