Martin Sheen as Josiah 'Jed' Bartlet. Picture: @Pres_Bartlet

Even by the standards of modern political media prattle, this was odd: the Guardian yesterday invited and ran a “response” to Barak Obama’s State of the Union address, from Jed Bartlet the fictional president in The West Wing.

One should immediately add that the response was not that of Martin Sheen (the actor who played Bartlet) or anyone from the show. It was that of an unnamed tweeter who can be found here.

The reader vox-pop box was quick to cry foul, asking what next: a piece on space exploration by Captain Jean-Luc Picard, or 007’s analysis of the War on Terror?

Fair enough. But if there is a serious point to be made, and I think there is, it is that fictional leaders do have a role in real world business and policy leadership.

Fiction and storytelling is important and enduring in all human societies because it is an excellent vehicle for considering complex human situations, reflecting on competing motivations and interpretations, assessing choices made with incomplete information, and following these through to their win-or-lose conclusion. Fiction allows multifaceted situations to be captured without losing the complexity.

Parallels

Incidentally, this is why scenario method, which tells stories of alternative future situations, is such an effective planning device. But the point here is that fiction captures complex human situations and senior executives would be the first to recognize parallels between the challenges that imaginary leaders are put through and what they do in a real working day.

If fiction captures and communicates tricky situations well, it therein becomes a learning vehicle. Whether reading a difficult modern novel or watching a soapy TV show, we put ourselves in others’ shoes, vicariously experiencing their conundrums and learning from the outcomes of their decisions.

Would-be successful leaders could do worse than take note of the leadership attributes of winners such as Sherlock Holmes or Superman or Andy Dufresne; or unpick the illusions and ultimate failures of dark lords such as Voldemort or Mr Kurtz.

Furthermore, a good way to learn is to judge real performance against an ideal template. (Judging me against my clarinet teacher, for example.) Whether your politics aligns with the positions and preferences of The West Wing White House or not, there is no denying that Bartlet is set up as a model president in a model administration. He is thoughtful, caring, effective; manifests an ideal balance of intellect, EQ, and decisiveness; is respected and loved by his staff who will go to the ends of the earth for him. He is a template leader.

So it’s hardly off-the-wall to wonder what Bartlet would have made of Obama 2012. That said, it would have been far more interesting to know what West Wing screenwriter Aaron Sorkin or even Sheen, rather than abitrary unnamed tweeter, thought of the State of the Union address.

For the record:

The Lion: President Obama. Mangy, patchy, apparently underfed. Definitely caged. But he has a heart. Whether it is the lion heart of the ruler of Narnia … time will tell.

The Witch: Here we have to go with Shakespeare; in fact there are three witches: Romney, Gingrich, Santorum. On Tuesday Obama called for a fairer country. Notice they responded: fair is foul, and foul is fair.

The Warmonger: he that exited the presidency in 2008, having wasted 4,000 lives and $800,000,000,000 on a war as poorly judged as that of Douglas Haig at Somme, 1916.

A fun and instructive example is the ongoing developments in Exhibition Road, a kind of ‘museum mile’ in London, where the distinction between road and sidewalk is being abolished to make way for a car, bike, and pedestrian free-for-all.

Have the city’s planning wonks finally, truly, verifiably gone mad?

Since the automobile first reared its fearsome fender, road-management wisdom has always been that pedestrians are safest when kept separate from 5,000lb of moving metal.

Evangelists for livable urban areas usually clamor for pedestrian-only streets; or failing that, bigger, better-marked walking, running, and cycle lanes from which drivers are banned.

But pedestrian-car segregation has its own systemic effect. It means drivers are less likely to expect people in front of them, and so less likely to be vigilant and more likely to speed.

Exhibition Road planners say making the street a mixed area makes drivers anticipate something crossing their paths at all times.

Monderman

The mixed-use-street idea is not new: it was pioneered by town planner and traffic engineer Hans Monderman in the Netherlands in the 1980s and 1990s. According to a Guardian obituary, Monderman “succeeded in challenging many long-established assumptions about safety and the relationship between pedestrians and traffic…

“Monderman pioneered an approach that respected the driver’s common sense and intelligence instead of reliance on signs, road markings, traffic signals and physical barriers. He recognised that increasing control and regulation by the state reduced individual and collective responsibility.”

The jury is out on how effective mixed-use streets are; or exactly where they are most effective.

But the leadership lesson is clear: all decisions and resulting directives rest on foundational assumptions. The more robust these underlying assumptions, the better the decisions.

In this case, the assumption that greater safety is achieved by separation of vehicle and pedestrian is being challenged, and may turn out not to hold up at all for specific city areas.

Where assumptions are weak — or become weak over time due to changes in technology or values or market needs — poor decisions follow.

Leaders who don’t identify and regularly revisit the assumptions that underly their past decisions abdicate the ability to manage reversals and transitions when required. And will be surprised and blindsided when others initiate them.

Fukushima plant, Japan. Picture: digitalglobe.com

At the time of writing, Japan is battling a nuclear meltdown and radiation emergency, and Fukushima could become a word suddenly the whole world knows, like Chernobyl.

Bloomberg News has called the whole tsunami crisis Naoto Kan’s “Katrina moment,” and one can only hope and pray for all concerned that the Japanese prime minister is a more competent leader than Bush was at this moment of human catastrophe.

As to the nuclear meltdown: If ever we have been warned about anything in the future, we have been warned about nuclear plant catastrophes. Not only have there been, as it were, verbal warnings going all the way back to the 1950s, but real-world events such as Three-Mile-Island and Chernobyl have fully fleshed out the scenario of nuclear reactor failure or near failure in populated areas.

If nuclear-generated electricity makes sense anywhere, it makes sense in Japan, which famously has no coal or gas reserves. But these are nuclear plants … built right on the Pacific Ring of Fire? Japan is a small island with 125 million people densely packed into urban areas. As we face the possibility of this many people put at risk, however the next few days play out it’s clear the risk and reward of nuclear energy here is out of alignment.

This is hardly news. The question is, why are the plants are there? And the answer is not a simple one of collusion or corruption of government, or shenanigans of power companies, although there may be some of that. It comes down to a misapprehension of probability and risk among leaders and decision-makers such that it appears that risk and reward are in balance, when in fact they are not.

Year 869AD

To think about this, consider yesterday’s BBC Story: Japan tsunami ‘could be 1,000-year event,” saying last week’s tidal wave was equivalent to a giant wave that hit the Sendai coast in 869AD. The report says: ”It is not unusual for undersea earthquakes to generate tsunamis in this part of Japan. Offshore quakes in the 19th and 20th centuries also caused large walls of water to hit this area of coastline. But previous research by a Japanese team shows that (only) in the 869 ‘Jogan’ disaster, tsunami waters moved some 4km inland, causing widespread flooding.”

