What Should We Be Worried About?

SOCIETY’S PARLOUS INABILITY TO REASON ABOUT UNCERTAINTY

AUBREY DE GREY

Gerontologist; cofounder & chief science officer, SENS Research Foundation; author, Ending Aging

Broadly well-educated people are generally expected by other broadly well-educated people to easily learn and accommodate new information on unfamiliar topics. By “accommodate,” I mean absorption not only of facts but also of the general tenor of the topic as it exists in the expert community. Unfortunately, this expectation is frequently unfulfilled. I first became aware of the depth of this problem through my own work, the development of medical interventions against aging, in which the main problem is that throughout history we have had no choice but to put the horror of aging out of our minds by whatever psychological device, however irrational, may work—a phenomenon to which researchers in the field are, tragically, not immune (though that is changing at a gratifying pace). But here I wish to focus on a much more general problem.

Uncertainty is, above all, about time scales. Humans evolved in an environment where the short term mattered the most, but in recent history it has been important to depart from that mindset. What that means, in terms of ways to reason, is that we need to develop an evolutionarily unselected skill: how best to integrate the cumulative uncertainties that longer-term forecasting entails.

Consider automation. The step-by-step advance of the trend that began well before, but saw its greatest leap with, the Industrial Revolution has resulted in a seismic shift of work patterns from manufacturing and agriculture to the service industries. But, amazingly, there is virtually no appreciation of what the natural progression of this phenomenon—namely, the automation of service jobs, too—could mean for the future of work. What is left, once the service sector goes the same way? Only so many man-hours can realistically be occupied in the entertainment industry. Yet rather than plan for and design a world in which it is normal either to work for far fewer hours per week or for far fewer years per lifetime, societies across the world have acquiesced in a political status quo that assumes basically no change. Why the political inertia?

The main problem here is the public’s deficiency in probabilistic reasoning. Continued progress in automation, as in other areas, certainly relies on advances that cannot be anticipated in detail and therefore not in precise time frames either. Thus it is a topic for speculation. I do not use that term in a pejorative way but to emphasize that aspects of the future about which we know little cannot thereby be ignored: We must work with what we have. And it is thought-leaders in the science and engineering realms who must take a lead here. Public policy overwhelmingly follows, rather than leads, public opinion: The number-one thing politicians work toward is getting reelected. Thus, while voters fail to reach objective conclusions about even the medium term—let’s say a decade hence—it is fanciful to expect policy makers to do any better.

The situation is the worst in the extreme cases that can be summarized as “high risk, high gain”—low perceived probability of success but huge benefits in the event of success. As any academic will aver, the mainstream mechanisms for supplying public funding to research have sunk to a disastrous level of antipathy toward high-risk high-gain work, to the point where senior scientists stay one step ahead of the system by essentially applying for money to do work already largely complete and bearing no risk of not being delivered on time. The fields of research that most interest Edge readers are exceptionally susceptible to this challenge. Visionary topics are of necessity long-term, hence high risk, and of almost equal necessity high gain. In the area of medical research, for example: Are we benefiting the most people, to the greatest extent, with the highest probability, by the current distribution of research funding? In all such areas I can think of, the bias apparent in public opinion and public policy is in favor of approaches that might arguably (often very arguably) deliver modest short-term benefits but offer almost no prospect of more effective, second-generation approaches down the road. The routes to those second-generation approaches that show the best chance of success are marginalized because of their lack of “intermediate results.”

We should be very worried about this. It is already costing masses of lives, by slowing down life-saving research. And how hard is it to address, really? How hard is Bayes’ Theorem, really? The single most significant thing that those who understand this issue can do to benefit humanity is agitate for better understanding of probabilistic reasoning among policy makers, opinion formers, and thence the public.