Conviviality in computational science

计算科学中的“共存性”（Conviviality）

Convivial technology was defined by Ivan Illich in his 1973 book “Tools for conviviality” as technology that supports a convivial society, which is a society that strives to grant each of its members as much agency as is possible without infringing on other members’ agency. 伊凡·伊里奇（Ivan Illich）在其 1973 年的著作《共存工具》（Tools for Conviviality）中将“共存技术”（Convivial technology）定义为支持“共存社会”的技术。所谓共存社会，是指一个致力于在不侵犯他人能动性的前提下，尽可能赋予每一位成员最大能动性的社会。

Conviviality is thus about equality, about the absence of dominance relations. Convivial technology is shaped by its users according to their needs, rather than being controlled by entities such as companies or governments, which then derive power over the user base by exercising control. 因此，共存性关乎平等，关乎消除支配关系。共存技术由用户根据自身需求塑造，而非由公司或政府等实体控制——这些实体往往通过行使控制权来获取对用户群体的权力。

One of Illich’s examples is transportation, with bicycles being convivial whereas railways and cars are not. Cars in particular have turned into what Illich calls a “radical monopoly”: a technology that imposes itself on everyone. 伊里奇举的一个例子是交通工具：自行车是共存的，而铁路和汽车则不然。汽车尤其演变成伊里奇所说的“彻底垄断”（radical monopoly）：一种强加于所有人的技术。

Once a society has adapted its landscape and infrastructure to cars, walking or cycling become insufficient as a means of locomotion for most people, if only because typical distances are now typical distances for driving, not walking. Moreover, the total societal cost for car-based mobility is enormous, if you count in the cost of road construction, traffic accidents, environmental pollution, and much more. 一旦一个社会将其景观和基础设施调整为适应汽车，步行或骑行对大多数人来说就变得不再够用，仅仅因为现在的典型距离是适合驾驶的距离，而非步行的距离。此外，如果算上道路建设、交通事故、环境污染等成本，基于汽车的交通方式所带来的社会总成本是巨大的。

A recent paper entitled “Conviviality for Digital Degrowth”, by Sophie Quinton and Jean-Bernard Stefani, discusses how today’s digital technology is not convivial, and outlines how this could change as part of a transition to a degrowth society. It motivated me to finally write down my personal story, which is about something much more modest: the conviviality of digital technology in scientific research. 索菲·昆顿（Sophie Quinton）和让-伯纳德·斯特凡尼（Jean-Bernard Stefani）最近发表了一篇题为《数字去增长的共存性》（Conviviality for Digital Degrowth）的论文，讨论了当今数字技术为何不具备共存性，并概述了作为向“去增长社会”转型的一部分，这种情况将如何改变。这促使我最终写下了我的个人经历，内容涉及一个更为谦逊的主题：科学研究中数字技术的共存性。

It’s something I have been thinking about for thirty years, even though I wasn’t aware of Illich’s work and terminology until recently. Let me start with the observation that most pre-digital technology in scientific research is convivial. 这是我思考了三十年的问题，尽管直到最近我才了解到伊里奇的著作和术语。让我从一个观察开始：科学研究中大多数前数字时代的技术都是共存的。

Theoretical tools (theories, models, etc.) are developed and evolved completely inside the scientific community and belong to no individual nor any institution. Scientific instruments and experimental setups are designed either by scientists, or explictly for scientists and in close collaboration with them. 理论工具（理论、模型等）完全在科学共同体内开发和演进，不属于任何个人或机构。科学仪器和实验装置要么由科学家设计，要么是专门为科学家设计，并与他们密切合作完成。

Neither kind of tool is controlled by outside entities, with the possible exception of very large instruments such as CERN. Nobody can decide that scientists may no longer use NMR spectrometers, nor that they have to replace all pre-2000 microscopes by new ones. 这两类工具都不受外部实体控制，可能只有像欧洲核子研究中心（CERN）这样的大型仪器除外。没有人能决定科学家不得再使用核磁共振波谱仪，也没有人能强迫他们将所有 2000 年前的显微镜更换为新设备。

This has changed with the adoption of digital tools and the integration of digital technology into scientific instruments. Theoretical tools are now often software, whose complexity makes its behavior inscrutable to its users and puts them at risk of losing their tools to software collapse. 随着数字工具的采用以及数字技术融入科学仪器，这种情况发生了改变。理论工具现在通常是软件，其复杂性使得用户无法洞察其行为，并使他们面临因软件崩溃而失去工具的风险。

Scientific instruments increasingly rely on built-in computers that create exactly the same issues. Finally, digital technology has enabled industrial-scale production of data, e.g. in DNA sequencing, and that technology is itself not convivial either. 科学仪器日益依赖内置计算机，这引发了完全相同的问题。最后，数字技术实现了工业规模的数据生产（例如 DNA 测序），而这种技术本身也不具备共存性。

