Two Articles on Predictions & Hype in Science
Matthew C. Nisbet, Ph.D. is Associate Professor of Communication Studies, Public Policy, and Urban Affairs at Northeastern University. Nisbet studies the role of communication and advocacy in policymaking and public affairs, focusing on debates over over climate change, energy, and sustainability. Among awards and recognition, Nisbet has been a Visiting Shorenstein Fellow on Press, Politics, and Public Policy at Harvard University's Kennedy School of Government, a Health Policy Investigator at the Robert Wood Johnson Foundation, and a Google Science Communication Fellow. In 2011, the editors at the journal Nature recommended Nisbet's research as “essential reading for anyone with a passing interest in the climate change debate,” and the New Republic highlighted his work as a “fascinating dissection of the shortcomings of climate activism."
Earlier this year, in an article at Nature Biotechnology, I joined with several colleagues in warning that the biggest risk to public trust in science is not the usual culprits of religious fundamentalism or "politicization" but rather the increasing tendency towards the stretching of scientific claims and predictions by scientists, university press offices, scientific journals, industry, and journalists. As I detail with Dietram Scheufele in a separate article at the America Journal of Botany,(PDF) each time a scientific prediction or claim goes beyond the available evidence and proves to be false, it serves as a vivid negative heuristic for the public.
This past week, two important articles describing the perils of prediction in the life sciences and climate sciences appeared at The Scientist magazine and Nature Reports Climate Change respectively. In a cover article for the The Scientist, Stuart Blackman identifies several factors driving the tendency towards hype. As Blackman notes, scientists are under increasing pressure to publish at ever more competitive flagship journals, meaning that the conclusions of a paper have to be that much more provocative. Granting agencies are also putting stronger emphasis on the public impacts portion of funding proposals, again creating an incentive to sometimes promise too much. A third and major factor is the increasing privatization of university-based science with strong incentives and rewards for commercialization, a route that usually involves a heavy dose of promotion. In his article, Blackman draws on the insights of some of the top social scientists studying these trends including Brian Wynne, Christine Hauskeller, and Daniel Sarewitz. A useful sidebar summarizes advice on how researchers can avoid hype in communicating with the public, policymakers, and/or the media.
In a commentary at Nature Reports Climate Change, Mike Hulme, Roger Pielke, Jr and Suraje Dessai warn against promising that climate science can "supply on-demand climate predictions to governments, businesses and individuals," estimating impacts on certain regions and sectors.
"Scientists and decision-makers alike should treat climate models not as truth machines to be relied upon for making adaptation decisions, but instead as one of a range of tools to explore future possibilities," they write. And as they aptly observe, it's not just a matter of technical certainty. Even in cases where forecasts might be accurate in a formal statistical sense, effectively communicating the complexity of these findings to the public and decision-makers will prove a difficult task. Here's the key take away from their commentary:
For scientists, the lesson here is clear. Caution is warranted when promising decision-makers a clarified view of the future. Guaranteeing precision and accuracy over and above what science can credibly deliver risks contributing to flawed decisions. We are not suggesting that scientists abandon efforts to model the behaviour of the climate system. Far from it. Models as exploratory tools can help identify physically implausible outcomes and illuminate the boundaries where uncertain knowledge meets fundamental ignorance. But using models in this way will require a significant rethink on the role of predictive climate science in decision-making. In some cases the prudent course of action will be to let policymakers know the very real limitations of predictive science. For decision-makers, the lesson is to plan for a range of possible alternatives. Instead of seeking certainty, decision-makers need to ask questions of scientists such as 'What physically could not happen?' or 'What is the worst that could happen?'
The authors' warning is important to the U.S. context. Perhaps the most effective way to convey the significance of climate change is to communicate to Americans how it is impacting the region or area in which they live. Yet as effective as this strategy might be, these communication efforts need to proceed cautiously, otherwise they risk opening the door to counter-claims that scientists and government agencies are going beyond available scientific evidence.