Is the "Settled Science" Argument a Straw Man?

The recent publication of the Fifth Scientific Assessment (AR5) from the IPCC concluding that human being are altering the climate has been met by a uniform response from the Right Wing: the idea that Science is settled, whether it be Climate Change or Evolution is a Myth. The argument surrounding "Settled Science" has actually been going on for a long time, especially between the NY Times and the Wall Street Journal (here). If the Right Wing doesn't like a scientific conclusion or theory it just argues that the science isn't settled. The Left Wing argues that science is never settled.

A recent opinion piece by Charles Krauthammer in the Washington Post (here) uses the settled-science myth to criticize the current Administration in Washington DC for worrying about climate change or requiring that health insurance cover mammograms. Since a recent large-scale, randomized clinical trial showed little benefit from mammograms (here), Mr. Krauthammer suggests that Climate Science will shortly also be overturned and, what is more, most scientific conclusions can be ignored by political commentators.

It is unfortunate that both sides have chosen to use the idea of "settled science" because the idea is wrong. Science is not about results and conclusions. Science is about "models" and the evidence that accumulates for and against the models. Some models have better support than others.

The best supported scientific model of climate change can be summarized with the I=PAT Model above (sometime called the Kaya Identity and a reasonable simplification of complex Integrated Assessment Models). Population growth (N) leads to more economic production (Q) which leads to greater energy use (E) which leads to greater CO2 emissions which leads to increases in global temperature (T). The competitor model (if the Right Wing can be said to have such a thing) would be that global temperature is a random walk,  T(t) = T(t-1) + U. Tomorrow's global temperature is today's global temperature plus random error, U (unknown).

The random walk competitor model is easily defeated (here). Until someone comes up with a better model and until there is some evidence either for or against that unknown model, the I=PAT model is the best one we have. Arguments about "settled science" do not lead to better models.

The same arguments can be applied to Mammograms, PSA screening for prostate cancer, or any area of scientific interest. First, we have to ask if good models are available. In the case of many medical findings, good models are not available. There are currently few good models for the causes of cancer. Screening is an attempt to find something early before it progresses. Screening would be better if it was less intrusive and if we knew what we were looking for (BRCA1 genetic screening is one example). None of this has anything to do with settled science.