The demise of the California effort to bypass federal inability to address the issue by passing a state universal health insurance plan can be no real surprise, nor can it be deemed all bad. It has put into increasingly sharp relief the basic barrier that should be in the minds of legislators and policy wonks, alike – how can we pay for it? The problem includes two things we cannot yet do: 1) predict the costs and full effects of universal insurance to the country; and 2) control the effects we do not want, while promoting those we do want.

Universal health insurance is already mired in purely political, a priori controversies over the sophistic issue of whether it should be based on a “free market” solution, or a “single payer” one. This issue is sophistic because it is being argued solely on the basis of how many persuasive politicians are pre-persuaded that one is the better choice. There are no scientific arguments over which has been proven to be better, or even which has been tested via simulation or predictive modeling and looks better.

Sophistry is still a major tenet of our criminal and even civil justice system whenever jury trials are involved. While forensic science has advanced somewhat since Magna Carta, there are still valid arguments in favor of juries, as long as they can think and choose with scientific input, as opposed to once popular alternatives such as dropping the accused in the water to see which floated, indicating “scientifically” who was guilty.

We have major challenges with respect to predicting the consequences of universal insurance, but we also have impressive predictive modeling technologies to put to the task. Artificial intelligence, which could easily include updated input from states such as Massachusetts that have already initiated universal insurance, may be able to deliver some credible forecasts of the consequences, rather than relying on pre-persuaded conservatives or liberals to deliver their dire or optimistic self-serving sophistry.

But the real challenge lies in learning how to better manage the health status of this nation, rather than simply predicting what will be the likely consequence of universal insurance in the present unhealthy status. We are in the midst of an enormous “natural experiment” by literally thousands of entities — government agencies, commercial insurers, and employers – to manage the health of their populations. By investing a good deal of money in scientific comparisons of which of these work well for which dimensions of health or disease in which populations, we could dramatically increase the data with which predictive modeling can work.

Until we can predict with at least a reasonable, non-pre-persuaded confidence what the future consequences of universal health insurance would be in something closer to achievable universal health than we have now, it is only wise and responsible of us to be chary of passing whichever solution gets the most votes. To inject Robert Frost into the matter, there are times for voting on an issue, and times for injecting thinking, instead.