Future Savvy: Quality in Foresight

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

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

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

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

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

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

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

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

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

“Back to the sources of error.

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

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

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

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

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

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

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

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

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

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Preliminary results of the European banking stress test are to be published by the Committee of European Banking Supervisors tomorrow (July 23.) Although the exact nature of the tests have remained under wraps — not without controversy — the essence is clear. Regulators are simulating various forms of adverse financial conditions (GNP performances, interest rates, currency values and flows, and other money metrics) to see if important banks have the resources to withstand these conditions.

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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I’m always looking for ways to explain the role of quality foresight in everyday management, so I liked this little animated gif from managewell.com.

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.

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The strategy world has mourned the sudden passing of C.K. Prahalad, Professor of Business Administration at the Ross School, University of Michigan, this week.

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.

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

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

Alternatively it can be accessed directly at

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

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

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Charlie Munger, Vice-Chairman of Berkshire Hathaway Corporation, the diversified investment firm chaired by Warren Buffett, has a piece titled: ‘Basically, it’s Over‘ in Slate this week.

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.

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I’ve been flying across the world recently, which has given me a few quiet moments to read a real bona fide book, and the one I have been busy with is Peter Bernstein’s Against the Gods: the Remarkable Story of Risk (Wiley, 1996). It’s aclaimed all over the place, particularly in risk management circles, but I’d never quite got to it.

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.”

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

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

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

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

Cognitive bias

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

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

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

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

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

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

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

Balancing techno-fantasy

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

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

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

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

Back-street abortionists

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

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

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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.

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