Future Savvy: Quality in Foresight

I was lucky enough to have Future Savvy included in a lengthy review of critical thinking in forecasting & foresight, done on the SmithySmithy “info-savvy” blog. The post also included Nassim Taleb’s ‘The Black Swan’ (2007) and ‘Fooled By Randomness’ (2005); Kenneth Posner’s ‘Stalking the Black Swan’ (2010), and Chris Luebkeman’s Drivers of Change (2009).

As Stoyko’s is head-and-shoulders the most insightful and thorough assessments of the book itself, and the book in context, I’m reposting it here, with thanks. There are also fabulous graphics added, such as these (see more below):

“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. [Editor's note: Agnostic? Moi? Hardly, but perhaps the chill of my irony was not chilly enough.]

“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?”

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Successful people are considered to be better future prognosticators than average. Why? Because it is assumed they must have known something about the future at some previous point in order to become as successful as they are. (Unfortunately Taleb’s various injunctions as to the workings of randomness fall on deaf ears, as do Gladwell’s many observations as to the tricky relationship between cause and effect.)

In 1995, at the height of Microsoft’s power over the economy and the zeitgeist (before Google came into its own, before Apple renewed, etc.) Bill Gates wrote “The Road Ahead,” which was, as one would expect, a broadly techno-optimistic look at the future. Did it see 9/11? No. Iraq War 2? No. The Credit Crunch? No. For a start it only really thinks about digital technology, and that’s going to be a very partial guide to the road ahead, at best.

But, in a recent The Atlantic article, “Bill Gates: More Profit than Prophet,” Tom McNichol evaluates Gates’s foresight on its own terms. As reproduced below, he finds it more “miss” than “hit.”

In general, Gates makes the mistakes outlined in Future Savvy, particularly in predicting the future based on its technological possibility rather than economic or social practicality. He’s short on systemic/feedback thinking and therefore misses side effects and unintended consequences. He also falls into the wishful-thinking bias: mixing up what he and (and Microsoft business) would like the future to be with what it really will be.

This last factor is less a mistake than a classic tool of future advocacy, and Gates would no doubt admit to a bit of this. It is illuminating (and sobering for future predictors) to see how much of the digital future Microsoft had within in its area of control in 1995, which it ceded to others. That lowered Microsoft’s ability to influence the road ahead and therefore weakened Gates’ predictions.

The McNichol analysis (shortened in places):

E-Mail
Prediction: Gates wrote, “Electronic mail and shared screens will eliminate the need for many meetings. … when face-to-face meetings do take place, they will be more efficient because participants will have already exchanged background information by e-mail. … information overload is not unique to the (information) highway, and it needn’t be a problem.”
Verdict: Miss. Gates’s view of e-mail now seems naively Utopian, failing to account for unintended consequences. If anything, e-mail has made workplace meetings more frequent and less efficient. “Didn’t you get that e-mail?” is probably the single most common question posed at meetings, a query that often leads to … another meeting.

The Wallet PC
Prediction: “You’ll be able to carry the wallet PC in your pocket or purse. It will display messages and schedules and also let you read or send electronic mail and faxes, monitor weather and stock reports, play both simple and sophisticated games, browse information if you’re bored, or choose from among thousands of easy-to-call up photos of your kids.”
Verdict: Hit. Gates’s wallet PC is more or less today’s mobile smartphone with voice capability added.

Wireless Networks
Prediction: “The wireless networks of the future will be faster, but unless there is a major breakthrough, wired networks will have a far greater bandwidth. Mobile devices will be able to send and receive messages, but it will be expensive and unusual to use them to receive an individual video stream.”
Verdict: Miss. Today, receiving a wireless video stream is neither expensive nor unusual; in fact, it’s so commonplace that most people don’t give it a second thought. Gates failed to anticipate that wireless would become cheaper and faster, but his chief mistake was a common but flawed assumption among techno-futurists: that new technology is adopted chiefly on the basis of technological superiority rather than social factors.

Social Networking
Prediction: “The (information) highway will not only make it easier to keep up with distant friends, it will also enable us to find new companions. Friendships formed across the network will lead naturally to getting together in person.”
Verdict: Hit and Miss. One of the killer apps of the information highway has turned out to be social networking… But friendships formed online don’t regularly lead to face-to-face meetings. Far more common is the user with 250 Facebook friends, most of whom he rarely, if ever, sees in person.

