Work hours in Pasteur’s lab were regulated, leisure time was viewed with suspicion. Experiments were undertaken only after solitary deliberations by the master himself (p.160, my loose translation).

Which leads to the following question: what is the relationship between mentorship style and scientific productivity? My prior is that there isn’t any. Apprentices just sort themselves on the basis of their compatibility with their advisers’ temperaments, such that, in equilibrium, and controlling for unobserved heterogeneity, autocratic PIs, democratic PIs, and “hands off” PIs (to adopt a crude and not necessarily exhaustive taxonomy) achieve the same level of average productivity.

But while this is fine as null hypothesis, there are of course complications. For example, do labor market processes really make it possible for  trainees to match with mentors in this way? How do trainees balance scientific interests, status, and temperament in the sorting process? And even if sorting is efficient on all dimensions, are certain styles more conducive to the production of scientific breakthroughs (vs. the slow accretion of “normal” scientific results in the Kuhnian sense)? Finally, Are there gender differences in the extent of mismatch, and does this have consequences for the underrepresentation of women in some scientific fields?

I think there is a research agenda here. Maybe this has been explored already, but I somehow that such studies are serious in the sense of taking sorting processes seriously. Am I wrong? I certainly hope so.

From my admittedly cursory search of the citation record, it does not seem that Brin and Page credited this earlier literature in their early efforts to rank web pages’ influence. How could this be? I see three possibilities.

1. They may have been genuinely ignorant of Bonacich’s contributions, and simply reinvented the wheel with a 25 year lag. As an economist, I am genuinely distraught by this possibility, since it implies that the social return to network sociology research was zero (or at least a very small fraction of what it could have been).
2. The use of eigenvector centrality to measure influence in networks might have been well known to contemporaries of Bonacich in computer science, but it circulated within their field in the form of examples rather than a codified body of knowledge, maybe because it was not clear what kind of problems it could be applied to.
3. Brin and Page might have been aware of the network sociology literature, but chose to credit computer scientists (including their advisers) for parochial reasons.

Which of these possibilities is the correct one? Writing this particular piece of scientific history could tell us much about how the market for scientific credit works ­­­— or sometimes fail to work.

NB1: of course, none of this is meant to belittle Page and Brin’s enormous contribution. First, they recognized the usefulness of couching the search challenge in network terms; this was a key recombinative step. Second, the complementary algorithmic innovations that made it feasible to compute the centrality scores for an extremely large (and constantly growing) network were essential to realize their vision.

NB2: Steven Levy’s rather excellent history of Google does not enable one to adjudicate between these interpretations.

NB3: The citation behaviors of physicists who study networks also exhibit pronounced parochialism. They generally ignore the contributions of Linton Freeman and other network sociologists. This tend to annoy my sociologist colleagues to no end, probably for good reasons…

As an economist, I always want to think of things in terms of the policies we’d want to pursue. To me this work suggests the advantage of being in environments where you are exposed to ideas that you wouldn’t typically encounter. That is a feature of highly interactive environments, whether it is Google! or the Niels Bohr Institute, where a ideas bounced around freely leading to a tremendous amount of creative science.

For more on the underlying research see Mark Jung-Beeman, who has a very cool website, and John Kounios on the cognitive neuroscience of solving problems with insight; Jonathan Cohen; Earl Miller; Sohee Park on schizophrenia and creativity; Jonathan Schooler on problem solving more generally. Here is a link to a longer piece on the relationship between this work and work on creativity in other fields.

Many (especially on the conservative side of the aisle) are skeptical of the rationales used to argue in favor of a carbon tax or a “cap and trade” system. In a world where new coal-fired electricity plants open every week in China, the direct effects on temperature levels from a US-only policy shift could be minute when compared to the very real costs it will impose on the economy. These critics have a point, but they miss the effects on the R&D incentives that a carbon tax would generate. That’s the example of a demand-pull policy shift: price carbon emissions at a price that reflects their true social cost, and entrepreneurs will rush in to devise a better carbon-trap.

But that is probably not enough, for these entrepreneurs do not have a very strong scientific base to draw upon.  Policy-makers should also attend to the supply-side of climate change R&D, by creating and/or reforming instiutions susceptible to quickly widen the world’s stock of climate-change relevant ideas. In the United States, much investment is channeled through sclerotic institutions (such as the DoE and the national labs), and not enough funds are awarded according to meritocratic criteria, and insulated from direct political control. To be sure, the sheer scale and capital-intensity of some energy-saving technologies will always justify a role for large institutional structures. But what is missing from the scientific landscape is something like a “National Institute of Climate Change Research,” operated along the line of the NIH, with a unique focus on extramural investments.

How about it, Barack Obama?

It could be that Kindle is a better technology, although there are plenty of customer reviews on both sides, but repeatedly there are two forces that it doesn’t hurt to have on your side in order to defeat a first mover advantage: distribution and complementary assets. We’ve seen this with TiVo vs. the cable companies set-top box DVRs (I wrote a chapter about this), where a sufficient but worse product sold more units than a better first-mover due to incumbent position in the home and a superior distribution channel and complementary asset (cable access and the cable installation network). We’ve also seen this with the Browser Wars (I wrote a paper & a chapter about this), where Internet Explorer overcame Netscape’s lead in the browser market with the help of distribution through exploding PC sales & Windows as a complementary asset to control access to that channel of distribution.

So it’s not always sufficient to build the first or a better mousetrap (in itself, another story about complementary assests overcoming first mover advantages): figure out how to get distribution and complementary assets on your side.

• that the NIH discriminates against young researchers by putting a premium on preliminary results when evaluating proposals. This practice has created a situation in which funding is better viewed as an intermediate research output – a prize for work well done, in the words of Ed Lazear – rather than an input into the production of future results.
• that peer review as practiced by the CSR (Center for Scientific Review) results in the funding of incremental, “normal science”, at the expense of truly creative proposals.

Both criticisms have some merit, and we’ll discuss them (separately) at some later date. For now, I’d like to make a very simple point. To paraphrase Churchill, peer review is a terrible system to allocate funding across projects…except that all the others are worse still. The alternatives, such as an institute model (as practiced in France, or in many of the US National Labs) can easily produce fiefdoms insulated from competition and increasingly disconnected from the outside world. There are also prizes, but they seem much more appropriate to reward innovation towards goals well-specified in advance. In short, there are many reasons to suspect that the competitive, peer-reviewed grant is an important meta-institution whose adoption greatly increased the world’s epistemic base.

So I am hoping that the NIH task force will remember a few things as it develops its reform proposals further. First, do no harm! Remember that those who have the most gripes against the current system might have agendas of their own. Second, experiment! Although scientists often have strong priors regarding what is likely to work, subjecting reform proposals to careful, randomized evaluations is the way to go…

On the day when we found out that consumer prices have risen more in the past year than at any point since 1991, in large part because of the price of energy, one idea that I am intrigued by is that increased investments in energy technology now could lead to lower prices now even though they won’t pay for many years because it would make oil producers want to pump oil now rather than later.