The point is, tsunamis are common, but “the big one” is a one-in-thousand year event — an extremely low probability outcome.

Here I’m strongly reminded of the days following the depth of the Credit Crunch, Bear Stearns’ collapse, and general world financial system meltdown of 2008. If bankers said one thing sensible through the whole period it was: “this was a one-in-ten-(hundred, etc.)-thousand probability outcome, and extreme ‘outlier’ event!”

A low-probability event means we can relax, right? Wrong. The problem is probability says zilch about impact. “Wild Cards,” or now more famously in Nassim Taleb’s terms, “Black Swan” events are low probability but of game-changing impact.

Taleb’s point, made repeatedly across his various books and articles, is that standard probability theory and Gaussian statistics lull analysts into thinking that because an event is low probability – an outlier in a normal bell-curve distribution – it is of low or lower consequence.

Ignoring the tail of the Bell Curve is okay if events are genuinely assessed as low impact. If they are high-impact aka “fat-tailed” events, they are the most important events we face in the future, in building or maintaining any system or organization.

A probabilistic framework misleads decision-makers because it degrades their attention to crucial events (by tagging them low-probability,) which means next thing they are betting banks on mortgage-backed securities, or building nuclear plants on earthquake fault lines.

While I was there I couldn’t help noticing the business print and radio waves being dominated by the potential entrance of Wal-Mart, with all the jitters of local businesses considering the knock-ons and side-effects of the “über cost competitor” turning up at the end of the street.

If it goes ahead, Wal-Mart will enter via acquisition of local retailer Massmart which is, as the name suggests, a copy-cat company anyway, so it would seem all there is to talk about is price. As things stand, Wal-Mart is in its fifth week of due diligence on Massmart, currently visiting all 288 stores under acquisition, according to a recent WSJ report.

Now Wal-Mart is not busting a gut for the SA market, population 45 million, of course. The whole project is about using the South African operation as gateway into Africa as a whole. It is bet on the 5-to-10-year-and-beyond future of sub-Saharan Africa.

Massmart Chief Executive Grant Pattison is quoted as saying “you have to take the long view on Africa,” and this is exactly what Wal-Mart is doing. Enacting a long forward play for the newly strengthening African retail market.

Other than inventing the scale-based supply-chain-squeeze model of retail, which must go down as one of the great business innovations of all time, Wal-Mart is hardly known as a foresight-based player. As forward looking as the Massmart acquisition is, Wal-Mart has in fact been well beaten to the African punch by the Chinese who have been investing across the continent over the past decade (although the Chinese investment has been predominantly in infrastructure and resources, while Wal-Mart’s would be in anticipation of lower-middle class consumer enrichment on the back of that.)

The glass half full
The Chinese invasion is by far the biggest thing to happen in African economies since European colonialism, not only due to widespread infrastructural investment, and not only because it comes without “Washington Consensus” strings attached, but, even more fundamentally, because it is driving a zeitgest shift in business confidence. Deep problems remain, but suddenly the glass that was half empty appears half full, particularly to occidentals.

One expression of the new half-full perspective is McKinsey’s breathless report (June 2010) on Africa’s economic emergence, entitled “Lions on the Move,” which starts: “Africa’s collective economy grew very little during the last two decades of the 20th century. But sometime in the late 1990s, the continent began to stir. GDP growth picked up and bounded ahead…”

Asian Tigers. African Lions. Geddit? But when both Wal-Mart and McKinsey are setting their watches to the near-term future African economic growth story, you can bet other companies are set to pounce too.

Truth be told, the foresight field is littered with predictions about the future of the automobile, from the futurists’ flying car that never happened to the-pumps-run-dry doomsday, and everything inbetween.

The Xiao EN-V concept car. Photograph © General Motors / Wieck Media Services Inc

But, judging by the interview, Burns has a higher-quality foresight view of this industry than most, and this because he prioritizes what consumers really value as a guide to what will emerge over any policy principle or ideological interest.

What do consumers really value? “There’s nothing like the freedom they (cars) provide to let us go where we want, when we want, with the people we want to travel with,” says Burns.

“Ever since people could walk, the ability to move when they want and where they want is something people have found very compelling.”

Nothing new, but what he is warding off, in preparing the ground to looking to the industry future, is views of the automotive future that are ideologically colored, particularly those imbued with the virtues of public transport.

Says Burns, “Three major impediments get in the way of public transportation:

• The first is routes. A public transportation system can’t go everywhere, so people have to have a way to get to and from the stations.
• The second is schedules. You can’t leave exactly when you want to, so you have to arrive before the public transit system arrives to pick you up, which has major impacts on how people schedule their lives. And unfortunately, those schedules aren’t always predictable, so you have to buffer.
• The third is that since people have to shift modes from how they get to the station — whether it’s in cars, on scooters, or on bicycles — to the public transport mode, you create a need for parking.”

This balance could change — this is what public transport executives seek to effect. But until there is clear reason to see public-transport pain-points diminishing, there’s no reason to see anything but private-dominated transport in the future (other than very dense urban environments such as Manhattan.)

Pain avoidance

Burns places automotive foresight at the intellectual crossroads between what the majority of consumers really want (or what pain they want to avoid) and what pundits and ideologues think would be a better solution. Guess which always wins?

With that issue solved, the question then turns to what these private vehicles are exactly? Here Burns and co-authors have a vision, but it is more “anybody’s guess.” Their fundamental assumptions is that onboard inter-vehicle accident-avoidance technology is watertight, which means cars don’t need all their defensive armour and can so be far lighter, and therefore use less energy, so battery power and life is no longer the limiting issue it is today. See the concept-car above.

This blog first posted at Forbes Leadership: http://blogs.forbes.com/adamgordon

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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?”

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.)

So may I say, this is what I was trying to say: When trying to predict the future, watch for dog poop!

Or perhaps: apparently helpful guides to the future are often dog poop disguised as chocolate, and here’s how to know the difference.

Something like that.

Note that this Greenbang story, below, is damaged by letting the most extreme predictions (the howlers) stand in for the general item. Prediction howler-spotting is sobering, but misses how many people got the future right, or right enough to make excellent decisions, and therefore overly damages the foresight field.

Also, howlers are actually the low-hanging fruit. Being future savvy is ultimately about the more subtle job of correcting weighing apparently very credible and well-founded predictions, some of which are excellent, but others of which are far flimsier than they appear.

There are various other minor problems such as not knowing the difference between the Gartner Hype Cycle and Zeitgeist bias, etc. And I would never call myself, not even in my most self-deprecating moments, a “futurologist.” But anyway, as I said, just a bit of fun:

Greenbang (13th April 2010) by Trish Lorenz & Martin Wright: Prediction is very difficult, especially about the future.” Niels Bohr’s words are a wise warning to reckless forecasters.

