This article is written by Claire Craig, Provost at The Queen's College, Oxford and former Chief Science Policy Officer at the Royal Society.

Here is a challenge that has puzzled policymakers forever:

Observational evidence is always about the past. Policy-makers want to know what consequences their decisions will have for the future.

Some of the biggest misunderstandings between scientists, scholars and policy-makers come about because of confusion about the relationship between the future and the past.

In public policy, the past and future are often bridged by the use of computational models, such as those of the climate, of the economy, or of the spread of an infectious disease. Here, models derived from historic data not only create robust evidence about aspects of the future, which is important in its own right, they also do it in a way that improves the quality of debate around that evidence.

Whereas discussions of modelling in policy tend to assume a scientific basis and tone, work on “futures” is often considered dangerously speculative

This is because models necessarily act as convening spaces, requiring exchanges between those who supply the data and the technical expertise, those who make judgements about the assumptions that underpin the model, and those affected by the model’s outcomes. In other words,

A forecaster’s dilemma

In broad terms, models are used for at least five purposes: prediction or forecasting, the explanation or exploration of future scenarios, understanding theory, illustrating or visualising a system, and analogy.

They are always in some sense fictions, but they are fictions that simplify and abstract important properties of the actual object or system being modelled, to create insights or outputs useful for the purpose at hand.

Policymakers should listen to the stories of today because stories can reach the parts where models fear to go

Whereas discussions of modelling in policy tend to assume a scientific basis and tone, work on “futures” is often considered dangerously speculative. To caricature: to talk explicitly about the future quite often leads to the speaker being accused by other experts, or commentators, of trying to predict or forecast what cannot be predicted or forecast, of trying to control outcomes that cannot be controlled, and of general over-reaching and silliness.

In the words of Dominic Cummings, Special Adviser to UK’s Prime Minister:

“Fields make huge progress when they move from stories (e.g Icarus) and authority (e.g ‘witch doctor’) to evidence/experiment (e.g physics, wind tunnels) and quantitative models (e.g design of modern aircraft). Political ‘debate’ and the processes of government are largely what they have always been — largely conflict over stories and authorities where almost nobody even tries to keep track of the facts/arguments/models they’re supposedly arguing about, or tries to learn from evidence, or tries to infer useful principles from examples of extreme success/failure.“

Evidence, experimentation and quantification, where they are possible, are essential. But, as Cummings’ asserts, stories matter too. Where he may be wrong is in implying that models are a proper source of evidence for the decision-maker, but that stories are not. When thinking or talking about the future, it is necessary – and increasingly possible, in many cases - both to keep track of the models, and of the stories. Stories function alongside models in several ways. Perhaps the two most important are that they can draw attention to specific aspects of a complicated model or of the system the model is trying to explain; and they can be models, in the sense of providing self consistent descriptions of real (or possibly real) worlds. So policymakers should listen to the stories of today because stories can reach the parts where models fear to go.

It’s increasingly possible to find expert sources to help analysts and policymakers

It helps to start from the position that “prediction is very difficult, especially about the future” and then go on to recognise that humans are all always making assumptions about the future, because it is impossible not to.

The question is whether we examine those assumptions or not and there are many potential future events for which we can’t generate any type of plausible quantitative evidence. We cannot rely on historic quantitative models when it comes to the emergent properties of complex systems, and moments of crisis, disruption, or phase change. Here, informed anticipation is the basis of resilience to future risks and of the creation of new knowledge, ideas, and actions. Anticipation informs action and stories and models can both inform anticipation.

The future starts with imagination

The good news is that it’s increasingly possible to find expert sources to help the analyst or policymaker who wants to take thoughtful account of both.

Take economics: in his recent book Narrative Economics, Nobel Laureate Robert Shiller points to the importance of stories in influencing markets, and the potential to map them as a way to begin to anticipate moments of instability and financial disruption. In less orthodox fashion, William Davies’ Economic Science Fictions points to the paucity of imaginings of alternatives to current models of corporations — as, in its more sober way, does the British Academy’s recent project on the Future of the Corporation. Meanwhile, Amitav Ghosh in the The Great Derangement suggests that if climate change had not been largely restricted to genre fiction and therefore not taken seriously by literary writers or critics, then it might have spent less time framed as a disputed scientific question to be resolved by more quantitative evidence (always historic).

It can be scary to acknowledge the need both for quantification and for a rigorous approach to qualitative insights

There might then have been political and policy space sooner to debate the future impacts of climate change both on the basis of the science and the prospective technologies, and as the social, cultural, and ethical challenge to future communities and policy-makers that it actually is.

Take the need to be thoughtful about the future of Artificial Intelligence. The Royal Society’s 2018 report on AI narratives does actually go back to the time of Icarus, because stories expressing extreme views about AI go back at least that far and yet are still with us, from the Prime Minister’s Terminator robot references to Elon Musk’s warning that advances in AI risk “summoning the devil”.

It would be comforting to ignore the persistence of stories and focus on studies of incremental change to, say, employment patterns in particular sectors such as truck driving, social care or law. But as we saw, for example, in the evolution of science, policy and practice on the use of GM crops, public debate needs to operate through both types of argument at the same time, and societies need to try to face up to what the persistence of the stories means in each case.

It can be scary to acknowledge the need both for quantification and for a rigorous approach to qualitative insights, but the thinking about the future in public policy is always an act of bounded imagination. The question is how far to acknowledge — even to enjoy — that. — Claire Craig

(Picture credit: Death to the stock photo)