Five major “concepts” or “fields” – words, really – took hold of public and professional discourse around the world during the recent Covid-19 pandemic: politics, public policy, science, data and evidence.
What are we to make of these concepts, post-pandemic – now that things have calmed somewhat, and in order to be more clear-headed the next time round?
As I explain in my new book on the conspicuous catastrophe of the pandemic school closures, the pandemic period saw scientists and science assume the dominant role, to a historically unprecedented degree, in policy decision-making – with such decision-making often relying on – or at least referencing – data. “Follow the science” and “follow the data”, as it were… Political leaders often obliged, instrumentalized or otherwise ceded to this logic.
But this pandemic-period framing was not only simplistic – nay, it failed to appreciate what exactly “policy” and “politics” are, and also what the appropriate roles of “science” and “data” ought to be in respect of public policy.
To be clear, science is not policy. And data is not policy. No! Both science and data are but inputs – among many other necessary inputs – into public policy. And public policy is, on my considered conception, that which legitimate public authorities (typically governments) do, using a variety of inputs and means, to advance a variety of objectives (outputs and ends) and interests for a candidate jurisdiction or society.
Politics, for its part, is, on my definition, the very contest for that legitimate (constitutional) power – power with which the said public policy can be developed and delivered.
What of science? Science is the study of nature, and the application of the knowledge of such study (as in engineering and medicine) to practical problems. Data in science comes from such study and application, including through mathematical (and computerized) modelling and experiments.
But policy is more complex still – for it must mobilize many other disciplines across many systems of state and society. And so science and data, for all of their apparent authority and contemporary prestige, cannot be dispositive in any policy exercise, as considerations of law, philosophy, sociology, economics and strategy are also and always apposite – again, across multiple systems. I show this in Figure A:
But why should policy not just follow the data or be based strictly or even primarily on data – which today is erroneously conflated with “evidence” as such? Answer: Because policy must be based on proper constructions of – and frameworks for – wicked public problems flowing into and over time. What problem(s) are we trying to solve? And what evidence can we adduce for our proposed method of solving the problem(s)?
To be sure, data may figure in the evidence. Yet that, in itself, is not enough – far from it. For there must be reasons – and reasons, based on any of the disciplines described above, are the core of evidence.
Bref, good policy is driven by good reasons – and data and science may or may not be such a reason (or provide the basis for a reason), depending on the policy problem in question, and the quality and relevance of the data and science at play.
In the case of the pandemic school closures, I began to publicly decry and sound the alarm on the ouster of hundreds of millions of (“third bucket”) children from school from the northern fall of 2020. When I did this online, a number of skeptics asked for the “data” to prove my assertion.I replied that the “data” was not yet available, as this was all happening, as is the case with many emergencies, in real-time – but with massive future consequences. Relevant data on year-over-year school enrollment for that particular school year would, in many countries, not be available to the public for almost another year still; in some cases, even longer.
How, then, to proceed in policy decision-making? Should one have waited for two years for “data” to prove what had happened two years prior in order to respond, two years later, to the mass ouster of students into the third bucket (no school at all)? Nonsense! But that was the logic of “following the data”… Conversely, should one operate on a hunch, at time zero, without any “data” or even other reasons in support of a possible policy move?
Answer: One must, in real-time, adduce all ken and instruments in support of proper decision-making: experience, wisdom, formal academic and professional knowledge, the advice of good advisers, the lessons of the past and other jurisdictions, imagination (yes, imagination!) and, yes, science and data in order to solve difficult public problems. And then one has a chance at some success.
Einstein himself argued for imagination, intuition and “bold speculation” in science as the sine qua non in jumping from the empirically (data-driven) path-dependent to new levels of understanding.[1] Indeed, Einstein argued that “[t]he intuitive and constructive spiritual faculties must come into play wherever a body of scientific truth is concerned […]. Our moral leanings and tastes, our sense of beauty and religious instincts, are all tributary forces in helping the reasoning faculty towards its highest achievements.”[2]
Figure B reproduces Einstein’s diagram on imagination and intuition in science from his 1952 letter to his friend, the Romanian philosopher-cum-mathematician Maurice Solovine. The physicist Gennady Gorelik describes Einstein’s logic as follows: “Axioms A, including the concepts for their formulation, are free inventions of the human spirit (not logically derivable from what is empirically given). Extralogical inventive intuition takes off from the ground, or, rather, the launch paid, of empirical data E. At the second step, some statements Sn are derived from A to be checked by landing in the E at the third step. If the landing is successful, the whole theory, including its axiomatic fundamentals, is justified.”[3]
Of course, all of this applies in spades in public policy as well – new, imaginative frameworks for analyzing problems often enhance the understanding and improve outcomes in practice.
From a public policy perspective – including in emergencies and disasters – what’s still missing? Answer: corrective feedback from the population and other “estates” of society, as I show in Figure A. And politics – and the political contest – plays a key role in this feedback dynamic from population to power. For while public policy is not solely the province of democracies (indeed, non-democracies often did better than democracies in preventing the third bucket catastrophe of the pandemic!), the chief functional advantage of the democratic system is the very existence of feedback mechanisms to public power in order to correct mistakes. (What if, for instance, a mathematical model on which scientific advice is based, on inspection, rubbish? Or what if the data feeding that model is poor? Or what if that numerical predictions of that model are improperly interpreted for or by decision-makers? How to correct? Answer: Feedback mechanisms, only.)
Assuming the public (political) power in question, under the pressure of emergency, is disposed to correction…
References
[1] https://arxiv.org/pdf/1106.6345
[2] Ibid.
[3] Ibid.
About the Author
Irvin Studin is President of The Institute for 21st Century Questions (Canada) and Editorin-Chief & Publisher of Global Brief magazine. He is the Chair of the Worldwide Commission to Educate All Kids (Post-Pandemic). His new book is Never Close the Schools Again. Ever! How the Pandemic Rise of Third Bucket Kids Changed the Arc of the 21st Century.