In Computer Science, there are so few single-author papers — how can you do a sane analysis? I propose the following comparison.
Single-author papers are riskier, but can be more original:
- You can work on weird or highly risky projects: you only have to convince yourself.
- You can fool yourself and waste much time.
- You have to wait for formal peer review before you get feedback. Fast feedback tends to help you improve faster.
- You have to increase your breadth. You may need to run your own experiments as well derive new theories.
- Except for a few reviewers, you may end up being the sole human being to know about your contribution.
Multi-author papers are safer, but can get bureaucratic:
- You have to choose safe topics if you want others to be interested.
- If the idea is bad and you are not too intimidating, your partners let you know early on.
- You can learn fast from the expertise of your co-workers.
- You often do not fully appreciate and understand the work delegated to others. You tend to focus on what you know best and become more specialized.
- Your partners are likely to cite your joint work and tell others about it.
Clearly, if your goal is to build up your career, I would say that working with others is a good idea. However, producing a paper on your own shows that you do not need to rely on others. It shows your independence. It shows your breadth.
I have always had solo projects. They tend to be more painful, but somehow, I always have good reasons to push them forward. Mostly, it allows me to produce work without compromising on my (crazy) ideas. I can take risks without feeling guilty. Sometimes I get lucky. Sometimes I waste months in vain.
My theory is that solo authors take more risks and more often end up with poor, unciteable work especially if they lack maturity and skills. However, I predict that if you focus on senior researchers, you will find that solo papers are as good as collaborative work. The only exception to this rule would be senior researchers who enter a new field.