Herman Kahn

Interesting times we live in, when most of the world’s business media has a front-page tab on their Web sites that says something like “Euro Crisis – Live – Follow Here” as if there was a hostage drama or bank heist on the go.

Perhaps it is a bank heist of sorts, in the frantic run up this week and next to the Brussels summit in on 8-9 December, where the 27 Eurozone leaders are expected to make some binding, if not bold, decisions.

There has been short-term market relief following the US and China’s undertakings to make dollars more easily available into the European banking system. But everyone knows that liquidity, while a problem in itself, is a symptom of the larger problem of sovereign debt. And sovereign debt is only a problem when lenders don’t see future growth such that loan capital looks safe at less than, say, 7%.

In the world of foresight we talk about the need to “think the unthinkable,” a phrase coined about Herman Kahn in the 1960s when he was making scenarios about the road to US-Soviet thermonuclear war. So I was curious to see this exact phrase pop up in various media analyses where implications of Euro-demise, such as redenomination risk, cross-border contract liability, and so on are getting a thinking through, at least in the media, for example here in the WSJ.

Adaptive Measures

This is scenario planning “lite”: thinking down the path to, and implications of, a plausible operating environment — even if it is highly unlikely — and determining best responses, necessary hedges, and other adaptive measures. (Non-lite would be to do the background work, not just the journalistic summary.)

As the unthinkable forces itself to be thought, even the Corporate Executive Board was motivated to put the injunction to their executive partners as follows: ”As the threat of a potential euro zone breakup looms, we strongly advise companies to enhance their scenario planning disciplines. Leading companies in our network begin by documenting project assumptions and building scenarios off of those variables to test profitability under a range of outcomes before committing capital.”

But to this they add the intelligent real-world rider, often missed by scenario-ists: ”Don’t make the mistake of assuming that entire projects, P&L’s, or budgets need to be reconfigured under volatile outcomes. Instead, build your contingency plans around critical, controllable line items.”

I’m going back to the S+B interview with Lawrence Burns, former GM head of R&D, cited in my previous post, because there is more to be had in understanding how systems dynamics has shaped and will shape the future of the automobile industry.

This not only helps us think about automobiles, energy, public transport, and so on, but also about foresight in all industries.

Asked about the likelihood of “transformational” change in the auto industry—given the historical pattern of slow, incremental change we have seen for decades—Burns says:

“The main reason upheavals haven’t happened is that the automobile transportation system benefited from a tremendous self-reinforcing dynamic: the codependence between the roadway infrastructure, the energy infrastructure, and the machines that we created.”

In other words, systems dynamics were at work, in this case dominated by a deeply powerful reinforcing loop:

“As cars became available in the early 1900s, you needed to build roads suitable for them, and the costs of the roads were paid with gasoline taxes… As more cars were manufactured, more gasoline was consumed; the more gasoline was consumed, the more roads were built. The more roads were built, the more valuable a car became. And as cars became more valuable, it led to more cars being bought… Next thing, you wake up and in the United States you have 250 million cars, and they travel on 4 million miles of road, 3 trillion miles a year…

“So we thought about a new DNA for the automobile, but you couldn’t create that just for the car itself. It has to operate within a new codependent system.”

Too smart to crash

What drives this new system, is of course the core of the debate. In Burns’ view the key issue is vehicles will become “too smart to crash,” allowing them to be built without current safety defences, that is, 75 percent lighter, which drastically reduces energy requirements.

“The problem with batteries today isn’t really the batteries themselves; the problem is the vehicle that we’re putting them in. To power a typical car today, you need a battery the size of one or two Sumo wrestlers, and it takes eight hours to recharge, so you need charging stations in garages or on the street. For the 750-pound class of vehicles that we envision, the battery could someday become small enough so that you could easily bring it into your house or apartment to recharge, and it would recharge in just three hours.”

Everything rests on the assumption of whether “too smart to crash” is possible, and the secondary assumption whether consumers will ever really trust this. The former is surely sound, the latter questionable.