Online Shopping
Prediction: “Because the information highway will carry video, you’ll often be able to see exactly what you’ve ordered. … you won’t have to wonder whether the flowers you ordered for your mother by telephone were really as stunning as you’d hoped. You’ll be able to watch the florist arrange the bouquet, change your mind if you want, and replace wilting roses with fresh anemones.”
Verdict: Miss. Gates was right that the information highway would carry video, but he completely misread the social and economic factors that would shape its use in online commerce. How on earth would a harried florist find the time to hold a videoconference with every customer who orders flowers for Mother’s Day? What company would absorb the colossal expense of having orders changed at the last second according to customers’ shifting whims? Gates’s vision of online shopping has turned out to be a lot like past predictions about personal jet packs and moving sidewalks: a future that’s technologically possible but socially and economically impractical.

Videoconferencing
Prediction: “Small video devices using cameras attached to personal computers or television sets will allow us to meet readily across the information highway with much higher quality pictures and sound for lower prices.”
Verdict: Hit. What came to be called webcams are standard issue on PCs, or can be purchased from Bill Gates’s favorite company for under $30.

The Internet and the Web
Prediction: Gates’s 286-page book mentions the World Wide Web on only four of its pages, and portrays the Internet as a subset of a much a larger “Information Superhighway.” … Verdict: Miss. Gates’s notion that the Internet would play a supporting role in the information highway of the future, rather than being the highway itself, was out-of-date the day The Road Ahead was published… and he made major revisions to a second edition of The Road Ahead, adding material that highlighted the significance of the Internet. In many ways, Gates’s cloudy crystal ball regarding the Internet amounted to wishful thinking. Gates built Microsoft into a global powerhouse by selling proprietary software that users loaded onto their PCs. He wasn’t likely to warm to the idea that the same functions could be delivered cheaper and faster through a decentralized network that he couldn’t control.

Privacy
Predication: “A decade from now, you may shake your head that there was ever a time when any stranger or wrong number could interrupt you at home with a phone call. … by explicitly indicating allowable interruptions, you will be able to establish your home — or anywhere you choose — as your sanctuary.”
Verdict: Little Hit, Big Miss. It’s true that technology lets you explicitly indicate allowable interruptions — you can use caller ID to dodge unwanted calls or sign up at the National Do Not Call Registry to nix telemarketers. But the notion that technology would pave the way to greater privacy has turned out to be anything but true.

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I had a chat the other day to Stephan Magus for his Abenteuer Zukunft (Future Adventures) podcast channel, taking about the rationale behind making a stand for quality in foresight. That is, what’s under the hood of Future Savvy, and why.

The podcast is up at the Abenteuer Leben site, playable via the buttons on the right hand side.

Alternatively it can be accessed directly at

http://media1.roadkast.com/abenteuerzukunft/DAZ71_120410_6tt6.mp3

(If you don’t speak German, you need to fast forward through the first 3 minutes.)

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Two running business stories with foresight importance this week, both I realize brought to me by smartbrief.com (Smartbrief on Leadership) which I find a very credible news aggregation service. The first is a WSJ piece ‘How Lean Manufacturing Can Backfire.’

Toyota President Akio Toyoda, Feb 11, 2010. Pic: AP

Lean manufacturing creates efficiencies and shaves production costs by creating just-in-time — no inventory — systems, using common parts and designs across product lines, and generally squeezing materials, processes, and (inevitably) quality controls. This may or may not include pressing suppliers to lower prices, and therefore squeeze their own materials, processes, and quality controls. ‘Lean’ has been very much a core process and operations mantra for about two decades. To misquote a favorite saying, manufacturing companies have been adamant: ‘one can never be too rich or too lean.’

But now Toyota has had a slew of embarrassing recalls — the 2010 Highlander; 2008 – 2010 Sequoia SUVs; and 2009 – 2010 RAV4′s due to gas pedal problems. It has just recalled 437,000 Prius and other hybrid vehicles worldwide to fix brake problems. In 2009 it recalled Corolla, Camry, Vios and Yaris sedans due to faulty electric window-control systems.

