Fired – Villas-Boas. Picture: uefa.com

The leadership brouhaha of the week was the sacking of Chelsea Football manager André Villas-Boas after only 8 months in charge. This means the London soccer club is looking for its eighth manager since Russian billionaire Roman Abramovich bought it in 2003.

When not firing managers, Abramovich is famous for, among other things, spending $52,215.34 on a lunch for 6 in at Nello’s in Manhattan.

Villas-Boas’ error, like those fired before him (bar one who quit) is he didn’t win enough fast enough. In his charge the team won three of their last 12 Premier League games, and face exit to SSC Napoli after loosing the first leg of their UEFA Champions League round-of-16 tie.

Villas-Boas’ remuneration for the year, including severance, is around the $20m mark.

In industry foresight, noticing extremes helps us see and interpret less visible changes in the world. Stellar pay and commensurately rapid churn at the top of Chelsea FC clues us in to what is going on in the daily mainstream that we may be too immersed in to register.

High pay is nothing new. Also, evidence of high CEO churn is on the radar. A 2006 University of  Chicago study showed CEO turnover was 17.4% per year 1998-2005, implying average tenure of less than six years. It related CEO longevity to three components of stock performance – performance relative to industry, industry performance relative to the overall market, and the performance of the overall stock market.

More recently, executive search firm Crist|Kolder studied Fortune 500 and S&P 500 companies and found that while average CEO churn dipped during the recession it was back on the way up, hitting 13% in 2011.

“Win Now”

What is less clear at this point is the correlation between higher pay levels and higher churn, that may play out more fully in the future. But if the trend is that CEOs are increasingly paid like sports (or sports management) stars — and they are — it’s reasonable to anticipate that this will be on an ever-increasingly short-fuse “win-now” basis.

If the analogy holds, we can expect chief executives to face shorter and shorter periods to justify their pay; probably the higher the remuneration the shorter the justification period. Where eye-watering sums are changing hands, fingers will be itchy on the trigger.

The poverty of management decision-making here is not just in embracing and fostering short-termism, the cancer of management. It is in prejudging and potentially wasting leadership talent, because short-term data is effectively no data. Put it another way, short-term wins or losses are at the mercy of randomness, such that what looks like good or bad results are almost always part of normal near-term fluctuation spread, as argued by Nassim Taleb in his book “Fooled By Randomness.”

Over the long term, twists of fate, or twists of ankles are ironed out, and quality prevails. Nobody would argue that Steve Jobs was not successful or not worthy of star pay. But Villas-Boas … won championships for Porto FC in 2010 and lost championships for Chelsea FC in 2011, and neither results tell us or Abramovich whether he’s any good or not.

The only thing we can expect with confidence is that where the sports-star model of remuneration migrates, Boards (or shareholders) will be eager to read too much into early data, and prone to make Abramovich-like decisions.

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.

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“The world around us is changing in ways that may have profound effects on the emergency management enterprise. Collectively, we must begin to think more broadly and over a longer timeframe if we are to understand these changes and their potential impacts. To this end, FEMA has launched a Strategic Foresight initiative (SFI), the objective of which is straightforward: to seek to understand how the world around us is changing and how those changes may affect the future of emergency management and our community…

“The SFI can serve as one important tool in the development of both strategy and plans. By understanding the potential future environment, organizations will better understand and anticipate risk while ensuring opportunities can be fully capitalized. For example, the SFI may identify new or increasing capability requirements as well as emerging capabilities that do not exist today. Such identifications could support decisions about future investments as well as planning activities and exercises. In a more indirect manner, the SFI can help establish a research agenda for the emergency management field by highlighting areas of emerging relevance and the key questions that remain unanswered.”

[On March 1, 2003, FEMA became part of the U.S. Department of Homeland Security.]

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.

Says Hamel, “The experience of growing up online will profoundly shape the workplace expectations of “Generation F” – the Facebook Generation. At a minimum, they’ll expect the social environment of work to reflect the social context of the Web, rather than as is currently the case, a mid-20th-century Weberian bureaucracy.

“If your company hopes to attract the most creative and energetic members of Gen F, it will need to understand these Internet-derived expectations, and then reinvent its management practices accordingly.”

He cites 12 work-relevant “the post-bureaucratic realities” that tomorrow’s employees will use as yardsticks in determining whether your company is “with it” or “past it.” These are:

1. All ideas compete on an equal footing.
2. Contribution counts for more than credentials.
3. Hierarchies are natural, not prescribed.
4. Leaders serve rather than preside.
5. Tasks are chosen, not assigned.
6. Groups are self-defining and -organizing.
7. Resources get attracted, not allocated.
8. Power comes from sharing information, not hoarding it.
9. Opinions compound and decisions are peer-reviewed.
10. Users can veto most policy decisions.
11. Intrinsic rewards matter most.
12. Hackers are heroes.