“Combining a nuclear reactor with a home boiler is no longer a problem. It would heat and cool the house, provide unlimited hot water and melt the snow from sidewalks and driveways. All that could be done for six years on a single charge of fissionable material costing about $300.” — Robert Ferry, US Institute of Boiler and Radiator Manufacturers, 1955

“Nuclear-powered vacuum cleaners will probably be a reality in ten years.” — Alex Lewyt, President of vacuum cleaner company Lewyt Corp, also 1955

Lewyt and Ferry both stumbled into a risky habit of all amateur futurists: extrapolating from present trends. In this case, they were caught up in the surge of excitement over the rise of nuclear power. They were not alone. In the tech-fuelled optimism of the ’50s, magazines, radio and the infant TV were buzzing with predictions of flying cars and lunar settlements.

They had fallen victim to what later became known as the Gartner Hype Cycle. This maps the enthusiasm and subsequent disillusionment typical in the introduction of new technology — a useful reality check for those caught up in “irrational optimism.”

By contrast, there are those whose feet are too firmly rooted in present realities, and fail to see how innovation can combine with social changes to speed the widespread adoption of new technology.

“The Americans need the telephone, but we do not. We have plenty of messenger boys.” — Sir William Preece, Chief Engineer, Royal Mail, 1878

“The horse is here to stay, but the automobile is only a novelty, a fad.” — President of the Michigan Savings Bank, advising Henry Ford’s lawyer not to invest in Ford Motors, 1903

It is difficult to consider any factor that doesn’t apparently exist at the time of making a prediction, but that’s essentially what looking ahead requires. It wasn’t all that long ago when people were predicting a bright future for teletext and fax machines. Few would have anticipated that both would be made almost obsolete by the internet and email. And yet the weak signals were there for those who chose to hear them. A fax machine, after all, is simply a modem with a rather complex print interface attached. It only evolved as it did because people were unused to reading information solely on screen, and computers were too big to carry around with them. Once laptops took off in the early ’90s, the fax was doomed.

“There is no reason why anyone would want a computer in their home.” — Ken Olson, Chairman, Digital Equipment Corp, 1977

Australian Senator Dr Russell Trood sums it up neatly when he says: ” ‘Nowism’ is a serious occupational hazard for those in the prediction game.”

Today’s futurologists no longer try to predict a single outcome for the future; instead they map a variety of scenarios. For Adam Gordon of Future Savvy, scenario-based thinking gives people “permission to think through alternative outcomes without necessarily predicting them.” Instead of trying to forecast precisely what might happen, he says, “we can ask ‘What if it does?,’ and then explore the outcomes and our responses.” Such thinking characterises much of the strategy adopted by forward-looking governments on tackling climate change.

James Goodman, head of Futures at Forum for the Future, agrees: “People think it’s the output that’s important, but actually it’s the process.” And, he adds, “All future planning has uncertainty at its heart.”

Or as Martin Raymond, Strategy and Insight Director at The Future Laboratory, says, “We always try to spot the dog
poop in our forecast.”

Greenbang Editor’s note: This was a guest article by Trish Lorenz and Martin Wright at Forum for the Future. This piece originally appeared in Green Futures, which is published by Forum for the Future and is the leading magazine on environmental solutions and sustainable futures. Its aim is to demonstrate that a sustainable future is both practical and desirable — and can be profitable, too.

Reading analysis by John Tamny in Forbes, I lighted on the following paragraph — as perfect an encapsulation of why looking to the future in timely and in a high-quality way is essential, and how quality horizon scanning is integral to it:

“As often happens as companies grow, Blockbuster concentrated on perfecting its existing service while beating competitors offering the same instead of looking into ways that outsiders might destroy its business model altogether… For Blockbuster, the “disrupter” in question was Netflix. Indeed, popular as the Blockbuster brand was, getting to the video store in order to take advantage of its services was a hassle for customers–as was returning videos on time to avoid paying late fees. The rise of Netflix from well outside the traditional retail space meant these problems were solved in one fell swoop.” (my italics)

Change that matters, that is, relatively sudden and acutely disruptive to incumbent business-model success, always comes from outside an industry. Britannica wasn’t beaten by another encyclopedia. Eastman Kodak was beaten by digital photo startups, not by Fuji. And so on, and so on, through industry failure, whether it leads merely to value hemorrhage or all the way to Chapter 11.

Looking vs seeing

Sure there are companies that lose because they are simply outcompeted, that is, are less capable than the competition in doing the same thing. Hertz is currently in this category. But when a clear market leader, with brand and capital and customers galore comes totally unstuck, it is always new technology and/or new business model coming from the outside that has done it. In these cases, as with Blockbuster, companies fall to industry entrants that change ways of doing things, solving pain or trade-offs that buyers suffer, or otherwise provide consumers with more value.

These are always, theoretically, innovations incumbents could have done themselves if they were ready to think ahead (and brave enough, when required, to cannabalize existing products that stood in the way of important future steps) and therein lies a conundrum about looking at new, external competitors. It’s seldom that the incumbent can’t see the intruder, that is, is not looking. Often they are looking intensively. It is that they don’t see the absolute disruption in the new until it is too late. It is a problem of perception. This is why industry horizon scanning is a little about the easy task of looking, and a lot about the much harder job of seeing. And why putting one’s corporate head down and making an existing product or service ‘more perfect’ is part of not seeing.

* Interestingly, the Blockbuster demise was called exactly right in November 2007 by Don Reisinger on CNET.

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.

So this stimulates me to put down a few thoughts about risk assessment and its relationship with industry and strategic foresight as a whole. This is a big topic of course, but seeing as the categories are confused a lot, it’s worth tackling even if just in summary terms.

When I reach the topic of Risk Assessment in my ‘Industry Foresight and Business Future Strategy’ MBA elective, I use the ‘Adidas-Salomon: Incorporating Risk into Corporate Strategy’ mini-case [Ref: ICFAI 304-141-1; sourced via Cranfield’s Case Clearing house.]

The case is a useful baseline in risk assessment because it describes the various risks a multinational company typically faces: marketing risks (market change, brand image); operations risks (quality; reliability of processes and suppliers); social & environmental risks (workforce & natural resources compliance); legal (liability, regulation, patent); information technology (compromise or disruption); and financial risks (currency, interest rate, credit).

Business disruptors
In sum these are the things that could damage or disrupt the business. Isolating such factors, keeping vigilance over them, and having thought through or enacted counter-measures in advance, allows the organization to better control or reduce the impact should risk become reality.