But, no matter. At least this view of the automobile industry evolution, or indeed revolution, is in clear acknowledgement that one will not see the future of the auto industry by looking through the lens of a single issue such as global warming, or any single propulsion or other technology.

The car is inextricably tied to the deeper systems it is part of. Any transformational future proposed or envisaged—whether that of Burns, or environmental lobbyists, or public transport evangelists, or any other—has to show how the whole current reinforcing system behind the car is overcome, that is bettered for most consumers and stakeholders, by a new system.

First posted at Forbes Leadership: http://blogs.forbes.com/adamgordon

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In its own words: “Global Voices is a community of more than 300 bloggers and translators around the world who work together to bring you reports from blogs and citizen media everywhere, with emphasis on voices that are not ordinarily heard in international mainstream media. Global Voices seeks to aggregate, curate, and amplify the global conversation online – shining light on places and people other media often ignore. We work to develop tools, institutions and relationships that will help all voices, everywhere, to be heard.”

There are of course other places to get local-blog perspectives on current issues and concerns, but this site appears to be the broadest and best, at least at the moment.

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Why is this important for thinking adequately about the future?

The biggest problem we have in foresight work is the double-whammy that (a) people, on aggregate, choose and make the future, and (b) we don’t know what they will choose because we don’t adequately listen to their concerns and motivations, or worse, are we are deaf to their motivations because they are outside of our frame of reference.

(a) Yes, the future is influenced by new capabilities, driven by new technologies, but technologies come out of societal perspectives (what are we going to invest in or research towards?) and then adoption (which technologies “make it”) is all about social and economic choices. So what defines the future is what most people want. (Not everyone wants the same thing: that’s what politics is about.)

(b) Share of voice is political too, and in our world some people and companies have vast sway over media channels, but most have no voice. But just because they have no voice doesn’t mean they are not making choices as to (a) above. All it means is that if you’re not listening, the future will surprise you.

A “surprise future” = a lack of mental preparation. Without exception.

It is easier both practically and ideologically to listen to ourselves and our micro-communities of associates online or off, which confirms what we think and how we think. It’s much tougher to absorb alternative perspectives. Global Voices is not perfect. It is still, naturally, the preserve of the literate and educated. But it is a first step out of the frame.

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The harder task in achieving quality foresight is to judge across such lists what is really going to change the world and therefore the operating environment for most firms, and what is just, well, merely of passing interest. The true test is to get trend impact right, not merely to call the trend.

There is no exact science to this of course. But a good heuristic is to judge the strength of the trend (drivers for vs. blockers against) x change to status quo (how new is this really?) x number of people affected. In this regard, a recent FT article reports on a genuinely world-changing trend.

The story is about how Indian television stations, led by Murcdoch’s Star India and Viacom are writing more independent, assertive roles for women in soap operas to reflect new realities in the Indian middle class. They hope to renew viewer ratings, as this clip explains:

New womens’ roles and aspirations have permeated Western society in the past generation and a half, and have profoundly changed everything from dress to daycare. Now the other 5 billion are going there too.

I would not (I stress) expect the Western model to be followed to the letter. Cultures always interpret world trends and technologies their own way. But billions of girls are growing up to be unlike their mothers in key respects, and will demand industries  — not just the media — move with them, and will reward those that do with unprecedented commercial opportunities. That’s a certain future.

I was lucky enough to be asked to do the opening address at the conference, and even luckier in that this Web site: South Africa – The Good News summarized some of what I and others said:

“Adam Gordon, Foresight Project Director and author of “Future Savvy” gave us some pointers:

1. Beware of sector experts, they are deeply entrenched in the present.
2. The consumer and choice is the determinant, not technology.
3. Change is about overestimating followed by underestimating.
4. Trends are patterns in the data, behind the trend are enablers and drivers, but frictional forces exist and in front of the trend are turners and blockers.
5. Trend extrapolation is limited, don’t fall foul of the turkey syndrome.
6. There is well behaved and badly behaved change. Both can be predictable and unpredictable. The potential of sudden shifts always lurks.
7. Scenario planning wraps up the key uncertainties over which we have no control.

