I was once a member of a research team tasked with predicting the future. Our aim was to chart future economic trends in an area of public policy. We applied sophisticated analytical techniques to massive amounts of data. We concocted multiple scenarios using assumptions of varying degrees of optimism and pessimism. And we had direct access to one of the most elaborate statistical models of a national economy. When I was told that this model is used mostly to generate forecasts, I rubbed my hands together in giddy anticipation. Then the wise old project-leader offered a reality check: “I’ve done this type of projection many, many times before. The forecast was right only once. That time all my variables were completely off but the errors cancelled each other out.” In other words, he successfully predicted the future only once … and that was pure coincidence.

As we emerge from a global recession, many of us wonder why policy-makers repeatedly fail to anticipate the gyrations of the economy. Politicians in the U.S. Congress were certainly wondering that when they dragged Alan Greenspan out of retirement to ask awkward and pointed questions about what went wrong. Greenspan is, of course, the former head of the U.S. Federal Reserve, America’s central bank. Pundits nicknamed him “The Maestro” because they were convinced he could read the economy with exactness and enact the right monetary policy at just the right moment. He was credited with superb foresight; touted as someone who could anticipate problems well in advance. Even if that’s wishful thinking, many supposed, at least he has an army of skilled economists to generate forecasts for him.

There are many reasons why Greenspan is undeserving of the nickname “The Maestro”. (For a sobering account of Greenspan’s hubris and blundering, read Grant, 1996 and 2008, as well as Fleckenstein & Sheehan, 2008.) But what about all of those forecasts? Alan Blinder, Greenspan’s former Deputy, offers some revealing admissions in his book Central Banking in Theory and Practice (1998). His forecasts didn’t hit the mark. But he defends the forecasting method nonetheless.

My approach to this problem while on the Federal Reserve Board was relatively simple: Use a wide variety of models and don’t ever trust any one of them too much. So, for example, when the Federal Reserve staff explored policy alternatives, I always insisted on seeing results from (a) our own quarterly econometric model, (b) several alternative econometric models, and (c) a variety of vector autoregressions (VARs) that I developed for this purpose. My usual procedure was to simulate a policy on as many of these models as possible, throw out the outlier(s), and average the rest to get a point estimate of a dynamic multiplier path. This can be viewed as a rough—make that very rough—approximation of optimal information processing. As they say: Good enough for government work! (Blinder, 1998, pp. 12-13)

That’s quite a witch’s brew of forecasting models and techniques. The last sentence, even if it is tinged with sarcasm, is quite chilling given the blunt trauma that monetary policy can have on an economy. What makes Blinder’s quotation especially worrying is that he was hoping for the lucky result which my former project-leader once achieved. He hoped that, with a bit of crude mathematical finagling, all the errors would cancel out and give him the “good enough” answer. That certainly puts Greenspan’s sphinx-like pronouncements into context … that and Greenspan’s difficulty in figuring out what was actually going on in the economy at the moment given the normal lag in economic statistical reporting.

My thinking about forecasting is heavily influenced by Nassim Nicholas Taleb’s books Fooled By Randomness (2005) and The Black Swan (2007). Taleb is a former finance industry wonk who is trying to get people to consider the occurrence of rare, unpredictable one-off events with extreme impact. Those events—called Black Swan events—are particularly prob­lem­atic because of our tendency to treat them, after the fact, as if they were entirely predictable. These events put the whole business of forecasting into doubt. If you can’t predict these extreme game-changing “wild card” events (outliers) that occur every so often, then how good can your predictions be? Taleb argues that, since we can’t predict these events based on the track-record of the past, then we should make ourselves flexible and adaptable for when they happen (Taleb et al., 2009). A blind faith in forecasting numbers to anticipate the future is “statistical self-deception”.

