Citogenesis in science and the importance of real problems

Scientists publish papers in refereed journals and conferences: they write up their results and we ask anonymous referees to assess it. If the work is published, presumably because the anonymous referees found nothing objectionable, the published paper joins the “literature”.

It is not a strict requirement: you can do excellent research without publishing in refereed journals. Einstein refused to publish in refereed journals. The famous computer scientist Dijkstra mostly published his work in the form of letters he would send to his friends: today, we would refer to this model as a blog. Dijkstra invited computer scientists to become independent of peer review as he viewed the peer review process as a path toward mediocrity. More recently, the folks from OpenAI appear to mostly publish unrefereed online papers. Yet OpenAI has probably produced the greatest scientific breakthrough of the decade.

Unfortunately, some people confuse “science” with the publication of refereed papers. They may also confuse our current knowledge with what is currently published in refereed journals.

Many papers in Computer Science tell the following story:

  • There is a pre-existing problem P.
  • There are few relatively simple but effective solutions to problem P. Among them is solution X.
  • We came up with a new solution X+ which is a clever variation on X. It looks good on paper.
  • We ran some experiments and tweaked our results until X+ looked good. We found a clever way to avoid comparing X+ and X directly and fairly, as it might then become obvious that the gains are small, or even negative! They may think: We would gladly report negative results, but then our paper could not be published. Some years ago, I attended a talk by a highly productive research who was providing advice to the students: never run an experiment unless you are sure you can turn it into a positive result.

It seems hard to believe that you can make sure that all your ideas turn out to be correct. But it is not so difficult. A popular approach to get positive results is to use a model as validation. Testing in the real world takes a lot of effort and your results could be negative, so why bother? Even when running experiments in the real world, there are many ways to cheat to ensure you get the result you need.

It looks harmless enough: just people trying to build up their careers. But there might be real harm down the line. Sometimes, especially if the authors are famous and the idea is compelling, the results will spread. People will adopt X+ and cite it in their work. And the more they cite it, the more enticing it is to use X+ as every citation becomes further validation for X+. And why bother with algorithm X given that it is older and X+ is the state-of-the-art?

Occasionally, someone might try both X and X+, and they may report results showing that the gains due to X+ are small, or negative. But they have no incentive to make a big deal of it because they are trying to propose yet another better algorithm (X++).

This process is called citogenesis. It is what happens when the truth is determined solely by the literature, not by independent experiments. Everyone assumes, implicitly, that X+ is better than X. The beauty of it is that you do not even need for anyone to have claimed so. You simply need to say that X+ is currently considered the best technique.

Some claim that science is self-correcting. People will stop using X+ or someone will try to make a name for himself by proving that X+ is no better and maybe worse than X. But in a business of science driven by publications, it is not clear why it should happen. Publishing that X+ is no better than X is an unimpressive negative result and those are rarely presented in prestigious venues.

Of course, the next generation of scientist may have an incentive to displace old ideas. There is an intergenerational competition. If you are young and you want to make a name for yourself, displacing the ideas of the older people is a decent strategy. So it is credible that science may self-correct, one funeral at a time:

A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die (…) An important scientific innovation rarely makes its way by gradually winning over and converting its opponents (…) What does happen is that its opponents gradually die out, and that the growing generation is familiarized with the ideas from the beginning: another instance of the fact that the future lies with the youth.

— Max Planck, Scientific autobiography, 1950, p. 33, 97
There are cases where self-correction does happen: if there is a free market and the scientific result can make a difference by offering a better product or a better service. That is why computer science has made so much progress, so rapidly: we have clients that will adopt our ideas if they are worthwhile. Once your new idea is in the smartphones, it is hard to deny that it works. Similarly, we know that Physics work because we have nuclear bombs and space exploration. What keeps us honest are the real problems.

