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.

Jeff Bezos

When I was at INSEAD for my MBA, I noticed it was fashionable for young men on the move in their careers to wear genuinely expensive watches. We’re talking $5,000 a pop and more (and no doubt they would upgrade in time.)

Me, I’d rather invest in my wine cellar: each to his own. The point is, it’s nothing new for rich men to spend handsomely on their timepiece. And nothing new for even richer men to lavish a fortune on signature and-or vanity projects.

So it’s all to type that Amazon founder and CEO billionare,Jeff Bezos, is spending $42m on his timepiece. The clock the size of a building, which will still take a number of years to complete, is being constructed deep in the Sierra Diablo Mountain Range, Texas. It is designed to run for 10,000 years.

On the clock’s web site Bezos says: “It’s a special clock, designed to be a symbol, an icon for long-term thinking… As I see it, humans are now technologically advanced enough that we can create not only extraordinary wonders but also civilization-scale problems. We’re likely to need more long-term thinking.”

This is partly the standard, “world-going-to-hell-in-a-handcart unless we wake up and change our lifestyle” plea for a long-term, sustainable, perspective.

But, in fact, the general thrust of communications around the 10K Clock is refreshingly low on planetary doom. Long Now Foundation founder member Steward Brand says of the clock: “Ideally it would do for thinking about time what the photographs of Earth from space have done for thinking about the environment.”

Builders in clock tunnel. Image: http://www.10000yearclock.net/

So the clock is in fact about exactly what it says on the tin: just a symbol of long-term thinking, a monument to the value of a long-term perspective.

And while 10,000 years is no business horizon, it’s possible to interpret the clock as symbol not just socially, but also in terms of dollars and cents. In a short-term world, where most businesses are rated by the quarterly numbers, it is a living monument to making scaled-up and lasting investments, and not pulling the plug too soon.

Who better than Bezos to put up this monument? In his first report to Amazon.com shareholders in 1997 he said: “because of our emphasis on the long term, we may make decisions and weigh trade-offs differently than some companies.”

The company was founded in 1994, listed in 1997, and but didn’t post profit until 2001. But by the time it did, it was far bigger and more influential than imagined. It was on the road to becoming what it is today: the world’s biggest online retailer, period. Reflecting a final coming of age after 15 years, the share price (AMZN) has doubled and doubled again in the last two years.

Arguably Bezos’ true leadership genius at Amazon in the early days was not just seeing the long-term and scalable possibility (beyond book retailing) but also being able tactically to hold the short-termers at bay for long enough to do the building required.

As a business culture, we’re locked into annual reports and rapid product life cycles. We’re quick to say “fail-fast” and pull the plug on a fledgling project that’s in the red. Or we make a return, so good, let’s cash it in and do something else.

But Bezos was able to see and to say that a critical component of business leadership success is looking beyond your own or your competitors’ time horizons and scale horizons.

The leadership message in the clock is “Don’t think small. Forget short-term wins. Look beyond your time horizon. Give weight to the long-term possibilities. Build for tomorrow and allow the full potential of a project to evolve.”

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.

In the pivotal moment of the movie “Remains of the Day,” US Congressman Trent Lewis (Christopher Reeve) in England in 1936 declares to the “gentleman-amateurs” around him who are blunderingly cosy-ing up to the Nazis: “leave politics to the professionals.”

It’s an expression of the 20th century zeitgeist shift to professionalization of not only politics, but all significant decision-making and management. Business certainly led the way through the century with the rapid rise of managers as a distinct class of professional, expecting the commensurate erosion of family-run firms of any real size and clout.

The problem with family firms are legion: under-qualified if not downright incompetent heirs thrust into positions they can’t cope with or don’t want, family wrangles, inheritance disputes, relative non-accountability of management leading to quixotic decision-making, secrecy mitigating against access to capital and therefore growth, and so on.

So the wisdom became that the family-firm management was appropriate in start-up mode, and then as companies scaled up and moved to external funding and responsibility to multiple stakeholders, professional management should take over for the good of everyone.