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“The ‘BIG’ question he asks is ‘when do we influence the future and when do we adapt?’ There are big predictable forces out there (like population growth / the diminishing availability of oil etc), and there are big unpredictable forces out there (ja, well no fine!). Importantly, we can design our ability to influence and we can design the way we adapt. It is critical that we are able to do both.

“But managing the future is more than just about scenario planning, it is also about the implementation of the plan. It is about developing a methodology that prioritises, engages with stakeholders, and enables proactive actions on the ground.

So how?

Some important considerations (from various speakers):

1. Often we know what causes the problem (poverty, crime, HIV) but we don’t know what to do about it.
2. Often the logic that gives rise to the problem is not the logic that will solve the problem.
3. Mostly the problem does not contain the makings of the solution.
4. Solutions in one area can exacerbate problems in another.
5. The current situation has momentum, change to the system should happen concurrently not suddenly.

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“What is critical is the foresight process, it must be well-informed so that the implementation strategies that follow have buy-in, are doable, are relevant and far-reaching. There is a very real danger of visions being disconnected, unachievable and, at the end of the day, a pipe-dream.”

Dr Elizabeth Dostal talked of a stakeholder democracy in which she promoted the design of a matrix that recognised different stakeholder levels on the vertical axis and different environmental dimensions on the horizontal axis. A multi-level, multi-dimensional model.

“Imagine” she said, “putting four Nobel Peace laureates together and asking them what the causes of global conflict are. One may argue poverty, another ideology, another resources, and another greed. In no time, they would all be in different silo’s defending their view, in one sense they are all right, but in another sense they have not looked at the whole picture. A multi-level, multi-dimensional model would reveal this, the gaps in their logic, and the opportunities for agreement.”

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.”

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.

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

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

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

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Probability

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

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

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

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

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

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

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

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

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Scenario thinking

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

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

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

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

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

DJIA since Sept '08

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

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

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

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

Prediction Markets

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

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

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

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

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

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

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

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

Quality, reloaded

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

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

Samuel, L., Future: A Recent History, University of Texas Press, 2009

I recently received a copy of Future: A Recent History to review. True confession: what hit me first on picking up the book was (a) “wow, the title Future is not already taken!? And (b) what a fabulous job the University of Texas Press has done producing this book. It is beautifully designed, with an understated Art Deco motif, and carefully laid out with enough text on the page, on delightfully solid paper stock.
It may seem odd to go on about text on the page, but it’s much easier to read like an adult, in paragraphs. So many books, particularly business books, these days appear produced at 14-point, double spacing, like pre-school readers. Makes you wonder…

Anyway, author Larry Samuel’s project is to investigate the history of views of the future from 1920 to the present. (The book has an acknowledged US-centric focus, partially defended by the notion that future-mindedness is “a principle strand in America’s DNA.”) He organizes the book chronologically into six periods between then and now, and shows, with interesting examples, how each period had its own views of the future, and how the views shifted from period to period.

In tracing the history of “tommorowism,” in this way, Future is on a similar track to the classic book in this field: I.F. Clarke’s The Pattern of Expectation 1644-2001 (Jonathan Cape, 1979). It ultimately makes similar points, although Samuel’s argument is obviously drawn from more recent examples. As Samuel puts it: “A look back on how people looked forward reveals that while it possesses certain common themes … the future is not a fixed idea but a highly variable on that reflects the values of those who are imagining it.”

Happily I can say this chimes exactly with the argument of Future Savvy, particularly Chapter 4 “Zeitgeist & Perception,” where I argued how heavily the nature of the present and its topical issues frames how the future is seen (what is forecast, what is aspired to or feared, what counts as a valid method for thinking ahead, and so on). Which means the framing conditions of the present  should be carefully analyzed in assessing the validity of any future view.

Historiography

Historiography – investigating the meta-conditions surrounding what is recorded and how it is interpreted by historians – what counts as “history” and for whom –  is a well-understood part of doing good history. Unfortunately, there is no equivalent standard “futuriography” in the foresight field, despite it being absolutely fundamental to understanding the value of our own predictions as, similarly, highly determined by the epistemic configurations of their production. It is here that Samuel very competently fills a much needed gap.