One hesitates to question Hamel, whose edifice of work, bookended by Competing for the Future (1994) and The Future of Management (2007) is as eloquent and substantiated a guide for innovation and future-thinking in management as you will find.

But, what is startling, for those of us around long enough to remember the Web-excited 1990s, which includes Hamel of course, is that these 12 principles are really old stuff, the mantras of the Internet 1.0 … the needs of Gen F are apparently not different to the needs of Gen Y.

But, now it’s a dozen years later, and this future is still the future. Hmm.

New management, but not in old bottles

Actually, surely Hamel’s beef is with the Fortune 500 set particularly, because what has happened is that most small and niche companies have already embraced a big chunk of these new-management attributes. It’s specifically the Fortune 500 that lags: but then, running organizations with stakeholders and budgets resembling mid-sized countries seems to fly in the face of Gen F value set.

Looking abroad, it appears that a Chinese factory or an Indian call center are not about to convert to Gen-F values either. Command and control, and uncreative hyper-attention attention to margins — effected by the Weberian bureaucracy — is the route to profit for them. The old paradigm will rule, and rule well.

From the Future Savvy vantage point, the real future will have, broadly speaking, two types of firm, the Weberian and the Gen-F. Firms running 19th century-type businesses will run them in 19C ways. Funky firms exploiting new ideas have already changed management style significantly and will continue to do so.

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

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.

No longer. The paradigm was broken by the Hans Rosling (Gapminder) video “Debunking Myths About the Third-World,” 2006. By Rosling’s own admission, his analysis is not based on new or better data. The (UN) data has always been there (yes now it’s becoming more available). But the seachange is new software which makes it easy to filter and present it in dynamic, graphic form. And, no surprise, this is popular. According to Gapminder, this video has seen by 500,000 people, not bad for a 20-minute treatise on perceptions of developing world countries.

Data turned into dynamic moving pictures is, one might say, required in our era (trends: visual literacy, short attention span, computing power) so thankfully we can expect more of this. What’s important, for forecast evaluation purposes, is the power of explanation and mental-model challenge that the improved communication provides. As Rosling says of his Swedish graduate students: “Their problem was not lack of data, it was preconceived ideas” (an outdated world view of “1st world” vs “3rd world.”) An endless amount of poring over dusty tables of figures would be unlikely to change that. But it’s hard to watch Rosling’s moving bubbles and not have one’s paradigm shaken.

Another site, in a similar vein, is worldmapper, a University of Sheffield initiative. Worldmapper communicates hundreds of world indicators, from infant mortality to military spending and so on, by manipulating the size of territory of each country to indicate presence or absence of the variable in question, as the following maps show:

Prisoners as percentage of population

Girls not at secondary school

Strikes and lockouts, 2002

Again it is basically UN data that is being sourced, but now presented in a way that cuts through the obscurity tells and the story much more vividly. As we know, humans “get it” better and faster via images than via words or figures. It challenges our perceptions in a way that figures in dusty tables cannot. They payoff is it’s harder to miss what’s really going on. So we have a better view of the world: our mental model aka ‘paradigm” more closely approximates reality. That means we will make better assumptions going forward which will, on balance (no guarantees of course), convert into better predictions.

Decision success always implies congruence between decisions and the world in which those decisions play out. If we decide today to launch a product, buy a house, study for a degree, build a new light rail system, or take any similar decision of significance, the environment of tomorrow will be a key factor in the success or failure of that decision. What we do will be tested by the future conditions that emerge. Where there is a good “fit” between the initiative and the environment it plays out in — “the right product at the right time” — we can expect success. If not, we should expect to fail. Our decisions are only as good as the view of the future they rest on. All opportunities and successes and profits are realized in the future. All threats, failures, and losses are in the future.

In a fast-moving world, we know that the future environment will be different to that of today in big or small ways. New technologies, market shifts, changes in legislation, or evolving social values damage or destroy the traditional good fit we have between ourselves and the world. To achieve “future fit” we therefore use forecasts to position ourselves and our organizations, creating (or renewing) the fit between our initiatives and environment. In some cases we may be strong enough also to influence future events and outcomes for our own future benefit, and forecasts help us do this too.

All enterprises benefit from narrowing down what they must adapt to and plan for – all effort spent preparing for a future that will not emerge is a waste of personal or organizational resources. Good forecasts are a key ingredient in limiting the vagaries of uncertainty, and therein working smarter not harder, avoiding surprises, exploiting new opportunities and plugging weaknesses in fitting in with the future, and where possible influencing the future to suit the organization. This is true not only of business. People and institutions of all types position themselves for success by anticipating and adapting to events, or shaping them. Whether it is an NGO raising money for developing-world children, an urban planner advocating a light rail system, a homeowner deciding to sell a house, or a student making a career choice, identical principles apply — a higher-quality reading of the future operating environment in which these decisions will play out is what separates winners from losers. We should all be vitally concerned with forecasts as we are all effectively betting significant resources on their validity.

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.