Generations

Or so we thought. There is a long-running counter-argument that family firms do many things better, even at scale. Key decisions are made with the fearless straight-talk that is often required, and without bureaucracy up and down the chain. Families may have their politics, but they don’t have the chronic office politics nor resume-polishing that besets so much of corporate life, wasting countless person-hours.

Furthermore, industry and business wisdom that is built up over generations stays in the firm rather than getting washed down the river every time the executive door revolves. The bottom line: family firms remain a more-than-viable model very much alive and kicking all across the world.

These are background issues to Randel Carlock (INSEAD) and John Ward’s (Kellogg) new book “When Family Businesses are Best,” (Palgrave, 2010) which is broadly about navigating a family firm in the changing, globalizing world.

What got my attention particularly is the authors’ contention that family firms are better at developing, retaining, and working to a long-term management perspective. That is, the family is an inherently long-term institution, and well-run family enterprises are run in such a way as to endure for the future for the family – and this is an advantage in navigating and surviving a changing world.

The term the authors’ use is for this kind of management is “stewardship.”

The root problem of most professionally managed businesses is they are run without stewardship  – without concern for long-term well-being of the firm or its stakeholders. If we needed reminding, the banking crisis was the product of management that couldn’t be further from stewardship – taking absurd risks with other people’s money for short-term personal wins.

Banks have become the poster child for the follies of short-termism, but the reality is short-termism remains endemic across professional management, both in business and politics. Long after “après moi le déluge” CEOs have taken their packages and are on the golf course, others – employees, taxpayers, the environment, etc. – are paying the price.

What’s measured

At least, post-crunch, it is now incontrovertible that short-termism is an extremely poor strategy for managing a complex and uncertain future. “What gets measured gets managed,” and when what is measured is only the next quarter’s profit figures, bigger failure looms.

The family-run businesses offers a model of long-term management. It is a conservative non- “bet-the farm” model to be sure, but perhaps the path a real steward of value genuinely operating in the best interest of valued stakeholders would follow.

So how might one, without the real flesh-and-blood bond of family ties, get senior executives to think through the effect of their behavior on employees or stakeholders 10- or 20 years in the future, as the head of a family would? Surely only by creating incentive structures that mimic family stewardship – incentives that mean that leaders can’t walk away smiling until the organization (or the value it represents) has been safely passed on to the next generation.

Leadership is most commonly associated with motivating staff and streamlining organizational effectiveness. While this is core, leadership implies far more. It implies foresight and vision. Leaders are not just those who are responsible for an organization’s “best manifestation today.” Whether they like it or not, they also carry the burden of responsibility for their organization’s best manifestation tomorrow.

And tomorrow, as we know, will be different in important and sometimes surprising ways.

So any leader of note is soon asked to go beyond “effective managing,” to look out at the uncertain road ahead and steer to the desired destination on behalf of followers and stakeholders. Leaders take their institutions to the future.

Therefore, as enterprises are forced to transform in response to rapid social, technological and market change, so anticipating and competitively interpreting new opportunities and setting appropriate direction under conditions of complexity and uncertainty has become a key competitive skill — perhaps the key skill — leaders bring to their position.

There are, these days, more high-quality non-predictive approaches to strategic foresight and future-management than most managers are aware of. So this is what I get to go over with an impressive array of real-world Scottish managers in workshop mode in Edinburgh over the weekend.

But will leadership itself change?

In leading the future, there is also a meta-question: will leadership itself change? Does this skill have “a future?” Will leading mean the same thing in the next generation as it has meant in the past? Or are there new skills leaders will need to acquire for the new era of business and society?

In a recent Forrester hub blog piece “Thoughts on Leadership in the Social Era,” authors Josh Bernoff and Ted Schadler bring insight from their book Empowered, and Charlene Li’s book Open Leadership in asserting what it means to lead “in a social world.” They offer this 5-point checklist:

1. Share strategy continuously, especially changes in strategy
2. Embrace half-baked ideas
3. Use councils to coordinate
4. Celebrate failure
5. Celebrate success (Full text here.)