The practical implication of this, which Future does not get into – it’s not that kind of book – is that to make better predictions (or make valid assessments of others’ predictions) we need to ask stiff questions as to how much of what we foresee is determined by the perspectives of today, and expect the answer to be “very much.” Understanding the limitations and biases of our own perspective is the sine-qua-non of a robust view of what tomorrow will actually bring.

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

One doesn’t start reading a “Brief History of” book in a series that includes A Brief History of the Birth of the Nazis and A Brief History of British Kings & Queens, without a certain trepidation. But, in fact, A Brief History of the Future is well-considered and well-written summing up of the characters and concerns that have shaped and continue to shape the future studies field.

Strathern, is a British journalist-turned-futurist, based in Vienna. One of the key attributes she brings is a journalist’s (and sub-editor’s) critical “don’t-bullshit-me” faculties, which is welcome in a field that is often short on common sense.

The book is hardly brief (at 300 pages) so there’s no sense that it’s a potted history. And it’s not compromised by what one – alas – expects of this kind of setup: pandering to all characters in positive or equal terms. In fact a key value of the book is its clear-headed and plucky judgment of who the key figures are (and who are not) and what their contributions have each been (vs what they might have thought they were). It is also unusually even-handed in balancing US and European inputs.

The book follows the obvious structure, starting with the oracles of Ancient Greece, Plato, moving through Leonardo de Vinci, and Thomas Malthus and so on through to the 19th century (Jules Verne, Karl Marx, etc.) and on to the present. In this Strathern argues for and operates with a wide definition of futures work – including in the dreamers, social reformers, and sci-fi writers in addition the more formal analysts and planners.

20th Century Weltanschauung
The book really hits its straps in the 20th century – in discussions of Aldous Huxley, H.G. Wells, George Orwell, Arthur C. Clarke, Herman Kahn, Buckminster Fuller, Alvin Toffler, Isaac Asimov, Margaret Mead and many lesser known figures. What is most interesting here is how the links between foresight approaches and the evolving broader existential and political “weltanschauung” of the century is knitted together, inserting “futurology” into the 20th century world of ideas at each point.

Although the book deals with institutions of foresight pretty well, the one angle I missed was the development of foresight education over the past 40 years. Part or full university degrees in foresight methods are an important part of the evolution of the field. Much has been learned in the debates over what and how and where to teach it. Ironically, the book – as intelligent a summary of the “future studies” field as you will find – would be an ideal text for an introductory course in such a curriculum.

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

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

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

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

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

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

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

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

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

Playing the game

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

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

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

The environmental pitch is a new salvo in the old “war on drugs,” which has been waged backwards and forwards, over decades now, without being won. It’s worth stopping to think why it has not been won, because it’s a salutory lesson in thinking about the future. It has nothing to do with the morals of “pushers” or willpower of “addicts” or the “the youth of today.” It is perfectly explained by the reinforcing loop (aka viscious/virtuous cycle) that dominates the drug-prevention system. This can be diagrammed as follows:

Alternatively the identical idea may be represented as a “fixes that fail” archetype, as defined in “The Fifth Discipline Fieldbook” (Peter Senge et al, Doubleday, 1994, p125).

For more on systems thinking see The Systems Dynamics Society. The role of systems thinking in improving our understanding of change (or non-change) is also the topic of Chapter 8 of Future Savvy.

So, yes, these are simple charts. We could make them more complex by filling in details of all agents and institutions at work in drug supply, demand, and prevention – but this would only elaborate, not alter the logic of the system. Either way, the chart allows us to see the wood for the trees, which is that drugs and their prevention are in a reinforcing loop. While it appears that preventive laws and their enforcement will lower drug use, in fact law enforcement constrains availability, pushing up the price, which makes production more attractive, which creates incentives to farm (incl, in rainforests), which raises supply, which leads to drug pushing (marketing by another name), which leads to drug trial, usage and addiction, therefore social concern, and therein renewed pressure for stricter legislation and crackdown, which sends the loop round once again. (There are many side effects of this main loop, including increased street crime – funding drug habits; the creation and enrichment of gangs and warlords; and so on.)