The point of the WSJ piece is to implicate lean manufacturing in this. (It’s unclear whether it’s too much lean or too little quality control, but they are clearly connected.) Now, lean as an idea is not going to go away. Nobody is suddenly going to advocate ‘bloat manufacturing,’ but looking at the damage in reputation and bottom line that Toyota has soaked up, the company and others like it will obviously looking across their lines and saying to themselves ‘a bit of redundancy (fat, if you like) in the system will be cheaper than this.’ Thus the pendulum swings back from lean extreme to somewhere a bit more durable. A happy medium.

Maharaj Mac

In the other story, the Times reports how McDonalds is seeing benefits from localization of it’s menu, for example, offering the McItaly in Italy, the (non-beef) Maharaja Mac in India, the McLobster in Canada and the Ebi Filit-O (shrimp burger) in Japan. The pendulum effect here is that McDo became the mega-corporation it is based on global standardization and a ‘one-menu’ mantra from Cleveland to Taipei. It wasn’t just one menu, but each item had to be produced from the same stock, and in the same way. McDo fries were identical everywhere, that was the guarantee (and they were always called ‘fries’ no matter what locals called them.)

It is now become common cause among the global food companies (notably Starbucks and KFC) to work local options into their offering. One may think this is merely ‘think global, act local.’ The point is, it is an about-turn indeed from the ‘think American, act global’ that went before. What works best is in fact a happy medium.

What does this have to do with better future-thinking? Expect a recall sooner or later on forecasts that don’t see change resolving itself around a happy medium.

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Economists make a handy, if mildly irreverent, distinction between “freshwater” and “saltwater” economics. Freshwater refers to economic theory that rests on the efficient markets hypothesis — a belief in the efficiency and rationality of free markets. It is associated with Milton Friedman and the University of Chicago school. It was the thinking behind Thatcher and Reaganomics and still more-or-less holds sway today, or it did up until the credit crunch.

Keynesian or saltwater economics by contrast holds that free markets often behave irrationally and inefficiently, and therefore need corrective policy from government. Saltwater economists say people and institutions often behave in ways contrary to the general good, or in ways that can bring markets (on which they depend) to their knees. Sound familiar?

Anyway, a recent Knowledge@Wharton article comments: “Like a natural science, freshwater economics lends itself to complex, often elegant mathematical modeling. The freshwater view is that consumers, offered an array of choices, will select the one that is best for them — a straightforward assertion that can be neatly expressed in mathematical formulae.

“In contrast, many assertions made in behavioral economics are more challenging to express mathematically. ‘Behavioralists’ argue that consumers don’t always act in their own interests, especially when they fail to understand the choices on offer or succumb to irrational impulses involving those choices… but such impulses are inherently vague and difficult to define.”

Cognitive bias

In other words mathematically modeling the economic future is possible if humans and the markets they create are rational, but far less possible if we act irrationally.

Now, as elaborated in Future Savvy, the fact that humans make irrational choices due to many cognitive biases and heuristics  is indisputable, not least since the work of  Tversky and Kahneman. Biases and heuristics such as “anchoring,” “recency effect,” “personal validation fallacy,” “herd mentality,” and so on, in which people make irrational choices, are well documented.

That’s why mathematical projections of economic behavior are unreliable. The economy may be counted in numbers, but it is still a human system, with associated inefficiency and irrationality. Blow this little debate in economic forecasting up large, and you have the essential problem with quantitative forecasting of any type. It assumes, erroneously, a freshwater view of humanity.

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We owe a debt to Nassim Taleb for memorably encapsulating the demerits of predicting by extrapolating trends as “The Turkey Problem,” and now seems the moment to reiterate it:

Imagine you are a turkey. Every day someone comes to feed you. Every day you get bigger. Your portion sizes get bigger too, brought by a nice man at regular intervals. You extrapolate the trend and you confidently predict a bigger you, with more to eat. Regularly too.

But what happens is … Thanksgiving. Or Christmas

Taleb, N., The Fourth Quadrant: a Map of the Limits of Statistics, Edge Foundation, September 2008

The hard reality for those who predict the future by extrapolating trends (and those gullible enough to believe them) is that even if our turkey had excellent data points (carefully observed and accurately recorded in, for example, a time series analysis) and, moreover, even if our turkey was a mathematically sophisticated — not merely simply projecting trends, but applying all the latest modeling techniques, from moving averages to compound regression — he is still going to be wrong about the future. Dead wrong.