Would he require a skills upgrade? Image via Wikipedia

To be honest, this looks a lot like the flattening and opening-up “leadership revolution” of the dot-com boom and the post-recession 90s, which leads me to think, is leadership (including the foresight injunction) perhaps a constant rather than a changing skillset? Would any leader in history, from Jefferson to Jesus, not be able to lead in today’s environment? Would George Washington, Abraham Lincoln, Sir Earnest Shackleton, Mahatma Ghandi, Golda Meir, Nelson Mandela, et al would be able to lead in the 2nd decade of the 21st Century, or would they they require some kind of “skills upgrade” to be fit for the world of social media, empowered consumers, and so on?

I’m very tempted to say they would do fine. Hyper-information and social networking is just another set of challenges drawing on an age-hold leadership skill set, which includes knowing how to effectively communicate and persuade and inspire, no matter what the media conditions.

But I’m indebted to my friend and foresight-sounding-board-extraordinaire, Andrew Curry, for offering this perspective:

“I think there must be *some* changes in the demands on leadership as a result of:

- rapid feminization of the workforce
- secular shift in attitudes to authority/ trust
- emergence of ideas about complexity.”

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?”

Controversy has resulted from lack of transparency in the tests, leading to speculation that they are designed to have most banks “pass” in order to boost confidence — as clear an example of mixing up judgment and advocacy as one is likely to get.

The key measure for determining which of the 91 banks fail the test — and need to raise capital — is whether their Tier 1 capital ratio would fall below 6% under the “loss assumptions” imposed by the test. This is the same level that was required in the stress tests of U.S. banks in its similar May 2010 test.

Model worlds

Anyhow, what is particularly interesting to this author is that the concept “scenario planning” has not been used through the bank test process, but these tests are fundamentally future scenarios, this is what scenarios are all about: creating model future worlds that express the evolution of important uncertainties towards somewhere at the limits (but not beyond) of plausibility, with the specific intent to use these worlds to stress test current decisions as to what a company is and does — from its business model to its resource base to product line to marketing, and so on.

If the organization’s key decisions would hold up (produce profitability or however success is defined) in different, alternative tests, this tells managers theirs are probably good decisions for the future. If they would flop in any test, this points to what needs to be urgently addressed. In this way an organization explores and becomes robust to its unknowable and unpredictable future.

Notably, it is precisely the stress-test purpose of scenarios that stops this foresight technique becoming (as it does all-too-often in the wrong hands) a “wishing well” for better times. When scenarios cease to be direct stress tests of present decisions, they become floaty indeed.

Full scenarios

Having said all this, the difference between the US and European banking stress tests and full scenario work is the bank tests are considering only economic factors, only adverse (risk) conditions, and only “known unknowns.” Full scenarios would include the full range of important drivers of change — and potential surprises — outside of economics or finance in their construction. In operating as stress tests, they would look at threats to the status quo as the bank tests do, but also provide a testbed for exploring opportunities in change.

Imagine driving down a country road when a street dog starts chasing your car. The dog attacks the car, but by the time it gets close, the car has moved ahead, so the dog changes direction and attacks the new coordinates. This goes on as the dog adapts, but it never quite catches up, and once it is following behind it is obviously too slow to catch up. Had it thought ahead and run straight it would have had its day with the tires.

The resulting curve looks something like this:

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In mathematics, this is known as the ‘curve of pursuit. The dog is attacking the problem as it sees it right now, but by the time it reaches it, the problem has moved on a few steps. A ‘problem-solving’ approach like this is going to prolong the time it takes to get to key decisions, and give the initiative to competitors. The better approach in managing moving situations — and all situations are moving — is to anticipate and tackle tomorrow’s position today.