John Regehr made a similar point about our inability to address mistakes in the literature:

In many cases an honest retrospective would need to be a bit brutal, for example to indicate which papers really just were not good ideas (of course some of these will have won best paper awards). In the old days, these retrospectives would have required a venue willing to publish them, (…), but today they could be uploaded to arXiv. I would totally read and cite these papers if they existed (…)

But there is hope! If problem P is a real problem, for example, a problem that engineers are trying to solve, then you can get actual and reliable validation. Good software engineers do not trust research papers: they run experiments. Is this algorithm faster, really? They verify.

We can actually see this effect. Talk to any Computer Scientist and he will tell you of clever algorithms that have never been adopted by the industry. Most often, there is an implication that industry is backward and that it should pay more attention to academic results. However, I suspect that in a lot of cases, the engineers have voted against X+ and in favor of X after assessing them, fairly and directly. That is what you do when you are working on real problems and really need good results.

It gets trickier in fields such a medicine because success may not ever be measured. Do you know if your doctor is more likely to cure you than another doctor? They may not even know themselves. So you need to work on problems where people measure the results, and where they have an incentive to  adopt ideas that work. In effect, you need real problems with people who have skin in the game.

We fall too often for the reification fallacy. A science paper is a like a map. You can create a map of a whole new territory, even if you have never been there. The new territory might exist or it might not exist, or it could be quite different from what you have described. If you never visit the territory, if nobody visits the territory in question, then you may never find out about your mistake. In effect, we need actual explorers, not just map makers.
And adding more map makers, and more maps, does not help. In some sense, it makes things harder. Who is going to compare all these maps? We need more explorers.

Daniel Lemire, "Citogenesis in science and the importance of real problems," in Daniel Lemire's blog, June 14, 2023.

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Daniel Lemire

A computer science professor at the University of Quebec (TELUQ).

13 thoughts on “Citogenesis in science and the importance of real problems”

  1. @Alejandro

    It is a benign example of what I am pointing out. There are indeed countless engineering papers written every year using the Fibonacci heap. It is unclear *why* they use the Fibonacci heap because we now have overwhelming evidence that it is not worth it.

    However, there are many hidden Fibonacci heaps out there in research papers.

  2. Is this also a BIG reason why authors don’t make available their source code publicly? I believe it is.

  3. Fibonacci heaps are still cited only because authors are too lazy to look past their textbooks to the more recent literature, where simpler, faster, and more practical data structures with the same theoretical guarantees have been known for years. (Fibonacci heaps are still cited in textbooks because _textbook_ authors are too lazy to look past _their_ textbooks to the more recent literature, where etc.)

  4. The point where things completely come full circle is when the engineers come out with an open source package/library (e.g., pyX) that implements several of the techniques, including X, X+, and X++, making it easy for researchers to try them all.

  5. From my personal experience, it’s the lack of experience and exposure to better / more advanced techniques that means that these better methods get left on the shelf.
    The cognitive effort required to apply, let alone come up with, is prohibitive to their adoption, and yet the majority of developers I know would rather work things out from first principals and use inappropriate levels of abstraction, more for macho purposes than practical ones.
    my 2c. 😉

  6. It is even worse. Suppose I find that X+ is no better (or even worse) than X and thus mention X+ negatively in my paper. Very quickly, my mentioning is going to be reduced to “a citation” and increase X+’s citation count. Even with my negative result, I’ll actually help promote X+!

  7. It is even worse.
    Negative results are not only harder to publish, but they will also not receive citations. One could argue that we could and should publish negative results at least on arXiv. Or make a journal dedicated to negative results.
    But citations tend to be even more important than the number of papers you published. So even if we would publish negative results, they will not advance your career; and we can make better use of the time spent for the write-up.
    What we really need to do is publish the source codes of failed reproductions, by contributing them to tools, so others can more easily see that some algorithm does not work as promised.

  8. No tiny part of the obsession with novelty is the political economy.

    Things that are “novel” are ones that be made into property and thus can most easily have economic rents extracted from them under our current system, so that’s where funding goes, and where prestige goes. Where there are incentives, we should not feign surprise at the fact that people respond to same and optimize their outlook and their conduct in a way that they have reason to believe maximizes their advantage, given those incentives.

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