In Stalking the Black Swan (2010), Kenneth Posner argues against Taleb’s sweeping dismissal of forecasting. He argues that “fundamental research” can help us forecast some Black Swan events or learn about them “a little bit earlier.” Even so, we will still be surprised from time to time. Again, in such circumstances, the best strategy is to be prepared to respond quickly to these surprises. This includes being conscious of errors in judgement so that we don’t make bad, knee-jerk decisions in the face of unanticipated alarming events. I have sympathies for Posner’s position. But that’s still a meagre defence of well-financed, well-staffed forecasting. That’s little comfort in highly complex and volatile circumstances. And the role of judgement raises an awkward question: even when we do anticipate a rare high-impact event, how seriously will we take the finding at the time of discovery? History is full of warning bells that were ignored because they conflicted with the prevailing mindset.

All of these considerations led me to seek out a sober stock-taking of forecasting and, more generally, foresight. I define foresight as the ability to (a.) clearly see the reality of today while considering how matters can change in the future. That includes spotting trends, complicating factors, the consequences of actions, and causal dynamics. Foresight also implies the ability to (b.) imagine opportunities and challenges in the future in order to (c.) make prudent preparations. Foresight is not prediction per se. Those with keen foresight are acutely aware of the limits to their ability to anticipate. But foresight involves thinking about the future in a disciplined way. It also involves considering the role that we play in determining that future given that we’re not just passive observers of the times, but active participants in them. And some of us are more influential participants than others.

Future Savvy: Identifying Trends to Make Better Decisions, Manage Uncertainty, and Profit from Change by Adam Gordon (AMACOM, 2009), pp. x, 294.

My search led to Adam Gordon’s Future Savvy. Like Posner, Gordon challenges Taleb’s blanket dismissal of forecasting. Gordon does not deny the existence of Black Swan events. And his book is a giant compendium of all of the things that usually go wrong with predictions. Moreover, Gordon offers a sceptical discussion of the subject that chastises simple-minded futurists, tech enthusiasts, and various other prophets of doom and boom. The difference between Taleb and Gordon is that Gordon doesn’t dismiss out-of-hand the usefulness of structured thinking about the future. Many important decisions require us to speculate about what the future might hold. Gordon wants us to be savvy in the way we anticipate the future instead of flying by the seats of our pants, so to speak.

To set the stage, Gordon talks about how the forecasting industry is rife with problems. There are no standards, no accepted methods, no standard terminology. There are no penalties for failure given that people tend to forget forecasts by the time they can be proven wrong. And when dealing with the forecasts offered by pundits, stakeholders, and activists, Gordon reminds us, “we are knee deep in predictive wishful thinking, scare-mongering, or blatant self-promotion.” (p. 5) Buyer beware.

Then there are the data problems. Forecasters use data from the past to project trends into the future. They rely heavily on data gathered for other purposes, not gathered for the task at hand. Availability is patchy. The data comes from multiple sources and is created using different methods. Important statistical caveats get lost. The context of the original studies gets forgotten. Variables are often defined loosely … and change over time … and are measured differently in different places. Data gathering methods often change over time in ways that exaggerate or obscure a trend. Sensationalist “newsy” data often commands the most attention. Some things are inherently difficult or impossible to measure accurately. All sorts of assumptions get embedded in data projected into the future. Furthermore, Gordon talks about the ways in which numbers can be finessed in an underhanded way. He advocates “number scepticism”, warning: “But no matter how scientific the data appears, choices have been exercised at every point about what to observe, what to count, how to measure it, and how to report it. … But numbers are not bedrock. There is no bedrock.” (p. 59)

As an aside, statisticians have a snide nickname for analysts who mix’n'match statistics from a hodgepodge of sources to create complicated models or story-lines. That nickname is junk-yard dog. Gordon gives the impression that the forecasting business is, by necessity, heavily populated with these collectors.

The sources of potential error don’t end with data. Our biases cause us to misinterpret and misreport the data.