Obviously the devil is in the quality of the anticipation, but for that there is Future Savvy and other key resources that exist for determining quality in foresight work. Industry foresight can never be done perfectly, but it can be done well enough to avoid the “dog chase” future-management style that characterizes much of industry leadership.

Front page 'Competing for the Future' Hamel & Prahalad, HBR 1994

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As many have commented, Prahalad made great strides in getting business to see the potential in emerging markets and ‘poor’ consumers, in The Fortune at the Bottom of the Pyramid and allied work.

In our rush for the new and latest, early work often gets buried. So I would like, as my take on the passing of Prahalad, to go back to his fundamental testimony to the role of and need for foresight in management, which is to be found in his co-authored piece (with Gary Hamel) ‘Competing for the Future,’ Harvard Business Review, 1994, which became a very famous book of the same name. Sixteen years on and now in the wake of the credit crunch, this piece remains as relevant as it ever was:

“Ask yourself: Do senior managers in my company have a clear and shared understanding of how the industry may be different ten years from now? Is my company’ point of view about the future unique among competitors?

“On average managers devote less than 3% of their time building a corporate perspective on the future.

“The painful upheavals in so many companies in recent years reflect the failure of one-time industry leaders to keep up with the accelerating pace of industry change… Those companies were run by managers, not leaders, by maintenance engineers, not architects.

“If the future is not occupying senior managers, what is? Restructuring and reegineering. While both are legitimate and important tasks, they have more to do with shoring up today’s business than with building tomorrow’s industries. Any company that is a bystander on the road to the future will watch its structure, values, and skills become progressively less attuned to industry realities.

(therefore) “Most layoffs at large US companies have been the fault of managers who fell asleep at the wheel and missed the turnoff for the future.

“If senior executives don’t have reasonably detailed answers to the ‘future’ questions, and if the answers they have are not significantly different of the ‘today’ answers, there is little chance that their companies will remain market leaders.

“The Quest for Foresight: Why do we talk of foresight rather than vision? Vision connotes a dream or an apparition, and there is more to industry foresight than a blinding flash of insight. Industry foresight is based on deep insights into trends in technology, demographics, regulations, and lifestyles, which can be harnessed to rewrite industry rules and create new competitive space.”

Footnote: this from the FT: The last time CK spoke to the FT he was buzzing with intellectual energy. “Really, in all my career I have been interested in ‘next practices’, and not merely ‘best practices’,” he said.

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.)

Charles Munger

First, let me say, what I like about investors (and managers and entrepreneurs) with long-term track records of success, is it means — it must mean, by definition — they have a high quality view of the future. Not only a high-quality view, but a high quality view that renews itself. There is no doubt that Berkshire Hathaway has consistently over time had a better view of the future than most expert forecasters, policy pundits, and futurists. The record is clear.

Anyway, Munger this week offers a parable about Basicland, a C18 Pacific island colonized by Europeans where: “Property rights were greatly respected and strongly enforced. The banking system was simple… Almost no debt was used to purchase or carry securities or other investments, including real estate and tangible personal property…  Speculation in Basicland’s security and commodity markets was always rigorously discouraged and remained small…

“(But) as their affluence and leisure time grew, Basicland’s citizens more and more whiled away their time in the excitement of casino gambling… Many of the gamblers were highly talented engineers attracted partly by casino poker but mostly by bets available in the bucket shop systems, with the bets now called “financial derivatives.”

And so it goes on, telling the history of America and the route to the Credit Crunch, and potential for new misery going forward, via this parable. He uses the parable as parables have always been used, to say something in ‘make-believe-land’ that cannot be said (or will not be heard) in reality. The folly of Basicland’s citizens and government is much easier to acknowledge than our own. Scenarios of the future are similar in function, similarly allowing mental and institutional ‘permission’ to think the unthinkable and ‘say the unsayable.

The worst investor in America

Munger wouldn’t be the first to say: “Change yer ways or ye be doomed.” Isaiah and many before and since have said that. Nor would he be the first old white guy to espouse traditional ways of doing things. We factor that in. But he does look to basics and basics are important in having a high-quality view of the future. They signal the limits of the excess and reversion-to-the-mean imperatives.