All risks are future events, so a risk assessment is undoubtedly a future study, but assuming a company looks diligently across all these categories for potential and emerging hazards, how prepared is it for a changing world? What kind of industry foresight does this give managers? Is a risk assessment a futures assessment?

The obvious first answer is that a risk assessment is only half the equation. It’s oriented to the downside potential of changes not the upside; looking for threats not opportunities. Obviously that means that opportunities are less likely to be identified.

The second thing is that a standard risk assessment operates in the realm of known risks, in known categories, that may cause disruption and damage in a known way. It doesn’t have the mechanism to expand conceptions of what could go wrong, or how it could go wrong, or what the full knock-on effects will be. The types of mental-model-expanding techniques that fuller foresight offers are not built into a typical risk assessment.

Strategy questions
Third, risk assessments never really broach the question: is the business idea or business model good and will it keep on being good? That is, what products or services will be appropriate going forward, or how will models of supply or manufacture or marketing or fulfillment need to change, due to technology change or shifting consumer preferences.

In other words, risk assessment doesn’t ask strategic questions of managers. It is part of the day-to-day management vigilance necessary with reference to the future – the hygiene factors in running an organization. It is about keeping the business going as is, not about changing it for a changing word.

There’s nothing wrong with this. The point is, it’s just ‘first base’ in building a quality view of the future, and therein a robust point-of-view about what to do next. Although no doubt companies such as Google or Apple or Virgin, etc., assess and mitigate their risks, they didn’t become successful in their future by doing risk assessment and saying ‘That’s it, were done. We’re ready for the future.”

Well, this raises the question of whether the past tells us anything about the future, and if so what? There’s a common wisdom attributed to Mark Twain (why is it that aphorisms are always attributed to Twain or Winston Churchill?) that goes: “History doesn’t repeat itself, but it often rhymes,” and this is the position that most educated future-thinkers would hold.

So what would the ‘rhyme’ be? From cases such as Argentina, Russia, South Africa, and many developing world countries over the past 50 years: lenders loose confidence in a country’s ability to repay on its national bonds and stop lending; the country is faced with a choice of drastic spending cuts (great social and humanitarian cost) or major tax increases (pointless, because it stifles business, therefore lowers tax revenue) or default. Going broke, into national “Chapter 11,” suing for time and ‘debt restructuring’ becomes the best among the bad options event though it pretty much ensures a deep and dark recession.
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Thinking the unthinkable

Could this be the future of America? As I’ve written before here and other places, after the ‘unimaginable’ Credit Crunch was ignored due to its ‘low probability,’ it’s a relief to know that remote but plausible outcomes with serious consequences are getting attention, at least in the Washington Post.

Clearly major economies are in a more precarious situation than they were 5 years ago. Too much debt is always precarious, for the smallest household or the biggest country alike. On the other hand, an economy’s size and enduring wealth counts too. As Samuelson observes, it created the unexpected effect in Japan’s case where debt at 200% of GDP (America’s is currently about 40%) should have raised the cost of its debt (lower confidence of repayment) but this hasn’t happened because domestic Japanese households and businesses rather than foreigners have easily (and confidently) bought the debt — and this may well hold true for the U.S. too. In other words, the rhyme may go this way.

The ‘more likely’ future is incremental raising of taxes and lowering of public service provision as Western economies incrementally claw their way back to stability. But at least this default wild card on the margins of plausibility has the oxygen of some attention and this is no bad thing. As with all good foresight work, it predicts nothing, but it does allow us to think through the roadmap to the outcome, and press for the right decisions now, in plenty of time and in a measured way.

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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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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.

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.

I note from a link on the Ian Miles Futures blog that “2025

For full disclosure, I should say I worked in the Coates office in Washington D.C. during the mid-late 1990s (but got there just after the book was done.)

There are deep and ultimately overwhelming problems with the book itself. It sees science-technology as the primary driver of change, when what science is done and what technology is produced is often the product of policy or economic or values / zeitgeist decisions further up the chain. It also has an astoundingly poor conceptual framework (‘Worlds 1, 2, 3′) for dealing with non-US societies and cultures, and their economic and social development: one that would make Tom Friedman (‘World is Flat’) giggle and Hans Rosling surely cry. Truly there are many reasons they have to give this book away for free.

But its importance is elsewhere. It remains remarkable for one thing — the thing that the Coates & Jarratt foresight firm was known for — a willingness to speculate confidently and in detail (and sometimes even stupidly) about future changes. The book is likewise exemplary in its commitment to concrete, interesting, ‘fearless’ long-range speculation, in a world where most analysts waste most of their foresight ink timidly equivocating and covering their back.

Quality, reloaded

Evocative, concrete speculation is important, even if it is wrong. It is commonly misapprehended that the purpose of foresight work is to “predict the future,” (and someone with this perspective is going to pop up in 2025 and say “so, how right or wrong was this book?”) But, nobody can be right. The real value of foresight work is other: to know as much as we can about the present, and the forces and factors changing it, to be able to preconceive the full range of possible future outcomes that pertain, in order to make decisions today towards an outcome we prefer. (Who “we” are and what “we” prefer — social welfare; shareholder value maximization; environmental sustainability, etc., — will vary hugely among interest groups of course.)

This preconception (of a range of scenarios, if you like) is what allows truly effective discussions and debates to take place in considering alternatives, and therefore promotes better decision-making regardless of whether the scenarios ultimately turn out to have been, in themselves, ‘right’ or ‘wrong.’ High-quality scenarios are to be preferred of course, but quality is in the ability to stimulate and provoke management attention to the right areas in a timely manner, not in having been right in prediction. As Coates used to say (and I echo this to my Industry Foresight students): “You don’t have to be right, you just have to be interesting.”

Peter Bernstein, the author of “Against the Gods: The Remarkable Story of Risk,” died recently at the age of 90. In memoriam McKinsey Quarterly reposted this recent Bernstein interview. I put it up here because it’s a timely and timeless lesson in thinking about uncertainty and threats, and avoiding simplistic (quantitative) approaches to managing them – one of core themes of “Future Savvy.” Bernstein offers and endorsement of real options and explains why sophisticated Long Term Capital Management (LTCM) mathematical models to control risk created “a math dependency” that was blind to, among other things, unexpected systemic feedback to its own emergence:

One of the first things Bernstein says is that risk implies that we don’t know what will happen, which could be good things happening too. Risk management, as it is currently understood, gets executives to look at what could go wrong in the uncertain future of the enterprise. (Somehow threats are easier than opportunties to get departmental budget for.) The standard approach is to break risks down into commonly understood threat categories: a typical analysis would illuminated risks posed by technology failure, communications failure, security failure, natural disasters, accidents, or market/reputation risk, liability risk, financial/credit risk, and so on. This negative-outcome identification is typically followed by strategies to monitor, minimize, or control the risk event or its impact.