All the data analysis in the world, all the fancy computer software, all the consulting time paid for, and he is still a dead duck.

Ouch. The lesson: there may be (or, vexingly, may not be) something outside the trend, a framing condition, which where it does exist is invisible within the trend projector’s mental model. The only way to get a view of the future that is “robust to Thanksgiving” is (a) to question assumed framing conditions, for example through properly done scenarios, and (b) to hold a view of the future which assumes fundamental ‘game-changing’ surprises can and will occur.

If, as they say, “the trend is your friend” it is assuredly only your fair-weather friend.

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In a recent Times article ‘The future was never going to be the C5‘ actor-comedian Ben Millar offers a familiar criticism of foresight work. Inter alia he says: “For all our achievements in art, science, and technology, the human race has always been spectacularly bad at predicting the future. Literature is littered with shockingly wide-of-the-mark utopias, dystopias, shiny suits, flying saucers and whole meals contained in a single pill. As a child of the Seventies, I was taught that as an adult in a world run by machines my main challenge would be how to spend my endless hours of leisure time…”

Yes, Ben. I’m sure you know this has all been said before ad nauseam. But more importantly, 40 years on many lessons have been learned, and it wouldn’t run foul of quality journalism standards to reflect this.

First, let’s be clear: nobody can predict the future. Anyone who says they can is a charlatan. Also, yes, unconscionably dreadful and irresponsible predictions have been made and are continually being made. But there are three problems with the ‘no-flying-car-so-there-we-can’t-predict-the-future’ argument:

(1) The kinds of predictions Millar cites are a product of a particular moment in Western thought and therefore foresight. The 1960s and early 70s were a time of Post-War American emergence, unleashing for a while a techno-futurist predictive rapture, most of which has indeed proved to be rubbish. There are still people, very famous talking-head futurists, promoting techno-rapture for the 21st century (caveat emptor) but as a whole the foresight field has moved on to become much more circumspect about what can be predicted.

Balancing techno-fantasy

Foresight practitioners are these days more likely to balance technology wowee with economic, social, and environmental friction; see systemic (often indirect or counter-intuitive) effects where once only simple cause-and-effect was seen; and create scenarios of key alternative outcomes rather than predict one.

(2) The second thing that is missed in gleefully deriding foresight work, is how many people and institutions get it right, or right enough.  It’s axiomatic that in order to be successful a person or organization must have correctly assessed both key changes and rate of change in their operating environment. To take a famous case, as quoted in Future Savvy, while Nixon’s Vice President Spiro Agnew in 1972 erroneously forecast super-sonic passenger air travel, Herb Kelleher, founder of SouthWest Airlines, foresaw the low-cost air travel industry. Bingo. Billionaire. Similarly, behind every success one can find future thinking that, while sometimes latent, was present and correct.

(3) The purpose of foresight work is misunderstood. We cannot predict the future and it’s pointless to try. We can only assess signals of change, trends, and potential for surprises and reversals, including challenging our all-too-easily calcified mental models, and take this into a process of understanding alternative outcomes and pre-considering best strategic actions. In other words, actively stimulating the investigation and analysis of future conditions in order to create the basis of better decision-making today.

In fact sometimes the ‘strategic conversation’ that results from poor predictions is instructive to managers. As I say to clients: the goal of foresight work is better decisions not better predictions.

Back-street abortionists

The reality is that there is good and bad foresight work. Yes, some futurists are the technical and moral equivalent of back street abortionists. But the good work remains, and quality foresight is a critical advantage to decision-makers. The key thing is to be able to tell good foresight work from bad.

Simplistic trashing of foresight work en bloc ignores the weight of case evidence that people and organizations can improve their management of future uncertainty and/or create a situation where they manage the future better than competitors. Further, it encourages managers to fly blind into changing environments, often resulting in spectacularly poor decisions that deeply and widely punish their dependent stakeholders.

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Continuing the theme of financial types talking to each other about predictions and predictability, this ‘Tea with the Economist’ interview of Stephen Roach, Chairman, Morgan Stanley Asia by Economist New York Bureau Chief Mathew Birk, carries interesting lessons about the limits of prediction.

