I think that, in a sense, a) would have been the right solution here.
While I'm not sure this is the case in all fields, at least in mine, when you apply for grants, people too much about how famous you already are and what you have published. People don't care about how many grants you already have, or how much money you burned through to produce your previous publications.
This is bad.
First, it creates the illusion that there are more scientists out there than can be sustained by grant funding, when in reality the problem isn't lack of funding, but the fact that the funding gets concentrated in the hands of the highly successful instead of being evenly distributed to the highly and moderately successful. Thus, pursuing a career in science becomes risky because you might not be able to get a job (as GP stated), and so people decide they don't want to do science.
Second, it's inefficient. A professor at my institution employs 50 people and produces about 1 paper per year. 15 moderately successful professors with 2 people working under each of them could do much better than that. But he has a Nobel prize and they don't.
Finally, it's horrible for the people in these labs. This is where accountability comes in. A professor with a small lab is accountable for the success of each of his students/postdocs. With larger labs, professors can allow students to fail, because their success is no longer necessary for success of the labs. Moreover, professors run out of ideas and waste their trainees' time. (I have heard rumors that a different Nobel prize winning scientist hired multiple people to work on the same project, not as collaborators, but as competitors. The person who won got a high-impact publication. The person who lost got nothing.)
There's no obvious solution here, since science can't be funded completely algorithmically, but to start I think that funding agencies should be requiring professors to disclose both the number of personnel they employ and their grant history when they apply for further grants, and take this into account during the review process.
While I'm not sure this is the case in all fields, at least in mine, when you apply for grants, people too much about how famous you already are and what you have published. People don't care about how many grants you already have, or how much money you burned through to produce your previous publications.
This is bad.
First, it creates the illusion that there are more scientists out there than can be sustained by grant funding, when in reality the problem isn't lack of funding, but the fact that the funding gets concentrated in the hands of the highly successful instead of being evenly distributed to the highly and moderately successful. Thus, pursuing a career in science becomes risky because you might not be able to get a job (as GP stated), and so people decide they don't want to do science.
Second, it's inefficient. A professor at my institution employs 50 people and produces about 1 paper per year. 15 moderately successful professors with 2 people working under each of them could do much better than that. But he has a Nobel prize and they don't.
Finally, it's horrible for the people in these labs. This is where accountability comes in. A professor with a small lab is accountable for the success of each of his students/postdocs. With larger labs, professors can allow students to fail, because their success is no longer necessary for success of the labs. Moreover, professors run out of ideas and waste their trainees' time. (I have heard rumors that a different Nobel prize winning scientist hired multiple people to work on the same project, not as collaborators, but as competitors. The person who won got a high-impact publication. The person who lost got nothing.)
There's no obvious solution here, since science can't be funded completely algorithmically, but to start I think that funding agencies should be requiring professors to disclose both the number of personnel they employ and their grant history when they apply for further grants, and take this into account during the review process.