I remember in the 1990s, when I was living in Washington DC, and Warren Buffet was “the worst investor in America” for missing out on the dot.com boom and Nasdaq bonanza. He just stuck to his guns saying, time after time, ‘there are no fundamentals behind these valuations (aka, this is just a casino) and fundamentals will prevail, which of course they did.

Now the brains at Berkshire Hathaway are saying that forums where risk, debt, currencies, etc., are up for speculation are ‘casinos,’ and their players therefore gamblers (rather than, as they would have it, ‘investors), and that they produce little fundamental value and fundamentals will prevail.

Anyway, this is in the intro (p5), and I found it a perfect encapsulation of a core problem in foresight thinking — quantitative vs qualitative methods — well worth retyping out to have on hand for reflection. Here goes:

“The story that I have to tell is marked all the way through by a persistent tension between those who assert that the best decisions are based on quantification and numbers, determined by the patterns of the past, and those who base their decisions on more subjective degrees of belief about the uncertain future This is a controversy that has never been resolved.
The issue boils down to one’s view about the extent to which the past determines the future. We cannot quantify the future, because it is an unknown, but we have learned how to use numbers to scrutinize what happened in the past. But to what degree should we rely on the patterns of the past to tell us what the future will be like? Which matters more when facing a risk, the facts as we see them or our subjective belief in what lies hidden in the void of time? Is risk management a science or an art? Can we even tell for certain precisely where the dividing line between the two approaches lies?
It is one thing to set up a mathematical model that appears to explain everything. But when we face the struggle of daily life, of constant trial and error, the ambiguity of the facts as well as the power of the human heartbeat can obliterate the model in short order.”

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.

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.

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.

Book reviews by SAMI fellows and associates
“Future Savvy” by Adam Gordon (American Management Association, 2009)

“Forecasts and predictions are ubiquitous. We are bombarded with views of the future on a plethora of subjects from myriad sources, with a diverse set of motivations and self-interests. Adam Gordon seeks to provide a practical users guide to the assessment and interpretation of all things about the future, with special emphasis on the cautions and ‘health warnings’ that need to be applied, so as not to be misled by forecasts. However, the author is careful not to veer towards over-cynical dismissal of all future projections; rather, he seeks to provide guidance to the reader on how to apply the necessary caveats, and in the author’s words “profit from change”.

The book covers a very broad field, from the basic issues of the misuse of data and statistics, covering the quality and validity of data as well as their misinterpretation, through technology forecasting, trend and horizon scanning to quantitative modelling and scenarios. The one theme common to all these activities is the need to be alert to bias, whether it be a deliberate motive to influence behaviour through a dire prediction; or a bias inherent in futurologists needing to see rapid and pervasive change in all areas of society – if it exists or not – and evangelising it.

The track record of much futurology is mixed. Well-known examples are quoted: television did not lead to the end of the cinema industry. Nor has space exploration led to people taking foreign holidays on other planets – yet! Bias may also lie in the beholder. The ‘Zeitgeist’ tendency, whereby we are all influenced by contemporary perceptions, affects not only how “experts” and professionals see the world, but also how the audience receives the views of the future – often with unprepared minds. The internal “official future” of an organisation can pose a real blind spot to its progress.

The weaknesses of much quantitative modelling are highlighted, with such forecasts only being as good as the assumptions on which they are based, but which are often not overtly stated. In contrast to the conceptual and practical errors inherent in much futures output, the role and advantages of scenario planning are emphasised as a tool for challenging assumptions and developing alternative futures: “It’s better to be vaguely right than precisely wrong”.

The penultimate chapter takes examples of relatively recent forecasts from a range of organisations, whose subjects range from US agricultural production to UK dementia sufferers. These are subjected to a form of ‘retro wind-tunnelling’ to illustrate the deficiencies in their construction and how they would have benefited from the application of methodologies described earlier in the book. The final chapter provides a summary checklist, or framework, to apply in evaluating forecasts and future predictions.