Doing all this is great, BUT it is just a narrow part of enterprise and industry foresight. Why? First, industry foresight or futures studies for business is focused as much on the opportunities change offers as on threats. Second, foresight tools (when correctly applied) set themselves the task of enlarging perspectives or mental maps so that we can see more things, or more possibilities than the generally expected set (whether good or bad). Set against this, risk management is little more than the catalog of known threats. The unknown or poorly understood threat, or unseen opportunity missed (and grabbed by others) is likely to be more damaging to the enterprise.

Christian Blanckaert, Executive Vice President at Hermès International was quoted as saying: “We have absolutely no visibility into 2009!”

On the one hand, fair enough. This economic downturn is steeper than previous down cycles, and the basic viability of the financial sector has been tested. Access to credit is normally easier in a recession, but in this one it is not. All of which makes luxury spending harder to predict.

No doubt the most unlikely prediction of all would have been that Hermès, Burberry, LVMH, Moët Hennessy, Louis Vuitton, and PPR (Gucci , Yves Saint Laurent) have all recently reported better-than-expected results.

Nevertheless luxury industry leaders have declined to provide investors and analysts with any official outlook. What’s curious, from an industry foresight point of view, is how executives such as Blanckaert thought they really had more “visibility” into any previous year, or that they will somehow gain it again when the financial crisis is over. They will not. The world will continue to surprise them and us. What they will gain, certainly, is a greater likelihood that the standard business-as-usual future assumptions they make will not be upset by reality.

Meanwhile, judging by the conference, the luxury goods industry has a very decent grip on current social and moral trends, and clear insight into the bigger picture of change in its industry over the next five to ten years. As they know from before, what happens in a recession is that luxury goes out of fashion. Conspicuous consumption wanes, or retreats further behind secluded walls. This is a basic pendulum swing that tracks the economy (witness how the early 1990s recession stimulated a return to “values” era after the “me, me, me” 1980s.)

Sustainable luxury

So we are again in a swing to modesty. But we also know that each swing of the pendulum also carries with it the specific issues of its time. Current key issues for consumers in this segment are sustainability, global warming, business ethics, and globalization (or fear thereof).

Therefore the luxury brands will be looking for ways of making, transporting, and displaying goods in an energy-efficient and socially conscious way, including a renewed emphasis on local artisans and traditional craftsmanship that speaks sustainability in both natural and human resources. This will be the basis of the “sustainable luxury,” positioning that the famous houses will define and compete in. Fabulous and renewable  – now there’s something you can charge top dollar for.

My question, as the Dow Jones Industrial Average (DJIA), and the FTSE100, the DAX, the Hang Seng and so on have hit a decade lows is, what is this predicting, if anything? What is the long-term value of this prediction, and could it be used to make better decisions in the real world?
We know that the value of a common stock – a share in a company – is based ultimately on the returns (dividends) it will bring. Buyers and sellers therefore derive a daily market price based on their views of the share’s expected, that is, predicted future payback. The greater the expectation, the greater the price. A high price vis a vis earnings (P/E ratio) suggests confidence in future earnings, and vice versa.
Therefore the current steep fall in share prices is an expectation of (crowd prediction of) lower future payouts. Of course the complexity in human-prediction situations is that this basic level is also overlayed with a meta-level: people are not only trying to figure out what will happen, they are trying to figure out what others think will happen. So falling PE ratios are an expectation of what others will do (predicting they will continue to sell.)

Madness or not?
One of the perplexing things about the markets is they very often seem to react opposite to what is expected; to what would be common sense. They often fall on good news, rise on bad news, close unchanged on big news, and so on. Although there is – famously much irrational behavior and herd instinct in the market – you don’t get hundreds of thousands of decision-makers wagering significant money not using common sense.
What is going on, of course, is that the market has often already risen or fallen in prediction of the news. When a new condition – an interest rate move, for example – is imminent, the market will move to “price in” the expectation. If market participants as a whole have called the future correctly the market will not move much on announcement.

Pricing-in the future
Because of this predictive component to group decision-making in market situations, the stock market as a whole is a classic leading indicator of the real economy. When prices move they may be taken as the crowd “pricing-in” a future prediction. So markets will fall ahead of real economic problems (they may continue to fall, as now, during steep economic declines.) But they will also turn up well before any real, measurable upturn.

By the way, there is little doubt it will overshoot in this time, as it always does. This is because, as in prediction markets, the wisdom of crowds can predict the trend but not the turn. Trend extrapolation will never show you the key shifts, and this is why predicting the bottom or top of a market is so hard.

The point, for market speculators, is that long before the real gloom is over the markets will be zooming upwards. The point for the rest of us is that recession times will be with us even after the markets move up. In the long term the market will go up. Like death and taxes, it’s the surest thing there is.

First, there is the prediction game on offer from ABC, taglined: “The Oscars Live Challenge: Think you can Predict a Winner? Make Your Picks Now!”

It’s all part of the marketing drive of course, but, nevertheless how would one play it best and what might that tell us? Let’s assume there is something at stake, like you’re really going to sit in front of the TV and mark off your right vs. wrong predictions, and compare your score with that of your spouse for year-long bragging rights – now there’s pressure – how would you predict? Would you think (a) “this is the best movie so I predict it will win”? Hardly. You would think: (b) “this is the one that I think most people will pick, so that’s the one I think will win.”

You would be making a meta-prediction – going with what you think most are going to choose. In this particular case you would also know that that Oscar winners are chosen by balloting the 6,000 members of the Academy of Motion Picture Arts and Sciences. So your more exact question would be: who is this special group likely to choose in each category?

What’s going on? In future situations that are heavily dependent on aggregate human choices – which is very many situations – the savviest predicting strategy is to figure out the choices most people are going to make. Oscars aside, figuring out the choices most people will make on any issue – hybrid cars, tighter securities legislation, public health care, etc. – is an excellent guide to what will really happen. It’s a mass market-led view of the future to be sure, but that’s exactly what makes it dependable in mass-opinion situations. (Not all situations are determined by mass-market choices – predicting a presidential election winner is; predicting a superbowl winner is not.)

Playing the game

I had a shot at the Oscar prediction game, joining the alleged 1,680 other “players” who were then online. From what I could tell via the rather gristly Flash interface is that the game is not (yet) “social” in that you can’t see what other people are predicting – there is no access to aggregate opinion. No matter. One can instantly get this in hundreds of prediction market forums right now, for example Intrade, where the price of each outcome in each Academy Awards category directly reflects how strongly players as a whole have bid up that outcome.

At Intrade, at time of writing, Slumdog Millionaire is at $87.30 (max is $100; the other 4 movies share the remaining $12.70). When used as a prediction this means that the aggregate opinion of people staking real money has been effectively captured: it is that Slumdog Millionaire is 87% likely to be the choice of the Academy members in its category.

