The science that informs public debate increasingly can not use experiments to adjudicate disagreements, and instead must rely on dueling models. We wouldn’t purposely expose randomly selected groups of people to lead paint, and couldn’t build parallel full-size replicas of earth and pump differing levels of CO2 into them.
There is a spectrum of predictive certainty in various fields that label themselves “science,” ranging from something like lab-bench chemistry at one extreme to something like social science at the other. Scientific fields that address integrated complexity sit in a gray area somewhere in between. We can pound the table all we want, and say with smoldering intensity that “science says X,” but our certainty is much lower when X = “the projected change in global temperatures over the next 100 years” than when X = “the rate at which this bowling ball will fall.”
Serious scientists in fields dominated by integrated complexity are constantly trying to develop methods for testing hypotheses, but the absence of decisive experiments makes it much easier for groupthink to take hold. A much larger proportion of scientists self-identify as liberal than conservative, so when scientific questions of integrated complexity impinge on important political questions, the opportunities for unconscious bias are pretty obvious. Hasty conservative political pushback (e.g., “global warming is a hoax”) naturally creates further alienation between these politicians and scientists. The scientists then find political allies who have political reasons for accepting their conclusions; consequently, many conservatives come to see these scientists as pseudo-objective partisans. This sets up a vicious cycle. Unfortunately, that’s where we find ourselves now in far too many areas.
Fortunatly, Manzi has some solutions, which involve having more engagement wich science and trying to get more experimental data on various subjects.