Some bias is intentional manipulation. Rascally analysts ignore or downplay countervailing evidence. They give evidence less scrutiny if it confirms the desired result. Emotionally charged language and associations are used. Terms are defined in leading ways. Extreme cases are used to represent the norm. Forecasts that don’t accord with an agenda get ignored, especially if the forecast is sponsored by a powerful interest. Organisational incentives can cause those being scrutinised to fudge the numbers. When forecasts are presented to the media, the most extreme trends get attention and important caveats remain unreported. Gordon is particularly critical of the so-called futurists who use “stretch thinking” and “big-picture thinking” to imagine a world full of only big changes. Many have a technophile bias, or the assumption that technology is the sole motive-force of large-scale societal change. Gordon’s advice is to keep your guard up and be wary of motives.

Setting aside the thinness of this advice, Gordon has a strange attitude when talking about manipulation. He makes a distinction between forecasts that attempt to be accurate and forecasts that attempt to influence. Employee-prodding managers, partisan policy wonks, and alarmist activists use loaded forecasts to move minds. Humility, qualification, and tentativeness don’t have a place in these circles. There may be a legitimate reason for using leading forecasts, such as communicating the art-of-the-possible or giving someone an ambitious target to strive for. However, leading forecasts without full disclosure are instruments of underhanded manipulation. Gordon is eerily agnostic. His advice and tone of voice suggests that he is oblivious to the ethical problems posed by the manipulative use of forecasts. It’s a strange contrast with Gordon’s advice about being careful and pragmatically sceptical.

Back to the sources of error.

Gordon itemises a number of cognitive biases that are inherent to the way we think. We often miss Black Swan events and abrupt changes in prevailing wisdom (“paradigm shifts”), he argues, because we are always filtering information based on perceived relevance. This “inattentional blindness” causes us to not notice important influences on the future. We also overemphasize recent happenings over older events (the recency effect). We’re susceptible to herd thinking and faddish ideas. A few chance events are often mistakenly interpreted as a trend or other pattern. Gordon places particular emphasis on how our current context frames the way we see and think (situational bias), especially how the prevailing mindset and preoccupations of an era skew the way we think about the future (Zeitgeist bias). For example, nuclear-powered airplanes may have seemed inevitable to someone living in the 1950s, a time preoccupied with thoughts of nuclear technology, suggests Gordon. That notion seems absurd today. To counter this problem, he argues for the need to extract the assumptions underpinning our expectations. Those assumptions need to be questioned and tested. And one good test is to reverse the assumption; that is, consider how the future would be different if the opposite (or very different) assumption were used.

I would add that people habitually rely on lazy assumptions about the future in general. As Howard Segal points out in his book Technological Utopianism in American Culture (2005), late-19th and early-20th-Century intellectuals assumed a technological plateau when describing the future. Even today, we assume our arrival at some destination—a future steady state—instead of a world of on-going change that is unevenly distributed and erratically paced, as exists now.

Gordon invites us to consider the utility people derive from a particular technology before jumping to conclusions about how it will revolutionise everyone’s lives. Tech-happy futurists are too quick to assume broad public acceptance of a new technology while ignoring the trade-offs of adoption. There are costs to be considered. In many cases, the price is too high and existing technologies do a good enough job. Or old technologies have an inertia, such as when users are “locked in” to a particular technology. Or social values change. Or switching creates undue inconvenience and aggravation. Or the technology has uneven appeal across diverse groups in society. Or, or … Gordon reminds us that simple technological domino effects almost never happen. The pace of change is usually slower than anticipated. A variety of factors determine how successful an innovation will be.

That leads us to the dynamics of change. I’m not going to describe each dynamic in detail. Gordon devotes a lot of space to them. Instead, I’ve listed them iconographically in the following diagram. Note that the darker lines signify consequences (and consequences of consequences; a.k.a. second-order and third-order events).

A trend observed today may not continue onward along a straight-forward path. Trends peter out … change course … hit limits … get caught in reinforcing loops … have side-effects … provoke reactions … et cetera. The same goes for underlying causes. Trends can be particularly difficult to track within the complex systems that govern our lives. Thus, Gordon offers a chapter on system analysis.