This is a guide to Oscar night that I would not bet against if I wanted to hold onto my bragging rights. Even in situations less overwhelmingly agreed on by players, it has been shown that prediction markets, tapping the aggregate “wisdom of crowds” (working like “Ask the audience” on Who Wants to be a Millionaire) are a fabulous tool for capturing what most people think will happen, resulting in excellent predictions. Caveat Emptor: prediction markets are poor at predicting long-term, open-ended situations, particularly where the outcome alternatives are unknown or can’t be clearly bounded, as blogged a few months back.

Conference: March 19-21

Tim Brown of IDEO, the the industrial design firm, recently published a Harvard Business Review piece Design Thinking – investigating designer-methods in business innovation. At Davos last month there was a “Global Agenda Council/ Design,” featuring Newsweek’s Bruce Nussbaum and built-environment design firm ARUP’s head of foresight, Chris Leubkeman. (The general agenda may be found here.) Next month, the Association of Professional Futurists are having a “Futures by Design”  conference in association with The Art Center College of Design in Pasadena, CA.

And so on. I’m going to be blogging more about this. But for now I wanted to put out a note-to-self I wrote on the issue about five years ago, trying to briefly define how the fields relate to each other, and what the crossover is. Here goes:

The tools of design and planning dovetail closely with those of industry foresight. The overlap and interaction between these two disciplines is not commonly understood, and so the methods and process insights from design professions that could augment the range of strategic foresight tools is often ignored.

1. Beyond aesthetics
Sunday supplements and glossy magazines often use “design” to mean style and fashion. While aesthetics is important, good design means much more than how products appear. It is about creating better processes, interactions and solutions for human benefit. This often involves experimenting with new technologies, envisaging possibilities under conditions of uncertainty and complexity, exploring and comparing alternatives, and determining the best and most durable solution for the long term.

2. Future focus
Whether planning a building, or redesigning a product, or innovating a process, the designer is called on to anticipate a solution that caters to future needs ­ often responding to futures issues, for example environmental-sustainability pressures and changing social values. In other words, design methods, like futures tools in general, form the bridge between current products, systems and practices and what it will be required and desired in the future.

In achieving this future focus, designers, like good “futurists,” must use techniques of imagination, creativity and intuition to generate and evaluate future outcomes. Like futures professionals, designers are called on to practice original thinking, imagine the world differently and see possibilities that others don’t. They are required to take risks, negotiate change and challenge the status quo under conditions of ambiguity and uncertainty. And like good foresight work, design succeeds only if it finds the right tradeoffs between technology possibilities, economic realities, and social needs.

3. Rendering
More than merely anticipating the future, designers and planners are practical agents of visual imagination, creating the blueprints for the objects and experiences of tomorrow. From product creation to urban renewal, designers and planners have tools and experience translating abstract future concepts and ideals into visible or tangible form ­- “making the invisible visible.” Through this rendering function they are primary agents in articulating the future, and therefore in helping us see and negotiate (or refuse) the transition.

4. Systemic innovation
Design is about systems and practices as much as products: better-designed systems improve utility, cut costs, and improve resource use. Designers play a key role in the organizational innovation process as a whole, including the development of integrated product and services, or inventing new types of value chains, alliances, and collaborations.

In sum, much of what foresight professionals are trying to do every day is already being done by design professions. Their methods and process insights should be integrated into the foresight field as a whole.

The mood is justly sombre at the World Economic Forum’s Davos meeting this year, as grim-faced world leaders mull over the dismal state of the global economy and how to fix it. This is in marked contrast to recent years, when the top executives were warmly congratulating themselves on the general state of things.

In one sense this is perfectly understandable. The crisis is upon us and leaders should be directly and practically involved in tackling it. On another level it’s profoundly disturbing, because world leaders and senior managers should be doing more than merely responding to situations. When crises occur, crisis management becomes part of a leader’s job, but their real job is thinking ahead effectively to avoid crises and, on the positive side, develop opportunities.

Put another way: the heads of a companies or countries – Davos-level people – are tasked far beyond effective daily management. They are tasked, fundamentally, with negotiating the VUCA (volatile, uncertain, complex, ambiguous) world on behalf of the rest of us. If not them, then who?

This requires foresight and vision. In this sense, many who are at Davos this week are responsible for the current crisis. They failed to foresee it, in fact they generally endorsed the growth of complex financial instruments, the shadow banking system, and private equity growth –- much of which bypassed SEC or equivalent regulation, and which is now seen to be the root cause of the meltdown.

In fact much of the “new finance” system was thought to spread and therefore actually lower risk. Turns out that was a poor view of the future. In fact the present situation as a whole is the result of key decision-makers operating on a poor view of future. As a group, their mental model was not open to bad outcomes, or even just alternative outcomes to what was commonly expected.

Could we have thunk it?

Their response might be: “nobody can predict the future!” “Easy to say after the event!” This is true. But it’s common knowledge that there were those who foresaw the mess — The Times identified at least 10. As Davos attendees might now be forced to agree, some forecasts are clearly better than others.

This is where executive leaders can learn from the foresight field and particularly the history of failed predictions. Everyone relies on predictions for their guide to the future – nobody can be an expert in every field. And there’s never a shortage of them – they are frequently published in the media, offered by consultancies and think tanks, and are a key part of Davos.

While getting a prediction is easy, the key leadership skill is to be able to tell a good one from a bad one: that’s what turns a forecast into a strategic resource. That is what leads to better decisions, better plans, and better actions.

Can one do that? Can one critically assess a particular or consensus-held view of the future, to identify its strengths and weaknesses? Absolutely yes. Among the tests one can run on a prediction are:

•    assessing motivation – who is speaking and what their agenda might be, particularly if they have an interest in maintaining a current system or shaping the emergence of a new one
•    determining whether the tools used are appropriate to the level and type of uncertainty faced. High-uncertainty situations and long-term views require different approaches to standard modeling
•    questioning consensus mental-models and forcing consideration of alternative outcomes. All foresight is swayed by “zeitgeist” – spirit of the times – and good forecasts swim against this tide.

These are just a few among the many forecast tests one can run, as detailed in Future Savvy.  But even if Davos attendees had been applying just these three in previous years, their foresight would have been greatly improved. It won’t help with this crisis, but it might forestall the next.