Scratching doesn’t help

Nobody in their right mind wants this to happen. But even a kindergarden child can see that policing and jailing, like a good scratch, feels good in the short term but just drives the wheel of the problem in the long term. What are the alternatives? From time to time pressure is brought to bear on production, for example, trying to obliterate coca or poppy fields, or disrupt supply chains. But this is also hopeless because as long as there is a good price to be had, the systemic reality is that drugs will be grown, produced, and shipped. What shows great promise is tackling price. The legalization lobby is all about capping price by making drugs legal, supervised, available, and free (or low-price), removing the superprofits from the industry and thereby blunting the primary interest of drug bosses and warlords. (This is what happened when Prohibition was repealed).

From a systemically informed viewpoint, only a solution that changes the system (interrupts the reinforcing cycle) can change the future. In other words nothing significant will occur in the future until the system changes, and removing drug barons’ price interest is the only way to do it. Until this happens the savvy forecast must be: no change.

Educating consumers

But the public is not ready for such policies. So we are left with the holding pattern we are in. And this includes exhorting the consumer, as Vice-President Calderón is doing. (The same story and interview was featured earlier this week on Radio 4′s “Today” Show.) He’s targeting the middle class, occasional, and recreational drug users who, he says, otherwise recycle, and compost, and “drive a hybrid” and buy fair trade coffee, and so on, and so should be desist from drug use because of it’s environmental impact.) This is not the first time that consumers have been “educated” – school and public education programs consistently target, inform, and discourage consumers and would-be consumers (including, of course, in the laughable “Just Say No” campaign.) All good or at least harmless work, in a good cause.

Into this Calderón has added a new-to-the-industry category of demotivator – the environment. Sure, this should work in giving middle-class consumers pause. But if environmentally sensitive cocaine customers are a big part of the market — and it’s hard to tell if they really are — expect producers to just respond with Green reassurance, real or fake: “No trees were ploughed under in the creation of your snort.”

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

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

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

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

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

This is the item:

There’s nothing new about this of course – the product has been around for a while, and ways to take vinyl and digitize it have been offered since the CD became the music industry standard in the mid-1980s. What’s interesting is that it is still being offered in 2008, more than 20 years after the technology transition. And still being bought, despite a sticker price of gpb 299 (nearly $600. In fact, this is the special newspaper-tie-in deal price.) The producers and marketers have, no doubt, done their homework: there are still enough people out there with vinyl records to justify a product and a campaign, including big newspaper spots that don’t come cheap.

What does this tell us about the future, and about predictions? It illustrates a key principle in thinking circumspectly and more accurately about the future. Legacy investments and legacy situations are a reality. They often represent a significant slice of daily practice or market share, well beyond the time when things have, officially, moved on. For all practical purposes, in any future the past continues to exist for a long time.

A slow and measured exit
This is common sense. But often missed by breathless techo-forecasters whose eyes are fixed on the next new thing. The implication of many forecasts is, when a new technology emerges into the market (which often takes longer than expected) that is also when previous solutions fall away. Not so. Yes, sometimes a new product is clearly advantageous, and adoption is rapid and pervasive. But when there are real investments in prior systems and technologies, these typically work their way out of people’s lives slowly, often over generations. The transition takes longer than we think it will.

While they are still part of the picture, legacy systems work against change (“This is working fine for me, why should I shift?” or “I’ve invested heavily in this, I can’t afford to shift”). On the other hand, as evidenced by the LP2CD in 2008, opportunities in the legacy system, or in facilitating a transition to the new system, may exist and be significatn long after everyone’s attention has moved on.

There are legacies in all kinds of products and services. A case that is currently pertinent, as discussed in Future Savvy, is the existence of deep legacies in the automobile industry and gasoline-petroleum supply chain. Both petroleum supply constraints and carbon emissions worries are driving hybrid engines, new fuels, and renewable forms of energy (technology is not the obstacle here) but the reality is that we are all deeply invested in a legacy petroleum-automobile system, from the well to the refinery to the factory to the forecourt. Even when new / alternative energies are proven, reliable, and equal in price and performance, the legacy will continue to exist, and it will erode gradually, as companies or consumers slowly renew their investment over time. Of course regulatory or social pressure can accelerate the incremental process, but nothing can make it vanish.

