The linear model: science to policy

Image source: FY 2012 NIH Budget Roll-out, PowerPoint Presentation, February 15, 2011.

In my last post, I brought up the "linear model" of basic to applied research. This is pervasive at the highest levels of U.S. federal science agencies: for example, the image above is from a presentation by Francis Collins, the director of the National Institutes of Health (NIH). The NIH has recently been under attack for not producing breakthroughs in biomedicine that can be applied to society. Looking at the statistics, the United States spends the most on health care (per GDP and per capita) than any other developed country, yet we rank 24/30 for life expectancy of these developed countries (source: Crow, 2011). And we spent 26.6 billion on NIH-funded scientific research in 2010. There's not simply a "gap" in the pipeline that links science with society; there's a fundamental mismatch of research funding and goals, and with health outcomes. Many of the health-related outcomes we strive for do not require more basic research, but rather changes in social, behavioral, and economic factors (access to cheap, nutritious food, preventative medicine, cessation of smoking, etc.).

Another version of the linear model as a research "pipeline."

A recent report by a medical advocacy group promotes the linear model that investment in the NIH has led to positive economic outcomes, such as creating public and private jobs. That's great, but that still doesn't answer the question about health outcomes. For example, what's the difference between creating medical jobs, and simply endowing the arts and creating more jobs for artists? A good answer is that we use science for more than just finding cures; we also use it for guiding policy decisions and making politics more transparent through a common language of science.

STS scholars like Yaron Ezrahi have written extensively on how science is necessary to democratic politics because we can require politicians to justify their actions. "Seeing is believing" has been a mantra of science since the 1600s, and science can be used to "see" things like environmental and health impacts. But most of the time, science is not so easy to translate into politics. The case of climate change, and other environmental debates, are a good example of this.

This brings me to the second type of the mythical "linear model": the science-to-policy model. Roger Pielke, Jr. writes about this in The Honest Broker, which I will once again recommend. Like the NIH, the Intergovernmental Panel on Climate Change (IPCC) is part of a scientific authority that believes that more science=good policy outcomes. For quite a few years now, the assumption has been that science tells us climate change is bad, therefore we need policy to stop carbon emissions. In this model, the scientific experts appear to be removed from the politics (the "Mertonian ideal") However, climate change is more complicated than just carbon emissions, and this linear model limits how we can deal with the impacts of climate change that we cannot stop. Dr. Silke Beck is a German social scientist who writes about this in an article called, "Moving beyond the linear model of expertise? IPCC and the test of adaptation," published in the scientific journal Regional Environmental Change in 2010.

According to the linear model, humans cause carbon emissions, carbon emissions cause climate change, and climate change has impacts that we must adapt to. If we are unsure of any of these steps, policy-making becomes a gridlocked debate over the science (which is full of inherent uncertainties, even when nearly all scientists agree that climate change is happening because of humans). Beck's analysis explains why more science has not led to better policies. In the linear model, solutions to climate change are restricted to limiting emissions. But there are other options: policies to promote overall adaptive capacity, and win-win improvements to infrastructure and technological innovation.

Beck's alternatives to the linear science policy model include promoting useful science that will aid decision-makers in addressing climate change impacts. She also calls for bottom-up involvement of local stakeholders (like farmers). This analysis relates not only to my previous post on "science for decision-making," but also future posts where I will discuss public participation in science. As a final thought, Dan Sarewitz and Roger Pielke wrote a great article in 2007 about reconciling the "supply" of science with the "demand" of social outcomes. They write,

"The resulting picture is complex and yields no single, straightforward model for how knowledge and application interact; yet one feature that invariably characterizes successful innovation is ongoing communication between the producers and users of knowledge." (Sarewitz & Pielke, 2007, p. 7)

Sources:

Beck, Silke (2010). "Moving beyond the linear model of expertise? IPCC and the test of adaptation." Regional Environmental Change. DOI 10.1007/s10113-010-0136-2

Crow, Michael (31 March 2011). "Time to rethink the NIH." Nature 471, 569-571.

Sarewitz, D. & Pielke, R. Jr. (2007). The neglected heart of science policy: reconciling supply of and demand for science. Environmental Science and Policy, 10, 5-16.