What's in a name?
The Science of Science and Innovation Policy sets up its limitations in the very title of the program. Science is valued by decision-makers for its predictive capacity, and the NSF’s desire for a “science” of science policy echoes that desire. As quoted above, SciSIP refers to the predictive ability of economists (which have since been seriously called into question by the global market failure) as an example of what is needed in science policy. Also stated as necessary are predictive models, metrics, and other analytical tools. The whole enterprise is aimed at reducing uncertainty in the science policy arena, and that is precisely where SciSIP falls on its own sword. For uncertainty is precisely what we have when speaking of the transformative capacity of science and technology. Indeed, the reservoir model is built on the insistence that the results of scientific activity can be neither directed nor predicted. Vannevar Bush got it partially right on that issue. Where the reservoir model is most dangerous is in its assumption that the impacts of science and technology are all beneficial.
The weakness of SciSIP is that it seems to want to eliminate the uncertainty of science while holding onto the uncritical view of scientific outcomes. A better use of resources would be to think through the potential negative consequences of science and technology, with the awareness that the one aspect of scientific change that is completely predictable is that there will be unintended, unpredicted consequences. SciSIP’s focus on analyzing investments in science research is misplaced. What matters is not what is put into the reservoir, but how scientific knowledge is gathered, synthesized, and used. In other words, what will determine societal benefit is not what goes in the reservoir, but what is taken out, and how, and for what purpose.