This means, in this example, there’s no possibility of a sudden change in individual land-based transport solutions. Whatever comes along will have to emerge into and live side-by-side with past systems and infrastructure for a very long time.

Legacy as luxury
Here’s another principle of legacy systems surviving into the future. There are many examples where a surpassed technology remains in existence, but moves into a niche or luxury market. The car replaced the bicycle and the horse, but both continue to enjoy massive popularity. In the developed world, more bicycles are sold than ever in history, but these are primarily for exercise or leisure. Horses, once widely distributed through society as instruments of work, are still part of a very active industry, but this industry is about leisure and/or gambling. Similarly, electricity replaced candles as our primary means of illumination, but candles are everywhere – associated with mood and romance rather than functionality. Ball-point pens squeezed the fountain pen off the table, but that merely freed the fountain pen to become an icon of status and refinement.

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.

Zeitgeist, German for “spirit of the times,” refers to the often unconscious spectrum of intellectual views, analytical approaches, political and social concerns, etc., that people in any era share. Evidence from the checkered history of predicting the future shows that forecasters have been very heavily biased by their then-current conditions, current issues, and current state of the world, that is by their zeitgeist. They see reality and therefore the future through the lens of their times. The key marker of this effect at work is when many forecasts are not only wrong, but are wrong in the same way.

The zeitgeist effect, by the way, is not a peculiar condition. It is part of the many common human and social cognitive-perceptual biases that exist. (For a longer list and their effects in forecasting, see Future Savvy.) Zeitgeist is in fact sometimes more broadly known as “situational bias,” where situations that people find themselves in frame what they see and how they interpret events. In a depression, famously, it is difficult to see any source of upturn; in boom times it is hard to see the crash.

The Zeitgeist of the 1990s
Just one example of zeitgeist bias in forecasting can be seen in predictions made in the 1990s, when the fall of Soviet Union and the end of the Cold War provoked new hope about global interaction and growth, and this, combined with the rise of digital technologies, the Internet, WTO agreements, and the dot.com market boom, fueled a new zeitgeist of optimism. It was very common, in this era to predict global prosperity, international peace and harmony, rising standards of living, enhanced personal freedoms, and a better environment. By the end of 2001, the NASDAQ bubble had burst, Al-Qaeda had struck buildings that symbolized U.S. power, the “War on Terror” had begun, and the entire rosy 1990s and all the forecasts that went with it, were finished. Zeitgeist or situation has, evidently, a very strong pull on what people think is possible and likely in the world at large, and in their own sector or industry. It frames the questions people ask, the topics they think are important, the outcomes they expect, and how they interpret signals of change.

The point is, do prediction markets somehow counter this well-known perceptual-cognitive bias in forecasting? No. The outcomes people think more plausible – that they will “buy up” in a prediction market – are deeply affected by situational conditions. This is sure to be a factor when, for example, Google sets up a market to tap its employees for forecasts. The people involved, smart as they are, will be strongly by situational factors – and this will affect which future outcomes look more likely.

Herd Effects
Moreover, they will be pulled in the same direction. As prediction markets, like Delphi studies, are particularly a consensus-based method (forecasters drawing predictive results from the study/game are going on what most people say/do), they are by definition deeply vulnerable to the zeitgeist effect. This dovetails with another well-known social cognitive bias, the “herd effect” or “bandwagon effect,” or “groupthink,” where people to believe or do things because many other people are doing or appear to be doing them. The only way to avoid groupthink is to isolate people from each other (as Delphi studies attempt to do by not disclosing others’ responses while the study is live.) But in prediction markets the player can, of course, see what others are doing. When buying a “stock” that is getting more expensive they may be reassured that others are buying it too — leading them to buy more, producing a classic herd-effect situation.