Adam Gordon has written a shrewd and perceptive book that deserves a wide readership, especially among managers in both the private and public sectors, as well as the familiar ‘general reader’. Those wishing a more detailed technical guide to the various forecasting and futurist methodologies will need to consult other standard works. Professionals in the fields of management and strategy consulting and scenario practitioners might well be familiar with many of the points made in the book. However, those with some savvy might do well to recommend the book to their clients.

Michael Owen, 20 April 2009

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.

The IFTF’s full map is available for download here.  Quick aside: these maps, putting complex forces into visuals, have defined IFTF’s public (and client, one presumes) communications for over five years, and have raised the bar of excellence in the foresight communications. The company has produced many such outstanding maps, some publicly available.

The new map and Sir Fred-gate are unrelated of course. But here was the connection for me: The IFTF map lists six “Key Driving Forces” (2007-2017) in the area of sustainability, and the first is:
“An Imperative for Looking Long: The 21st century will test our ability to grasp the future impacts of present choices, but even as we struggle to incorporate future knowledge into our day-to-day decisions, we’re tuning up our bodies and minds and even our cultural frameworks for a much longer view.”

My question is, “really?” Is the long view really a driver – something that will drive change and shape the future? Or do we hope it is. Are we trying to talk it into being?

No question that the long-term view is crucial. Solving just about any social, technological, or environmental problem requires sustained long-term action. And everyone who works in foresight keeps evangelizing long-termism. But, in fact, what we have in industry and government is rampant short-termism and there is no indication this will change, despite the crisis and many heartfelt calls.

Linking big to long

The problem with Sir Goodwin’s package (in career and in retirement) is that the reward numbers were based on short-term company returns. “Hey, we made lots of money this year, so you get a big bonus, and you get a big bonus,” etc. But a few years down the line  – in the long term – it turns out that no bonuses were valid (if a bonus is, truly, a reward for success).

Put it another way: in finance, as in other aspects of society, technology, and the environment, we don’t know if we’ve succeeded or failed until the long-term numbers are in. Few would have a problem with handsome rewards for a valuable job well done, but those rewards must surely be delayed, and delayed, until we are in command of the long view of the performance.

Easy in theory, hard in practice. Perhaps impossible in practice when most politicians and legislators are themselves on a short 3-7 year cycle, like CEOs. I have some inkling from the IFTF map that the thinking is that life-extending technologies will improve to the point where people will really see themselves in for the long haul, and so adopt a longer perspective on benefits and rewards.

Time on the clock

Perhaps. But, life-technologies aside, plenty of decision-makers – Goodwin included – still have a lot of time left on the clock and that doesn’t appear to stop them chasing and cashing in short-term incentives at the expense of the future. Or legislators (and the public who votes them in) structuring performance rating on our immediate perception of their performance.

What we have, and what we have increasingly had (the trend) over the past few decades, is systemic short-termism. Winning in the next annual report or the next election is what what leaders’ rewards are based on. Incentives for politicians or business leaders or even scientists or engineers to make a better world for 2025 or 2050 are negligable.

Until there is reason to anticipate that this fundamental underlying short-term incentive structure and mentality changes (that is – convince me – who will change it and how?) the future savvy perspective must say that the “long-term imperative” remains a nice sound-bite, but not a material driver of anything.

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.

The forecast is the recently published academic research paper: Chinn & Frankel (2008), “The Euro May Over the Next 15 Years Surpass the Dollar as Leading International Currency,” Faculty Research Working Paper RWP08-016 (Cambridge, MA: Harvard University, John F. Kennedy School of Government) available here. Frankel is a Professor of Economics at the Kennedy School.