As someone who studies organisations, I’m often seeing policies and strategies change with sadly predictable pendulum swings. Gung-ho leaders push in one direction with gusto only to get a lesson in humility. Their efforts hit limits and opposition. Their assumptions hit reality. Subsequent leaders see wreckage everywhere and push in the opposite direction, looking for balance. Balance alludes them and they go to far. Another pendulum swing begins. Some swings happen from season to season. Others happen over decades. These swings may be predictable, but their exact timing certainly isn’t.

Gordon rounds out Future Savvy with a utilitarian survival-guide of sorts. His big advice is that “it’s better to be vaguely right than exactly wrong.” Success is being alert to important changes and being prepared to cope, not with having accurate predictions. Narrowing down the things that need to be prepared for is an important practical benefit. In that spirit, Gordon talks about the strengths and weaknesses of using multiple scenarios instead of pat forecasts. He steps the reader through the analysis of some forecasts while looking for weaknesses. A chapter-long battery of questions is offered to guide the analysis. These questions do a good job of summarising the book.

All told, Future Savvy is an excellent textbook for those who want to discipline the way they think about the future. I disagree with Gordon’s tangents about the inherently subjective nature of truth. I also have a few qualms about his take on scepticism. But these tangents rarely get in the way of his stock-taking exercise. That exercise has led me to be even more suspicious of forecasting, especially forecasts in volatile industries where data is patchy and assumptions are legion. I’d love to know the success rate of high-tech cheer-leaders … er, research firms that peddle forecasting numbers. Gordon dismisses the tracking of forecast failures as “smirk lists”. I’m with Taleb and his tsk tsking. If these numbers are just part of the hype machine and have a dismal track-record, then what good are they? Validation for reckless investment strategies? Fodder for misleading Power­Point slides? Numbers that give a false sense of being in-touch with the market? Tsk tsk.

That said, Future Savvy has increased my interest in foresight more generally. Gordon’s guide left me wondering how I can better prepare groups of decision-makers to think about the future. How do we get them to see the many changes afoot with greater foresight?

Drivers of Change 2009 by Chris Luebkeman et al. (Arup and Prestel Verlag, 2009), pp. 143 and 175 cards.

I recommend an unusual teaching aid. It’s a deck of recipe-­style cards called Drivers of Change. The cards were developed by the Global Foresight & Innovation unit of Arup, an international design and development firm. Each card is devoted to a societal or technological topic, such as obesity in the population, the desalination of water, and the financial services industry. There is a strong public policy and governance bent to the deck. A picture is found on the face of each card, along with a provocative question and an interesting factoid. Some elaborating research is reported on the back. The purpose of the deck is to help a group of people think about the future across a wide range of topics. A booklet accompanies the deck with some suggestions about uses. The big question asked: “What will the world look like in 2050?”

This is the second of three Drivers of Change decks that have been published. The original Drivers of Change 2006 deck (Arup, 2006) is a set of 50 cards organised into five suits: social, economic, technological, environmental, and political. The new deck is bigger (175 cards). The cards are organised into seven narrower suits: energy, waste, climate change, water, demographics, urbanisation, and poverty. The cards are further organised into five cross-cutting suits based on the original deck.

(There is a third deck, the SlimCity Knowledge Cards (2009), that was designed specifically for thinking about urban governance issues. It was distributed at the World Economic Forum in Davos in 2009. You can download a free copy by following these links.)

With a random pull from the card deck, I find the following: “SOCIAL/ENERGY. Personal Milage. How far do you roam? In 1950 the average Briton travelled 5 miles a day; today it’s more than 30 miles and forecast to double again in the next two decades.” On the back of the card is more information about personal mobility and modes of travel. I pull another card: “POLITICAL/URBANISATION. Planning Policies. Who are cities planned for? Every week for the next 30 years, the equivalent of a new 1M population city is needed to accommodate the doubling of the urban population of developing countries from 2bn to 4nb.” The back features information about housing availability and governance structures. Another card: “TECHNOLOGICAL/WATER. Distribution efficiency. “How many leaks are you paying for? In 1998, more than 600km of pipes in Berlin were inspected via video camera vehicles as part of routine maintenance. This preventive maintenance policy has reduced the percentage of unaccounted water by 5%.” The back has some more facts about water distribution efficiency. You get the general idea.