Birk commends Roach for being one of the few to have predicted the Credit Crunch problems, to which Roach demurs in saying he was “too early”. He then furthers his modesty in saying that the “breakage” in the financial system was “in excess of anything I envisioned.”

Self-deprecation in assessing one’s predictive abilities will endear anyone to me. Even Roach, who later in the interview burns this hard-won credibility by laying the blame for the credit crunch at the door of regulators, forgetting how hard financial institutions lobbied regulators for greater freedoms in the 1990s.

But I digress. The predictive issues the interview raises are as follows. Issue one: it’s not enough (as any stock short-seller will confirm) to get the direction of a future change right. One must get the timing right too. Issue two: it’s not enough to anticipate a change. One must be able to judge it’s impact. Getting either timing or impact wrong is effectively to have missed the future.

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Probability

On the latter topic — the problem of impact — Nassim Taleb is unrelenting, and he is right. Analysts routinely mix up probability and impact. They think that because an event has a low probability (‘it would be a 10-sigma event!’) it can be marginalized in the predictive number crunching. Of course, it can’t. The low-probability of a wildcard or black swan event is irrelevant because when it happens it will change the game, and that’s why, in every predictive situation of reasonable complexity and uncertainty, using statistical extrapolations (regressions and so on) to predict, is to dangerously paper over the cracks. It is precisely the cracks that businesses and policy makers need to worry about.

Determining the direction of change is hard enough. Assessing timing or extent of impact — a ‘total future impact index’ — is wickedly difficult. It’s a task not to be underestimated, and to simply extrapolate current trends (= assuming the trend’s timeline and impact stay the same as in the past) is the royal road to underestimating it.

This is the reason foresight for complex, uncertain, changing situations can only be grasped by NOT predicting (quantitatively or otherwise) but by exploring the limit-conditions of the plausible (What would happen if the timing of the change accelerated, or was significantly delayed? What if  the impact was 10x or one tenth of what we expect? And so on.)

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One of the benefits of scenario-based future thinking is the ‘permission’ to think through alternative future outcomes without necessarily predicting them. ‘Predictors’ focus, by contrast, on isolating the highest probability future in order not to have to think through or plan for less likely outcomes.

Predictions of the dollar’s demise are as old as the greenback itself of course, but over recent weeks the specter of the dollar heading way way below its trading range — a dollar crunch — has entered the zone of the credible, or, in scenario terms, the ‘cone of plausible uncertainty.’ That means decision-makers with lots at stake are taking it seriously.

Like the British pound, the dollar has been under a cloud due to perceptions of economic fallout from the credit crunch and global recession, but particular questions about the US currency have recently surfaced, driven by reports [Robert Fisk's 'The Demise of the Dollar' story in The Independent (Oct 6)]  that “Gulf Arabs are planning – along with China, Russia, Japan and France – to end dollar dealings for oil, moving instead to a basket of currencies including the Japanese yen and Chinese yuan, the euro, gold and a new, unified currency planned for nations in the Gulf Co-operation Council” (Saudi Arabia, Abu Dhabi, Kuwait and Qatar).

The subtext is far from merely financial. Practically, it would mean that on any day, the real cost of oil to US consumers and businesses would go up or down depending on the strength of the currency. This is something America is not used to. But, more deeeply, dropping dollar-denomination of oil is a direct shot across the bows of Washington’s say over oil affairs, and the hegemony of the dollar as the dominant global reserve currency.

De-dollarizing oil would not in itself push the US currency below its 25-year range. But it is portentous of the clear trend to a genuinely multi-power world, for better or worse, in which the dollar will get no favors. That will push the dollar down, at least while the news and fallout make their way through the financial and real economic systems.

Rumors of de-dollarization have been hotly denied, as further reported here, but as the Independent points out, denials are to be expected, and are always issued in these situations. They mean nothing. Even cub reporters know that.

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Scenario thinking

What’s particularly interesting to me is that a ‘scenario’ of dollar demise has become not only plausible in the mainstream view of the future, but scenario thinking is being used as a way to consider the nature of this outcome, and how best to respond without predicting the outcome either way. As recently as directly pre-credit crunch, the media question would have been: ‘what is the best prediction for the dollar (or the housing market, or credit default swaps?) and that, rather then scoping out the implications of the lesser-likelihood, would have dominated the discussion.

