Simultaneous discovery, as famously discussed by Merton, is really a fascinating idea. On the one hand, we have famous examples like Bell and Gray sending in patents for a telephone on exactly the same day. On the other hand, when you investigate supposed examples of simultaneous discovery more closely, it is rarely the case that the discoveries are that similar. The legendary Jacob Schmookler described – in a less-than-politically-correct way! – historians who see patterns of simultaneous discovery everywhere as similar to tourists who think “all Chinamen look alike.” There is sufficient sociological evidence today that Schmookler largely seems correct: simultaneous discovery, like “lucky” inventions, are much less common than the man on the street believes (see, e.g., Simon Schaeffer’s article on the famous story of the dragon dream and the invention of benzene for a typical reconstruction of how “lucky” inventions actually happen).
Michaël Bikard thinks we are giving simultaneous discovery too little credit as a tool for investigating important topics in the economics of innovation. Even if simultaneous discovery is uncommon, it still exists. If there were an automated process to generate a large set of simultaneous inventions (on relatively more minor topics than the telephone), there are tons of interesting questions we can answer, since we would have compelling evidence of the same piece of knowledge existing in different places at the same time. For instance, how important are agglomeration economies? Does a biotech invention get developed further if it is invented on Route 128 in Massachusetts instead of in Lithuania?
Bikard has developed an automated process to do this (and that linked paper also provides a nice literature review concerning simultaneous discovery). Just scrape huge number of articles and their citations, look for pairs of papers which were published at almost the same time and cited frequently in the future, and then limit further to articles which have a “Jaccard index” which implies that they are frequently cited together if they are cited at all. Applying this technique to the life sciences, he finds 578 examples of simultaneous discovery; chatting with a randomly selected sample of the researchers, most mentioned the simultaneous discovery without being asked, though at least one claimed his idea had been stolen! 578 is a ton: this is more than double the number that the historical analysis in Merton discovered, and as noted, many of the Merton multiples are not really examples of simultaneous discovery at all.
He then applies this dataset in a second paper, asking whether inventions in academia are used more often (because of the culture of openness) or whether private sector inventions are used more often in follow-up inventions (because the control rights can help even follow-up inventors extract rents). It turns out that private-sector inventors of the identical invention are three times more likely to patent, but even excluding the inventors themselves, the private sector inventions are cited 10-20% more frequently in future patents. The sample size of simultaneous academic-private discovery is small, so this evidence is only suggestive. You might imagine that the private sector inventors are more likely to be colocated near other private sector firms in the same area; we think that noncodified aspects of knowledge flow locally, so it wouldn’t be surprising that the private sector multiple was cited more often in future patents.
Heavy caveats are also needed on the sample. This result certainly doesn’t suggest that, overall, private sector workers are doing more “useful” work than Ivory Tower researchers, since restricting the sample to multiple discoveries limits the potential observations to areas where academia and the private sector are working on the same type of discovery. Certainly, academics and the private sector often work on different types of research, and openness is probably more important in more basic discoveries (where transaction or bargaining costs on follow-up uses are more distortionary). In any case, the method for identifying simultaneous discoveries is quite interesting indeed; if you are empirically minded, there are tons of interesting questions you could investigate with such a dataset.
September 2012 working paper (No IDEAS version). Forthcoming in Management Science.