Thursday, December 3, 2020
Interview with Margaret Heffernan on UNCHARTED: How to Navigate the Future

Margaret Heffernan joined me on The Business of Government Hour to discuss her recent book UNCHARTED: How Navigate the Future. Heffernan authors a compelling and timely work that explores the human desire for certainty and our seeming addiction prediction. The future, since it has yet to happen, remains unknow and is always uncharted. This reality tends to make us uncomfortable. To assuage this desire for certainty, we look to methods and tools that can approximate what the future may portend. Today, forecasts and predictions purport to give insights into or define what is to come. Heffernan posits a different perspective: these tools offer us merely “educated guesses” or hypotheses that should be used to engage our curiosity rather than simply accepted as inevitable outcomes. Our choice is not between false certainty or ignorance; it is between surrender or participation. We need to leave simple solutions behind, to be bolder in our search, more penetrating in our inquiry, more energetic in our quest for discovery. The following is an edited excerpt of our conversation.

Why do we tend to be uncomfortable with uncertainty and does that in turn create an addiction to prediction?

To a significant degree, we have succeeded as a species because of our capacity to anticipate what might be happening. This ability to anticipate means we avoid getting run over when we are crossing the road. It bestows an absolute evolutionary advantage.

The problem arises when we come to believe prediction can apply to everything because we might be good at predicting some things. For example, even though people continue to complain, weather forecasting has improved year after year. It is incredibly good. Our grandparents would be amazed by the specificity we enjoy today. The issue is when we take this forecasting to an extreme when we believe we can see further and with more detail than we actually can. It derives from a craving for certainty because that makes us feel safe, comfortable, and fosters an addition to prediction.

Why are election predictions, polls, and surveys specious and often wrong?

There are several reasons. One is because no two elections are the same. If you apply the same methodology from an election where the polling worked, if you could find one, there is no guarantee that that methodology will work next time.

Even if you took this year’s methodology and tweaked it to accommodate everything you’ve learned since, by the time we get to the next election, all sorts of other things may have happened which your algorithms don’t accommodate. Also, people are not always frank and hones when they participate in polling. Tiny margins of errors can have a disproportionate effect and polls today seem to offer a level of certainty they may not deserve. That said, I am sympathetic to pollsters. To some degree, the fault is ours for giving too much credence to polls and surveys. These are better understood as highly educated guesses. This belief that somebody out there has insight into the future is kind of weakness. The Greeks thought that crazy old women in caves knew. The Romans thought they could discern the future reading bird entrails. We seem to have this desire to see into the future without grasping that since it has not happened yet, people don’t know what it is.

In the book, you provided a wonderful history of the evolution of forecasting. You note that there are profound problems that seem endemic to forecasting that still dogs it today. Would you elaborate?

Forecasting has a fascinating history. The founding fathers of forecasting were all diagnosed with tuberculosis. They really understood uncertainty and had a passionate, personal need to feel that they could find certainty. However, there are profound problems endemic to forecasts that dog it still today: they are incomplete, ideological, and self-interested.

The first problem is models are incomplete. Today’s technology accommodates far more data but the intrinsic difficulty of models remains: the more data is compressed, the more its predictive power is compromised. Revealing the intrinsic difficulty of models: they will always be subjective and incomplete representations of complex reality.

The second problem lay in agendas. However much the early forecasters believed themselves to be of pure, scientific inquiry, they all held cherished, implicit beliefs about how the world worked, about what mattered and what did not.

Finally, models are also profoundly susceptible to the human beings who design and run them; they are not and cannot be morally neutral.

This is not about good forecasters versus bad forecasters. This is just endemic to the nature of the beast. You cannot get away from it especially as forecasting becomes a business. There is always a level of uncertainty in life that the forecasting business would attempt to minimize. And in minimizing it, might miss the things that mattered most.

If we cannot predict the future, how can we prepare for it?

I am quite buoyant in this regard. I have written much about organizations that effectively prepare for what is to come. Very often organizations that do not prepare well view planning as optimizing efficiency. Efficiency is fantastic when you are dealing in environments where you have significant levels of control with characteristic processes that are quite linear and repetitive: Think of a car assembly line. Once you start getting involved in more complex environments, efficiency will erode any margin you have to deal with shots and surprises.