So, what struck me forcefully in the Business Week video interview above, where BW Chief Economist Mike Mandel interviews the news magazine’s Economics Editor Peter Coy (see Coy’s underlying story here), is how the less-likely, non-predicted, but very significant outcome is actively addressed:

Says Coy: “It’s so hard to know what the dollar is going to do. We don’t argue that we know… what we do is we say, ‘it could happen’ and let’s take that possibility seriously, in the same way we should have taken the possibility of falling housing prices seriously…”

This is not formal scenario-building of course. But it is, fundamentally an adoption of the framework, saying in the classic ‘scenarios’ way: “we can’t predict if it will happen or it won’t, but if it does it will have significant impact. So let’s just ask: ‘what if ‘ it does and explore the outcomes and our responses. What will the word look like? What would be the implications, the knock-ons and spinoffs? If it comes to pass, what would be wish we had done today?”

Perhaps failing to predict the credit crunch has dented predictors’ halos enough to cause a mini-zeitgeist-shift towards the only real way to cope with important uncertainty: exploring all outcomes that pass the plausibility and significance test, whether or not we actually believe they will happen.

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The sustained market rally, with stocks up over 40% on average since the lows in March 2009 (The Dow Jones Industrial Average was about 6,500 in March 09; it is now about 9,500) is taken to be a forecast that real future economic recovery is on the horizon. But is the market a reliable forecaster of anything? That is, from the perspective of real industry and strategic foresight professionals, using hard-won, battle-tested approaches to anticipating future outcomes, should we factor the market’s direction into our expectations of the economic future?

DJIA since Sept '08

The answer is, broadly, yes. Stocks are shares in the future earnings of a company. They are therefore a “bet” on (er, an “investment” in) the future performance of a company, or many companies. The trading price on any day is the price at which there are as many buyers as sellers for these future returns. Rising prices mean there are more buyers than sellers, that means general expectation of future profits is going up. Investors are putting a higher price on the future.

The market is therefore considered a leading indicator of economic conditions. (By contrast, employment figures are lagging indicators — due frictional forces, not to mention morality, it takes companies a while to downsize in recessions or upscale in booms, so employment levels track economic conditions but with a delay.)

But how valid and dependable is the market as a leading indicator? It is also apparent that markets move up slowly and steadily, but fall in a hurry. So the downward move can hardly be held to be predictive. But the upward move appears to hold some weight as harbinger of better times. How much weight?

What’s particularly important is that the aggregate insight into future returns from shareholding investments — across many investors and many stocks — cancels out individual errors. Any one person may have a dumb idea of the ‘future cash flows’ from one or many companies, and the price of any one company may be unreliable for innumerable reasons, including fraud, but the knowledge and intelligence of hundreds of thousands of people, when aggregated and spread over many thousands of stocks, corrects for all these errors. It becomes robust.

Prediction Markets

This reliability of shared, aggregated insight — the wisdom of crowds — is precisely what makes ‘prediction markets’ such a powerful forecasting tool, as I have mentioned in previous posts. (Prediction markets apply market-like wisdom to create foresight in areas that are not normally ‘tradeable.’) Any one person will, as likely as not, get it wrong, but everyone together, rather astoundingly, get it right.

Ironically, crowd wisdom is much more reliable than the technical forecasting models that investment institutions use to try to determine how business, macroeconomic, interest rate, or other conditions will affect future stock prices. These predictions, based on the assumptions of a handful of model programmers and/or model users, are deeply vulnerable because there is no crowd-wisdom balance. It’s no better than reading tea leaves, only apparently (and unaccountably) more respectable.

Having said all this, it is well known that the ‘crowd,’ aka the ‘herd’ can and do all get it wrong together. This is what happens in price bubbles, or panic market exits, with everyone buying or selling because they are making the same wrong assumptions, or just doing what everyone else appears to be doing. (Most players making the same mistake together is the basic problem when prediction markets fail too.)

However, what is clear is this case is there was a very hard sell-off in the months prior to March 09, following revelations of the gravity of the Credit Crunch, but that this has slide has been arrested and mostly reversed. This says that innumerable smart people with, collectively, billions of dollars at stake, are expecting future profits higher than they did in March. That’s a prediction one can rely on.

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