* This article, authored by Adam Gordon, was first edited and published by Bnet.co.uk

This punditry is widely read, and sometimes published in respectable places. Some of it is just year-end fun, and nothing wrong with that. But there is also a failed-forecast “nyah-nyah” that is corrosive to the foresight field in general, which demands answers. So at the risk of giving the 20/20 hindsight artists undue oxygen of attention, here are a few thoughts:

Consider Foreign Policy’s “10 Worst Predictions for 2008.” (Dec, 2008). Highlights include:

“If [Hillary Clinton] gets a race against John Edwards and Barack Obama, she’s going to be the nominee. Gore is the only threat to her, then. … Barack Obama is not going to beat Hillary Clinton in a single Democratic primary. I’ll predict that right now.” —William Kristol, Fox News Sunday, Dec. 17, 2006

“Should I be worried about Bear Stearns in terms of liquidity and get my money out of there?’ No! No! No! Bear Stearns is fine! Do not take your money out … —Jim Cramer, responding to a viewer’s e-mail on CNBC’s Mad Money, March 11, 2008 [Bear Stearns was sold to J.P. Morgan Chase at about a 90% discount to it market capitalization at the time of the forecast]

“The possibility of $150-$200 per barrel seems increasingly likely over the next six-24 months.” —Arjun Murti, Goldman Sachs oil analyst, in a May 5, 2008, report [Oil was then around $130 a barrel. By late December it was below $40.]

Or this one from Business Week’s list of 10 (December 24, 2008)

“Existing-Home Sales to Trend Up in 2008″ —Headline of a National Association of Realtors press release, Dec. 9, 2007 [On Dec. 23, 2008, the group said November sales were running at an annual rate of 4.5 million—down 11% from a year earlier—in the worst housing slump since the Depression.]

The Future Savvy question is: how should we think about predictions like this? And how should we think about failed-forecast spotting?

1. Failed-forecast spotting is not remotely “scientific”
This should be obvious, but somehow never is. Purposefully extracting the failed forecasts from the total set of forecasts says nothing about the quality of the set in general. Many did predict Obama; did predict the downturn, etc.

2. Failed-forecast spotting raises a healthy skepticism, but runs to nihilism
Despite not passing any credible test of knowledge, at least failed-forecast spotting stokes apprehension about forecasts and the wisdom of experts. At base this is healthy. Prediction is hard, and it is mostly done poorly. And experts often transgress the boundaries of their expertise. (Typically, in this instance, they know a lot about their field, but often don’t know more than the next Joe about the future of their field, often because their expertise is wedded to existing practices and assumptions.)
Prediction skepticism is fine. What happens, however, is that tempts a “nobody can predict anything” nihilism. This is its own failing because many predictions are in fact excellent, producing good foresight, which is a key strategic and competitive advantage.

3. Often the short term future is harder to see.
This is the trickiest insight of the lot. As everyone knows, it’s impossible to accurately predict the future (which is not the same as usefully predict the future, see arguments in other posts). The world is just too complex, too chaotic. But there’s a wrinkle. It should be that the further we look into the future the harder it is to see. The world will change more – there is more time for unpredictable things to happen. The short-term future (one year, say) is closer to us, it should be more like today and we should be able to anticipate it better.

In fact, short-term foresight is the most impossible task: a casino game. In the longer term (10-20 years, say) strong trends can be relied on to have had their impact. For example, the move away from fossil fuels, or effective nanotechnology engineering, or simple domestic robotics, can be reliably forecast. But while the sweep of these and other similar evolutions are reliable over time, the short-term picture will suffer lags or reversals that follow no pattern at all. (It’s no accident that is this is just like the stock market. In the long term the market will go up, in the short term it can go anywhere.) Also short-term predictive failure is compounded by the fact that the standard to which it is held is higher – we expect specifics: dates, places, numbers, players, winners – that are not demanded of a long-term view. In other words, near-term predictions are all about “point forecasts,” and there’s nothing more impossible than a point forecast unless you believe in tea leaves and crystal balls.

The take away: short-term point forecasts really are a mugs game and the skeptics are right. Medium-long forecasts, when well done, are worthy of our strategic and competitive attention.

In many ways, focusing on whether “this” or “that” is predicted, or not predicted, is to put the cart before the horse. The horse is the adequacy of our approach to anticipating outcomes and the quality of our foresight as a whole. When this is good, the cart – not missing important changes – will follow.

Credit: http://www.lewrockwell.com/blog

In this, it’s important to realize that many did predict the financial crisis (as many predicted 9/11 in various ways). Sticking with the financial crunch for now: it has generally been portrayed it as a “why-didn’t-anyone-see-it-coming” event. It wasn’t. Hats off to The Times for their October 12 piece: “10 People Who Predicted the Financial Meltdown.”(Summary here). Allowing for a fairly loose definition of “predicted,” the article shows that among those who foresaw the crunch were: Vince Cable, deputy leader of the Liberal Democrats (2003); US congressman Ron Paul (2003); Stephen Roach, senior executive at Morgan Stanley (2004); Christopher Wood – chief strategist of a broking firm in the Asia-Pacific Market (2005); and Nouriel Roubini, economics professor at NYU (2006)… and there were many others.

A different problem

So this reframes the problem entirely. It’s not that the predictions were not there. It was that not enough people believed them and, particularly, important decision-makers didn’t believe them or didn’t have the institutional capacity to respond. So there are two halves to the problem: the ability to see the full spectrum of what may happen, including unexpected outcomes; and the ability to act on what we see. Quality in foresight work – the raison d’etre of Future Savvy – makes it possible to see more outcomes more clearly, and to act with more confidence in choosing what to prepare for. (In the real world we can’t prepare for every outcome.)

There was a good letter published in the FT from eminent futurist Peter Schwartz on December 2, which describes this very well. It shows predictions for what they are (one-horse scenarios), and how decision-makers are typically bound into inaction or wrong action not only by working on the basis of a wrong prediction, but by the predictive mindset itself. This mindset – the habit or culture of picking “one right answer” in the face of a complex situation with many competing outcomes, prematurely closes alternatives and leaves us open to surprise. As Schwartz says, as scenario planners have always said (and he was one of the people who defined the field in the first place), a compelling set of alternative future scenarios encourages decision-makers to recognize unlikely and unpopular outcomes, along with expected outcomes, and therefore to be able to respond earlier and more effectively whatever happens.

Scenarios also contribute to the “act” side of the problem. In a well-done set for the banking industry, a financial-meltdown scenario would at least have been in play, institutionalizing the consideration of less unlikely, less popular outcomes in company and government forums, forcing serious consideration of necessary strategies and contingencies, and therein creating the ability to act early and effectively without having predicted the crisis.

The letter is well worth quoting in full:

Sir, The real question regarding the financial crisis is not, as the Queen asked: “Why did nobody see this coming?” In fact, any number of thoughtful people in academia, politics and business had been compiling the data and sounding warnings for several years.
The question we should be asking is: “Why didn’t decision-makers believe that a global financial meltdown was increasingly likely and then act on that belief?” Or, to put it another way: “What would it take to make decision-makers both believe and act?”

