OK, so we can’t hope, in general, for predictions of a given, say, piece of legislation, in the future of human beings. What can we hope for?
There are some nice suggestions in {Bankes 2002}:
(a) conceive and employ ensembles of alternatives rather than single models, policies, or scenarios; (b) use principles of invariance or robustness to devise recommended conclusions or policies; (c) use adaptive methods to meet the challenge of deep uncertainty; and (d) iteratively seek improved conclusions through interactive mechanisms that combine human and machine capabilities.
Of course, most of these are ruled out by the modern policy cycle, are they not? When can you advise someone to adopt a policy of rapidly trying stuff out and adapting when it doesn’t work? Is there anything less compatible with the modern policy cycle than policy cycles which deviate from an electoral term and outcomes which are delivered as anything other than certain? When push come to ballot, does any political rhetoric run on anything like statistically valid evidence, or is it all trail by anecdote and “common sense”?
OK, my opinions of our political cycle are jaded, but, given them, what role can i see for science-based policy?
In fact, a thing I do not see science being useful for. Causal questions in policy, where they are meaningful at all, are seldom interesting. e.g.
These kind of questions are probably not even well posed, and even if they were defined enough to be answerable, they would still be useless. (in general, that is, not just for science. More pertinent questions:
I suppose this frames the use of science for policy as kind of utilitarian stochastic calculus, which is both more and less than I think it could do, but it will serve as a first pass.
If propagating that idea is, in itself is enough to slightly lower the incidence of people on talk shows demanding of experts, or one another, “but does [loaded issue A] cause [ghastly consequence B]?” then my blood pressure will benefit.
Still working on this one. Consider classic science (which is paradigmatically, I suppose, physics, for your average working philosopher of science.) There we are greatly concerned with causation, by which we mean specific effects which will reliably and quantifiably induced by specific perturbations, and we nut it out by setting up the same perturbations and observing the same effect, time after time after time. Then we try to come up with maximally general or elegant explanations for those results, then get Nobel prizes all round, heartily congratulate our colleagues and everyone lives happily ever after, eventually with jetpacks and benign posthuman intelligences. And nanobots.
That’s what it seems like from over here in social science, anyway.
For policy, things are fiddlier. Policy questions have huge numbers of interacting variables, highly contingent answers and are often one-offs. Sometimes many of the interacting variables can be reduced to simpler, more empirically sound sub-systems, and sometimes not. Moreover, living systems tend to be non-stationary, grossly non-ergodic and highly path dependent. What is the best we can do under such circumstances?
See also: Post-normal science, stationarity.