Conviviality matters for science for multiple reasons. One of them is epistemic: if you want to derive knowledge from your work, you need to know exactly what you are doing, and that includes a detailed understanding of your tools. 共存性对科学至关重要，原因有多个。其中之一是认识论层面的：如果你想从工作中获取知识，你需要确切地知道自己在做什么，这包括对所用工具的详细了解。

Moreover, research is much facilitated if you also have the inverse: the ability to create a tool that does exactly what you want to do. And since science is a collective activity, in which participants critique and build on each other’s work, the understanding of tools needs to be shared inside a discipline. 此外，如果你拥有反向能力——即能够创造出完全符合你需求的工具——研究工作将大为便利。由于科学是一项集体活动，参与者在彼此的工作基础上进行批判和构建，因此对工具的理解需要在学科内部共享。

There have always been limits to this shared understanding, in particular concerning specific physical devices or unique experimental setups, but building shared understanding on a best-effort basis has always been one of the tacit underpinnings of science. 这种共享理解一直存在局限性，特别是在涉及特定物理设备或独特实验装置时，但基于“尽力而为”原则建立共享理解，一直是科学的默会基础之一。

This best effort has been abandoned in the digital era, as I discuss in an analysis of trust issues with scientific software, in which conviviality plays an important role. 在数字时代，这种“尽力而为”的精神已被抛弃。正如我在关于科学软件信任问题的分析中所讨论的那样，共存性在其中扮演了重要角色。

When I started doing computational studies of colloidal suspensions in the late 1980s for my master’s degree and then my PhD, research software was still quite convivial. Like most PhD students, I wrote medium-size Fortran programs, which ran on any computer with a Fortran compiler, from the Atari ST I had at home to the Cray X-MP that I used for production runs. 当我在 20 世纪 80 年代末攻读硕士和博士学位，开始进行胶体悬浮液的计算研究时，研究软件还相当具有共存性。像大多数博士生一样，我编写中等规模的 Fortran 程序，它们可以在任何带有 Fortran 编译器的计算机上运行，从我家里用的 Atari ST 到我用于生产运行的 Cray X-MP。

Other scientists could read and understand my code in a few days, given sufficient motivation, and I know that some actually did, because I received questions from them by e-mail. It was also quite common for PhD students to look at and comment each other’s programs. 只要有足够的动力，其他科学家可以在几天内阅读并理解我的代码，而且我知道确实有人这样做过，因为我收到过他们通过电子邮件发来的问题。博士生之间互相查看并评论对方的程序也是非常普遍的。

Publishing software was still exceptional, but publication venues did exist, and I ended up publishing the main code library underlying my work in low-Reynolds-number hydrodynamics in 1993. 发布软件在当时仍属例外，但确实存在发布渠道。最终，我在 1993 年发布了支撑我低雷诺数流体动力学研究的主要代码库。

Unfortunately I didn’t publish, nor properly archive, the small bits of code that did the actual computations for concrete specific systems, and that is why the results of my papers aren’t reproducible any more. But the library still works exactly as it did in 1993, and still finds new users. 遗憾的是，我没有发布，也没有妥善归档那些用于具体系统实际计算的小段代码，这就是为什么我论文的结果现在已无法复现。但那个代码库至今仍能像 1993 年那样正常工作，并且仍在吸引新的用户。

When I moved on to a postdoc in another field, biomolecular simulation, I discovered a very different world. There were only three big simulation programs that everybody worked with: AMBER, CHARMM, and GROMOS. 当我转到另一个领域——生物分子模拟——做博士后时，我发现了一个截然不同的世界。当时只有三个大家都在使用的大型模拟程序：AMBER、CHARMM 和 GROMOS。

Only a very small number of researchers understood them in detail and could modify them. Everyone else computed whatever the software allowed them to compute, rather than what they actually wanted to compute. 只有极少数研究人员能详细理解并修改它们。其他人只能计算软件允许他们计算的内容，而不是他们真正想要计算的内容。

But even the correct use of the software was a challenge if you weren’t in personal contact with the development teams, as documentation tended to be incomplete and outdated. Biomolecular simulation software was clearly not convivial, an observation that I attributed to the complexity of the underlying theoretical models. 但即使是正确使用这些软件也是一项挑战，除非你与开发团队有私人联系，因为文档往往不完整且过时。生物分子模拟软件显然不具备共存性，我将这一观察结果归因于底层理论模型的复杂性。

I was also finding out about the politics favoring the concentration of power over software, but I didn’t make the connection at the time. The objects of biomolecular simulations, proteins and… 我也开始了解到那些倾向于将软件权力集中的政治因素，但当时我并没有将两者联系起来。生物分子模拟的对象，蛋白质和……