了解你工具的局限性
了解你工具的局限性
教育不是你记住了多少,甚至也不是你知道了多少。而是能够区分你所知道的和不知道的。
阿纳托尔·法朗士
数学教育(和研究论文)自然倾向于关注那些有效的方法。但同样重要的是,要知道你所拥有的工具在什么时候不起作用,这样你就不会在从一开始就注定失败的战略上浪费时间,而是去寻找新的工具来解决问题(或者寻找新的问题)。
因此,了解一系列反例或易于分析的模型情境,与了解你的工具能够处理哪些障碍以及哪些障碍它无法解决同样重要。同样值得了解的是,在什么情况下你所选择的工具可以被其他方法替代,以及每种方法的相对优缺点是什么。
如果你把你最喜欢的工具之一视为某种”魔杖”,它能神秘地为你解决问题,而你无法通过其他方式获得或理解解决方案,这表明你需要更好地理解你的工具(及其局限性)。
如果你认为你刚刚使用了你最喜欢的工具之一来证明某个令人印象深刻的结果(例如某个未解决的主要问题的证明),这一点尤其值得牢记。当这种情况发生时,你应该尝试看看是否有办法重写你的论证,使得该工具不被使用。如果你很好地理解你的工具,那么你应该能够做到这一点,尽管可能会以显著延长和混乱论证为代价。但如果你完全看不到不使用该工具的任何前进方式,那么你应该将此视为一个警告信号,表明你可能没有正确使用该工具。
虽然反直觉,但在撰写使用某个工具的论文时,也应该讨论该工具的已知局限性。乍一看,这似乎削弱了论文的价值;但它有助于准确识别哪些相关类别的问题可能也适合尝试使用该工具,并且通过展示对此类局限性的认识,在推广该工具的优势或将其与竞争方法进行比较时,可以获得更多的可信度和客观性。它还有助于确定新突破需要从哪个方向来超越仅使用该工具所能达到的成果。最后,如果向读者隐瞒工具的局限性,而这项工作又很有趣,最终一些后续工作会遇到同样的局限性,并在他们的工作中讨论这些问题;这会使你的先前论文看起来完全忽视了这些问题。
另请参阅”了解其他数学家工具的力量”和”学习并重新学习你的领域”。
Learn the limitations of your tools
An education isn’t how much you have committed to memory, or even how much you know. It’s being able to differentiate between what you do know and what you don’t.
Anatole France
Mathematical education (and research papers) tends to focus, naturally enough, on techniques that work. But it is equally important to know when the tools you have don’t work, so that you don’t waste time on a strategy which is doomed from the start, and instead go hunting for new tools to solve the problem (or hunt for a new problem).
Thus, knowing a library of counterexamples, or easily analysed model situations, is very important, as well as knowing the type of obstructions that your tool can deal with, and which ones it has no hope of resolving. Also it is worth knowing under what circumstances your tool of choice can be substituted by other methods, and what the comparative advantages and disadvantages of each approach is.
If you view one of your favorite tools as some sort of “magic wand” which mysteriously solves problems for you, with no other way for you to obtain or comprehend the solution, this is a sign that you need to understand your tool (and its limitations) much better.
This point is particularly worth keeping in mind if you think that you have just used one of your favorite tools to prove some impressive result (such as the proof of a major unsolved problem). When this happens, you should try to see if there is a way to rewrite your argument in such a way that the tool is not used. If you understand your tool well, then you should be able to do this, though probably at the cost of making the argument significantly longer and messier. But if you do not see any way to proceed without the tool at all, then you should take this as a warning sign that you might not be using the tool properly.
While unintuitive, one should also discuss the known limitations of a tool when writing a paper that uses that tool. At first glance, this may seem to undercut the value of the paper; but it helps identify precisely what classes of related problems might also be suitable for trying the tool on, and also by demonstrating awareness of such limitations, one gains more credibility and appearance of objectivity when promoting the strengths of the tool, or comparing it to competing methods. It also helps identify the direction in which new breakthroughs will need to come from in order to go beyond what can be done with that tool alone. Finally, if one conceals the limitations of a tool from readers, and the work is interesting, eventually some followup work will run into the same limitations, and discuss them in their work; and it will appear that such issues were totally overlooked in your previous paper.
See also “[learn the power of other mathematician’s tools](learn the power of other mathematician’s tools)” and “[learn and relearn your field](learn and relearn your field)”.