The problem is that decision-makers believe that they are forced to pick one right answer: the most likely scenario. Their approach to decision-making does not afford them the opportunity to consider apparently low probability but highly consequential scenarios. The answer, therefore, to the “believe” half of the question is a decision-making process that considers several scenarios: compelling stories about alternative futures that incorporate the analysis of “outliers” and describe three or four plausible paths forward.
Good scenarios force decision-makers to challenge their own assumptions and reconsider what is possible. As a result, they can take seriously those scenarios that seemed less likely at first, but whose plausibility increases over time.

The second part of the question – “What would it take to act?” – is much harder to address. Suppose that Ben Bernanke or Hank Paulson had come to believe a year or two ago that the house of cards was about to collapse and trigger cascading, global failures. What would they have done, given the realities of the complex interconnected systems at the heart of the problem? Perhaps if they had good scenarios with appropriate indicators to start with, they could have rehearsed different strategies and contingencies. Importantly, these decision-makers could have used these scenarios to persuade others on all sides of the issue also to recognise the complexity of the impending crisis in a more timely way. It’s never easy to convince everyone around you that the game they have been playing to their great benefit is about to change. But with a shared recognition of the magnitude of the risks and the ways they might unfold, they could have acted far earlier to prevent some of the dire consequences that have occurred, let alone what is to come.

Future Savvy is intended to be a book for business and policy professionals, not academics. But it does speak to students and scholars who need to assess and evaluate foresight work. In any event, professors Jay Gary and Dennis Walters have included it alongside works by Wendel Bell, Jerome Glenn, and Ted Gordon (no relation) — household names in the foresight field — so the comparison is of course very happily accepted.

The following is from the course outline. I’ve included the full bibliography, which is in itself a valuable collection of sources in the futures field, and merits attention all the way down the list.

Course Description:
Surveys traditional forecasting theory and methods. After a consideration of forecasting in general, students learn how to conduct research using both qualitative (secondary sources, interviews and questionnaires) and quantitative (data analysis, numerical forecasting and trend decomposition). They also apply critical thinking skills to existing forecasts. [Learning objectives:] 1. Managing: understand the principles and applications of operational forecasting within organizations. 2. Assessing: decide when to use statistical or judgmental methods in strategic forecasting, and how to combine foresight methods to generate 10 to 20 year outlooks. 3. Evaluating: gather information in a specific domain that can be used to forecast baseline as well as alternative futures. 4. Researching: construct a long-term strategic forecast for a client organization that draws upon both quantitative and qualitative sources.

Required Resources
* Bell, Wendell. 1996. Foundations of futures studies: History, purposes, and knowledge. (Human Science for a New Era), vol. 1. New Brunswick, NJ: Transaction. ISBN: 0765805391
* Carlberg, C. G. (2005). Excel sales forecasting for dummies. Hoboken, NJ: Wiley. ISBN: 0764575937
* Glenn, J. C., and Gordon, T. J. Futures Research Methodology V2.0 CD-ROM American Council for the UNU. ISBN: 097220511X
[This item is available through http://www.acunu.org/millennium/FRM-v2.html]
* Gordon, A. (2009). Future Savvy: Identifying trends to make better decisions, manage uncertainty, and profit from change. New York: American Management Association. ISBN: 0-8144-0912-1
* Jain, C. L. ed. (2001). Practical guide to business forecasting. Flushing, NY: Graceway. ISBN: 092126758

Recommended and supplemental resources:
*  Coates , Joseph F 2025, John B. Mahaffie, and Andy Hines. 2025: Scenarios of US and Global Society Reshaped by Science and Technology. Oak Hill Press. ISBN: 1886939098, also available in .pdf files via http://www.josephcoates.com/2025_PDF.html
* Armstrong, J. S. (1985). Long-range forecasting: From crystal ball to computer (2nd ed.). New York: Wiley. ISBN: 0471823600, also available in .pdf files via http://www.forecastingprinciples.com/Long-Range%20Forecasting/contents.html
* Armstrong, J. Scott. 2001. Principles of Forecasting . Kluwer. ISBN: 0792374010.
* Caplow, T., Hicks, L., & Wattenberg, B. J. (2001). The first measured century: An illustrated guide to trends in America , 1900-2000 . Washington , DC : AEI Press. Download chapters at: http://www.pbs.org/fmc/book.htm
* Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34, 571-582.
* Duberley, J., & Johnson, P. (2000). Understanding management research: An introduction to epistemology. Thousand Oaks, CA: Sage.
* Einhorn, H.J. (1986). Accepting error to make less error. Journal of Personality Assessment, 50, 387-395.
* Fischoff, B. (1994). What forecasts (seem to) mean. International Journal of Forecasting, 10, 387-403.
* Gawiser, Sheldon R., and G. Evans Witt. 1994. A Journalist’s Guide to Public Opinion Polls . Praeger. ISBN: 0275949893.
* Gillham, Bill. 2000. The Research Interview. Continuum International. ISBN: 082644797X.
* Hetman, F. (1969). Le Langage de la prévision, the language of forecasting: With a French-English-German vocabulary. Paris: S.ÉD.ÉI.S. http://www.cnam.fr/lipsor/eng/data/langageprevision.pdf
* Jantsch, E. (1967). Technological forecasting in perspective. Paris: OECD. http://www.cnam.fr/lipsor/recherche/laboratoire/data/prevtech_en_final.pdf
* Makridakis, S. G., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and applications (3rd ed.). New York: John Wiley & Sons.
* Molitor, G. T. (2003). The power to change the world: The art of forecasting. Potomac, MD: Public Policy Forecasting.
* Moore, D. A., Kurtzberg, T., Fox, C. R., & Bazerman, M. H. (1999). Positive illusions and forecasting errors in mutual fund investment decisions. Organizational Behavior & Human Decision Processes, 79, 95-114.
* Orrell, D. (2007). The future of everything: The science of prediction. New York, NY: Thunder’s Mouth.
* Rescher, N. 1998. Predicting the future: An introduction to the theory of forecasting. Albany: SUNY Press. ISBN: 0-7914-3553-9
* Salant, Priscilla, and Don A. Dillman. 1994. How to Conduct Your Own Survey . Wiley. ISBN: 0471012734.
* Seidensticker, R. B. (2005). Future hype: The myths of technology change. San Francisco, CA: Berrett-Koehler.
* Schnaars, S. P. (1989). Megamistakes: Forecasting and the myth of rapid technological change. New York: Free Press
* Sherden, William A. (1998). The fortune sellers: The big business of buying and selling predictions. New York: John Wiley.
* Wood, G. (1992). Predicting outcomes: Sports and Stocks. Journal of Gambling Studies, 8, 201-222.