Over the last four years, I’ve been doing a lot of research on card-based dialogue methods. This research is part of my on-going efforts to improve adult learning and professional development. I have much to say about the benefits of these methods but that’ll have to wait for another time. For now, I’ll note four features.

• Communicating Ideas. Cards are an excellent way of communicating information in small, easy-to-digest bites. The messages written on them are necessarily brief and to-the-point, limited in length by the size of the card. Busy people may not set aside time to read a book but may not think twice about thumbing through a deck of cards. Reading doesn’t seem like a long slog. Yet a well written deck may contain as many substantive lessons as a book.
• Organising Ideas. Books present information in a sequential narrative. In contrast, cards can be sorted and shuffled. More than two pages worth of text can be displayed at a time. Irrelevant cards can be set aside. Cards can be arranged into patterns and diagrams. This flexibility allows people to juggle more ideas in the mind at one time and draw connections between ideas.
• Visualising Ideas. Most dialogue cards display an image, such as a photograph or icon, that illustrates or represents an idea. Many card decks are organised into colour- or symbol-coded suits. These visuals animate abstract ideas in the mind. They help people situate ideas in a context.
• Generating Dialogue. When a small group gets together to discuss a complicated matter, it is often difficult to get the conversation started. It’s just as hard to keep the conversation on track. Contrived exercises and overbearing facilitators may help but at the cost of unduly limiting the scope and depth of the discussion. Cards jog the memory and coax debate. They give the conversation something to focus on and build upon. Thus, self-directed dialogues can be made more productive.

I’ve run many card-base dialogue sessions. The level of engagement is remarkable. Indeed, over the last couple of years, I’ve been turning some of my own research into card decks for teaching purposes.

The Arup cards are an excellent tool for card-based dialogue. There are shortcomings, to be sure. Some of the forecasts reported are presented as uncontroversial factoids. Some of the questions don’t lend themselves to expansive discussions. There are recurrent themes in the deck that very much represent the current Zeitgeist and, therefore, the conversations generated are highly susceptible to the Zeitgeist bias. Nonetheless, the cards are well suited to provoke a lot of intelligent thought about the future.

The main benefit of Arup’s card deck is that it helps us be better ponderers. Most of us think of pondering as idle contemplation: being lost in thought; dreaming up possible futures. It can be that. But I think it’s also a useful skill that needs to be exercised regularly. For me, pondering means (a.) being able to recognise the significance of something in our lives today, even if that thing seems very banal on the surface. And (b.) asking insightful questions about how that thing will evolve in the future. How can it be made better? How can this change the way we live? What problems will it cause in the distant future? What causes this to happen? Who benefits from this? Visionary designers are always asking themselves questions like these. It’s an example we should follow if we aspire to have keen foresight.

The leadership literature has many sins to atone for. But the literature is right on at least one point. A truly visionary leader doesn’t have all the answers but knows how to ask insightful questions. Many executives think this means the ability to interrupt and intimidate others by asking probing and hectoring questions. Those deserving of the title “visionary”, however, are the ones who know how to ask questions that shed light on future possibilities … and help others ponder those possibilities too.

Review By Peter Stoyko

Update (29.07.10)

Adam Gordon has given my review an approving nod on his blog. I, in turn, give an approving nod to his blog, which is well worth following.

Gordon thinks I’m a bit tone-deaf when I accuse him of being agnostic about the ethics of leading forecasts. In Gordon’s words: “Agnostic? Moi? Hardly, but perhaps the chill of my irony was not chilly enough.” I’m all for clever irony over finger-wagging preach­iness. And I may be aloofly misconstruing the deadpan voice in the book. But, yes: not chilly enough.

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