The critique, “Forecasting the Euro’s Future,” by Benjamin Cohen, is here

The argument of the Chinn & Frankel paper, which is also summarized here is that the euro may surpass the dollar as the leading international reserve currency as early as 2025. The authors use econometrically-estimated determinants of the shares of major currencies in the reserve holdings of the world’s central banks. Significant factors include: size of the home country, rate of return, and liquidity in the relevant home financial center (as measured by the turnover in its foreign exchange market). The analysis predicts a narrowing in the gap between the dollar and euro over the period 1999-2007, and forecasts this trend to continue.

Cohen has technical issues with the forecasts, saying, “the analysis addresses just one specific function of the two rival monies – their use in central bank reserves – ignoring all the many other roles that international currencies play. But the essence of his critique is deeper. He says, “By concentrating purely on economic factors, (the forecast) ignores the politics involved, which in practice could prove to be far more decisive… key considerations include both the quality of governance in a currency’s home economy and the nature of relationships between countries. Is the issuer of a currency capable of assuring effective political stability at home? Can it project power abroad? Does it enjoy strong inter-governmental ties – perhaps a traditional patron-client linkage or a formal military alliance? Though it is by no means easy to operationalise many of these factors for purposes of empirical analysis, it is hard to deny their importance (for an accurate forecast.)”

Cohen’s agenda is not merely to tackle possible shortcomings of Chinn & Frankel’s study, but to critique economic forecasters far-and-wide that analyze the technical data, while ignoring political (or social) factors that are hugely influential on outcomes, yet harder or impossible to quantify, and which are therefore conveniently ignored.

Coming to grips with politics
Says Cohen: “Chinn and Frankel are not alone in this shortcoming, of course. Many economists, perhaps even most, have a hard time coming to grips with the intricacies of politics, which can seem so messy and indeterminate when compared with the pristine parsimony of formal economics. When it comes to the analysis of public policy, few even bother to try to address political factors systematically.

“The result, though, is sadly predictable. By ignoring the role of politics, economists often get it wrong. How many trade specialists were prepared for the recent breakdown of the Doha trade talks, despite the obvious gains to be had on all sides from a new round of liberalisation? How many can explain the unprecedented accumulation of reserves in China or other East Asian countries, the widespread distrust of multinational corporations or the failure of the international community to do a better job at combating global warming? Politics is clearly critical to all these questions, and more… (Yet) conveniently, Chinn and Frankel set all these considerations aside in order to build a parsimonious model that they can use for forecasting purposes. Only three independent variables are highlighted in their regressions: country size (relative income), foreign-exchange turnover (representing the depth of competing financial markets), and trend exchange-rate changes (representing the rate of return on currency balances).”

Cohen offers potential political and ideological blockers to the particular forecast: “Japan, for instance, has long relied on a formal security umbrella provided by the United States to protect it against external threats; and the same, less formally, is true for Saudi Arabia and other Gulf states as well. Can we really imagine any of these nations, all very large dollar holders, casually jeopardising their ties to Washington for the sake of a few basis points of return on their reserves?”

To be fair to Frankel, the nature of his analysis is consistently political – see his blog at http://content.ksg.harvard.edu/blog/jeff_frankels_weblog/
One can’t imagine that Frankel or Chinn would dispute that politics will strongly influence the accuracy of their forecast. (What they clearly imply in their data-centered model is that the economic data is backed up by political shifts towards Europe, or at least there is nothing in the political realm that would counter their technical analysis.)

Yet the problem remains that these contextual factors are not built into the model. The technical stuff is quantifiable and gets forecasted quantitatively. The rest is a kind of political/social/ideology soup that we flounder in, and the best we can apparently say is “it’s going in the same direction” or “ceteris paribus”.

International Political Economy
Going with Cohen, one may well ask: what is the value of the forecast that ignores the context, or separates it in this way? Surely very little. As impressive as the economics or the modeling is, the results are are circumscribed by the larger questions that are not in the model, and that affect everything.