Efficiency really gets in the way of preparing because preparedness is about robustness. I distinguish robustness from resilience. Robustness means when something surprising happens, you can just keep going. It is why planes have more engines than you need and more operating systems in a plane than you need. You want the crash not to happen. You want the plane to be able to get through a geese strike or a bug in the operating system. This is robustness engineered into the system to avoid crashes.  On the other hand, resilience means having the capacity to bounce back when one is derailed. They are both necessary but organizations that are better prepared should think much more about robustness and how to build in these buffers.

As a follow up, what can we learn about preparedness from the Coalition for Epidemic Preparedness (CEPI)?

CEPI grew out of four key insights: First, that epidemics come without warning; they are inherently unpredictable. In a world of global travel, they move fast: an outbreak could spread to all major capital cities within sixty days. Since the 1970s, new pathogens have been emerging at the unprecedented rate of one or more a year. Finally, no two epidemics are the same; they don’t repeat themselves. There is no profile of an epidemic, because new strains of a wide array of diseases constantly evolve; because separate geographies with distinct medical, education, and transportation systems require a variety of logistics and supplies; because diverse cultures respond to different messages and authorities in specific ways; and because successful approaches are both medical and nonmedical, albeit in different proportions.

CEPI’s idea of preparedness brings together multiple ways of thinking about an uncertain, unpredictable future. It commissions a wide range of experiments and works through multiple scenarios. This work depends on an artist’s imagination to craft stories of an optimistic future, one in which epidemics are managed or controlled. As a coalition, public understanding and support is vital to the work that stretches indefinitely into the future. But that doesn’t make it a distant dream: epidemics emerge at imperfect times, putting the organization to the test. But preparedness demonstrates how far, and how much better, we can think about the long term when we abandon dependency on prediction and accept uncertainty.

You do a wonderful job of explaining and introducing scenario planning and distinguishing it from forecasting. Why is scenario planning so useful?

Scenario planning as a process grew out of an understanding of complexity and the recognition that it was impossible ever to identify all the forces at work that defined the future. Unlike, traditional planning, which usually contains many assumptions offering certainty where none existed. The best one could do was to identify a plausible variety of futures and interrogate them for implications and consequences. Scenarios identify and test how and where the future and the present meet. One of the great insights from scenario planning has been that the act of doing it can change the systems it strives to describe.

What is adaptive leadership? How does it incorporate characteristic of the artist?

While it is easy to talk about adaptive leadership, it’s hard to produce. It really is a new kind of leadership. Those who will rise to that challenge will be outstanding convenors, better chosen for their skepticism and curiosity than their confidence. Collecting voices, structuring exploration, keen listening, and synthesizing success and failure will be the focus of their work. They need to be excellent interrogators of the ecosystems in which they reside, aware of where they fit and the impact of their decisions on others. They can’t be addicted to prediction or to certainty, but confident and courageous enough to create the conditions in which people can think, and experiment, freely. Being able to reconcile opposites—efficiency and robustness, just-in-time and just-in-case—is a hallmark of their adaptive minds. These leaders are tolerant of ambiguity with a keen capacity to adapt and improvise. They are able to hold the tension between urgency and integrity, to stiffen resolve for what is confusing, frustrating, and frightening, and to resist simplifying what is innately complex.

Your book is chock full of anecdotes that illustrate the importance of thinking creatively as way to tolerate ambiguity. You also acknowledge that poor decisions derive from anxiety and anxiety comes from a lack of control. How can we change this dynamic and perhaps you could highlight the example you provide in the book around the redesign of the patient waiting rooms?

There is a phenomenal experiment going on in Austin, Texas at the Dell Medical Center where they’re building a whole new healthcare system. Their big bet is improving the relationship between the patient and the healthcare institution. Creating individual “care rooms” controlled by patients, in which they could sit with friends and family, could reduce stress, enhance doctor/patient relationships, and produce better choices, leading to better results. When the clinics opened in 2017, patients went directly to their care rooms, where doctors, nurses, social workers, the whole integrated care team came to them, where they waited in calm social surroundings. The geography of the experience put the patient in control. The upshot of all of this is that they can treat people more cheaply with better outcomes by focusing on the relationship between doctors, other medical practitioners, and the patients.

Medicine is saturated with uncertainty and anxiety. At Dell, doctors, communicators, and designers are experimenting together, exploring and devising what they now call “interpersonal medicine.” This approach accepts that improved human relationship between doctors, patients, and caregivers is associated with a 19 percent gain in patient adherence to therapies and improved outcomes. It recognizes that a patient is a person, not just a body; there is more to medicine than data and statistics, and the next big leap in medicine depends on making health care interactions more human.

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