学术研究中的激励措施
Incentives in Academic Research

原始链接: https://www.msoos.org/2026/10/incentives-in-academic-research/

作者认为,学术研究的激励机制已经偏离其核心目标:推进真理、培养严谨的研究者和维护科学诚信。发表、晋升和名望可能形成强大的激励,使人们倾向于保护错误或误导性的工作,而不是承认缺陷、撤回主张或告知科学界。 作者对近期一些研究者抵制批评的实例感到担忧,并将他们与自己职业生涯早期的经历作对比:那时发现错误会令人尴尬,却也会立即促使人们加以纠正。他们担心,新一代博士生正在学习一种文化,在这种文化中,精确、好奇心和良好的科学操守不如职业成功重要。 核心信息是,研究应由诚实、责任和集体理解来指导,而不是由个人声誉来支配。机构必须奖励透明、谨慎评估、纠正错误以及对未来研究者的负责任培养;否则 declining standards 将继续损害科学和下一代。

一则关于学术研究激励机制的 Hacker News 讨论探讨了如何处理存在缺陷但并非造假的论文。评论者 jruohonen 认为,问题更多源于系统性激励,而非研究人员为了保护个人职业地位;与此同时,论文仍可能继续发表,编辑也会鼓励发表相关后续研究,例如重复实验、勘误和评论。 Zero_k 则反驳称,有时仍有必要进行更正、撤稿或发布编辑注记。揭示论文重大缺陷的重复实验可能难以通过顶级期刊的同行评审,也可能得不到足够关注;而发表后续研究还可能无意中使原本粗疏的工作再次获得发表和引用机会。这场讨论凸显了一个尚未解决的权衡:如何兼顾问题归因、成果可见性问责与研究激励。
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原文

Charlie Munger said: “Show me the incentive and I will show you the outcome”. The issue with Academic Research, in my opinion, is that the outcomes have drifted very far away from the original goal, which I believe to be the advancement of scientific understanding, and training of the new generation of researchers. Academic research was meant to be about breaking new ground, keeping to honesty and good scientific conduct, being clear and upfront about uncertainties, errors, and mistakes, and improving our common understanding of science, all the while training the new generation to follow these goals and principles.

Recently, I have bumped into multiple cases where I believe the correctness of the results, the honesty of the people writing them, or the lack of curiosity once they are told that their results are wrong, incorrect, faulty, or misleading, has been unsatisfying. Simply put, researchers are not too interested in learning that their papers or reports are wrong, and/or misleading others who don’t happen to know that the results are — known to the authors, and a few select others to be partially — incorrect.

The issue is, once you published the paper, and got the promotions and fame, it doesn’t matter that the results are wrong, and known to be wrong or misleading, and potentially doing harm to the advancement of science. It’s not your problem. It’s someone else’s problem. In fact, when I challenged the authors of one such paper, considered state-of-the-art, and known to the authors to have incorrect evaluation, one of the author’s response was along the lines of acknowledging the issues, but refusing to retract the paper, and instead asking if it’s bothering me in publishing my paper.

The incentives are wrong. In my opinion, it’s not all about the papers — mine or others’. It’s about scientific integrity, about being honest with each other, it’s about caring about the results, it’s about advancement of our common scientific understanding through seeking of truth and correctness. It’s about communicating when something is wildly wrong, and either retracting the relevant incorrect claims, or notifying the community of the known serious issues. It’s about caring for what we all consider to be the common understanding of what is the truth, and cultivating an environment where the new generation grows up to learn what the proper scientific conduct is, and that it is not acceptable to seriously deviate from it.

Unfortunately, I am seeing more and more PhD students who are less and less interested in correctness, precision, and what I’d consider proper scientific conduct. They have learned from those successful in the field what does and does not matter. I remember when someone once told me that my approach for a particular algorithm was wrong (about strongly connected components discovery), and how upset I was that I had no idea. I went home that day and I immediately fixed my tool to use the right approach (i.e. to use Tarjan’s algorithm). I felt deep shame that I had no clue what I was doing, and that I messed up. In contrast, recently talked with a PhD student who wrote a tool that was meant to perform well in certain contexts. When I explained the student that the approach to the evaluation was incorrect, they didn’t follow up at all. I think they were surprised that I later followed up, demonstrating that indeed the evaluation was not careful enough, and the tool is less useful than it seems from the paper’s evaluation. It was a strange experience — when I was a PhD student and someone sat down and showed me that what I was doing was potentially sloppy, I was super worried and very curious to find out what’s going on. I still remember this moment when a reviewer of my dissertation challenged a graph and I was stressed for a week before I figured out they misread the graph, because I didn’t label it clearly. Or when in a presentation I accidentally left out performing so-called ‘restarts’ as a major advancement in the history of SAT solvers, and someone in the audience rightfully pointed it out. I felt embarrassed for having made such a mistake.

I am not sure how to fix this problem. Seemingly, researchers are less and less keen on correctness, precision, and scientific curiosity. They don’t seem to be incentivised to do so. I sometimes wonder if the system has become so damaged, so many PhD students have grown up to be professors in this environment, that much of what I wrote here seems alien, even repulsive, to many. It makes me sad.

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