As an alternative, Cohen offers International Political Economy (IPE), which explicitly combines political analysis with economic theory, saying, “part of what IPE offers is a critique policy choices as ‘rational calculus by unitary actors responding to well-defined structural constraints and incentives – in effect, an approach akin to the analysis of atomistic firms in a setting of perfect competition.’” IPE suggests three levels of political analysis: the systemic level (macro-international politics); the domestic level, revealing competition of domestic interest groups and institutions; and the cognitive level, ideas that legitimate governmental policy making. If one is not thinking at all three levels of politics, any prediction will surely fail.

Whether IPE succeeds in mitigating the shortcomings of technical analysis or not, one can only say amen to the principle – and that, additionally, there’s surely even more to factor in. Beyond politics, there are issues of technology change, changes in culture, values, ideologies and perceptions that shape the future. Truth is, we don’t know how to quantify all this – and it’s certainly not tractable to quantitative measures for anything but the short term. Using the technical analysis to predict the euro’s status vs. the dollar in 2025 must return a result which (while even possibly correct) is one we cannot rely on.

Chapter 1: Recognizing Forecast Intentions, deals with considerations of how forecasts come about, who makes them, and with what intention. Those who research and produce forecasts, those who invest in understanding trends and drivers of change, and those (including the media) who bring the forecasts and their implications to our attention, inevitably have reasons for doing so – to benefit from the knowledge by seizing opportunities or avoiding threats or by affecting outcomes in the world. Understanding a forecast’s “return on investment” gives us an important vantage point in assessing the merits of a forecast.

Chapter 2: The Quality of Information, shows how a forecast communicates information between forecaster and reader subject to the same standards of accuracy, truth-telling, and bias-control by which one would judge any communication. Forecasts can be very different in methods and goals, but all forecasts lay claim to factual truth, particularly truth in the data, and the argument deals with the various ways in which data can be less solid than it looks, even with the best intentions.

Chapter 3: Interpretation and Bias, considers how data – whether good or bad in itself – can be interpreted or misinterpreted in forecasting, that is, the “political” aspects of forecasting. Just as there is no value-free look at history, so too there is no value-free look to the future and asking the right questions allows us be ready to mentally rebalance forecasts that are presented.

Chapter 4: Paradigms and Perception, investigates how predictive statements are exposed to a broader form of interpretive bias that has to do with the forecaster’s mental model or “paradigm,” and the “zeitgeist” (spirit of the times) when the forecast is made. This chapter investigates situations where forecast failure is caused by failure to escape society’s current mental models – which often do not hold through the forecast period.

Chapter 5: The Utility Principle, considers economic and market forces, and the role of consumers, in promoting or resisting the future. Without reigning in creative thinking, some simple economic filters inevitably apply direction or timing realism to futurist flights of fancy.

Chapter 6: Drivers, Blockers, and Trends, consider drivers and blockers of change, and how viewing these dynamics improves forecast assessment. It identifies the roles of Drivers, Enablers, Friction, and Blockers acting on events to cause change or resist it, and problems in dumbly projecting current trends.

Chapter 7: The Limits of Quantitative Analysis, discusses the role of statistical analysis and quantitative modeling in predicting the future – where this is possible and useful and where it is not, and why not.

Chapter 8: The Systems Perspective, investigates “system effects,” which occur whenever different elements or variables that may appear isolated are in fact linked together, such that changes in one element cause changes in others. Anticipating future behavior of any variable hinges on identifying the broader systemic elements influencing it and failing to do this is a big part of what causes forecasts to fail.

Chapter 9: Living with Alternative Futures, investigates non-predictive ways of approaching change – where the tone is more about managing uncertainty than predicting the future. It acknowledges unfathomable complexity of most future questions and provides perspectives that raise chances of success in an inherently unpredictable future.

Chapter 10: Forecast Filtering in Action, illustrates the processes of the book by applying them in case studies to real-world sample forecasts that decision makers in business and policy areas might find themselves interacting with. This demonstrates how real everyday predictive material may be probed and critically evaluated, following the principles developed in previous chapters.

Chapter 11: A Forecast Filtering Checklist, is a cross-cutting checklist which summarizes the principles of the book in one convenient, thematic list.