These biases – and various others, as detailed in Future Savvy, are among the many perceptual and cognitive biases that people bring to the world. They are part of (and evidence of) perception being active and constituent in our understanding of the world, and therefore of the future. We can chip away at our perceptual framwork, particularly by questioning our assumptions and investigating the basis of our knowledge (possibly through scenario planning.) However, no matter how astutely we look and how consciously we try to eliminate them, our paradigms exist, and they color our forecasts, and prediction markets do not make them go away.

First, what’s if all about? If you already know, skip this section. Let’s start with the example in yesterday’s Telegraph: “Predicting the future – with the power of betting” Paul Parsons, August 19, 2008. As Parson’s reports, the University of Iowa is running a market where investors can buy “shares” in the two major US election candidates, each priced between $0 and $1. On election day, traders holding stock in the winner – Obama or McCain – receive $1 per share while the others lose their money. Investors can buy and sell their shares along the way, and as they do this the candidate more people will want to own (because they think he will win) will get more expensive. In other words, market forces will drive up the price of the outcome more people think more likely. As of August 19, the trading value of the Obama, at $0.62, suggests participants expect a 62 percent chance he will win. (Another prediction market site, midasoracle.org, has the figure currently at 59.8 percent.)

Prediction markets mimic stock market and deploy much the same software. Where a real market trades shares in an underlying asset, in a prediction market it is future outcomes which are “securitized”. The key principle at work is the sage market wisdom that “the price of a stock captures all the information known about it” – that is, all information is factored into the price (notwithstanding that some may have more or better information than others; some may be acting more wisely on their information). Therefore price is our guide to the cumulative knowledge of all participants and, in prediction markets, this “price discovery” allows us to know what most people think the future holds. They allow the “the wisdom of crowds” to be turned to a future problem, and tapped.

Serious Success

What’s exciting about all this is its success rate. Prediction markets are amazingly accurate in many circumstances, and by all accounts consistently beat more conventional quantitative and extrapolative methods. Prediction markets have consistently out-predicted election opinion polls and exit polls. Of course the predictive potential goes way beyond polling. Forecasting markets can and have been set up to predict the dollar movements to the success of same-sex marriage legislation, to who will win best actor Oscar. At one point there was even a US government market in future terror targets (trying to elicit public predictions of likely targets so as to plan accordingly) but this was deemed inappropriate and taken down.

As it has become clear that this method outstrips conventional forecasting methods, prediction markets have taken root in forward-looking businesses. Companies such as Google and Hewlett-Packard routinely use (internal) prediction markets to forecast sales figures, customer preferences, product adoption, and so on. HP is on the record as saying prediction markets consistently outperform their official forecasts.

The method has other advantages too. First, it requires no special techniques or expense. There are no fancy models to apply or complex algorithms to … to do whatever one does with such things. Second the forecasts are available in real time, all the time, and constantly update themselves. There’s no waiting for data collectors to collect, or statisticians to emerge with their answers.

The Limits

In my book, Future Savvy, I show how and why humans are poor at predicting, for dozens of reasons. The record of predicting is littered with failure. But, is that now all in the past? Do prediction markets solve the perennial problem of predicting the future, or at least get us closer? Yes and no.

Yes where prediction markets are appropriate. They work best under two conditions: first where there is a clear view of the options and operating conditions; second (related) where the time frame predicted is relatively short, usually under 18 months depending how fast things are moving. Where predicting the future means choosing between known alternatives, such as an election winner, or anticipating a point along a known continuum, for example the level of next year’s sales, prediction markets are great.
Where prediction markets run dry is in dealing with unfamiliar conditions, or unknown variables, or potential game-changing disjunctures in the world. Where the future is seriously fuzzy, where there are many variables, and the way they interact unknown, and drivers, blockers, and lags are hidden, prediction markets are of limited use because the outcomes can’t be framed adequately so that people can bet on them or against them. A prediction market for US president in 2012 would be far less useful than 2008. Similarly, while a market for the oil price in 2009 would be helpful, by 2010 or beyond factors driving the price may be so different (viz. developments in sustainable energy or geopolitics) that the result of a prediction market conducted in 2008 would be undependable.
So while prediction markets sort out probabilities between known likelihoods, they are not adequate to the task of investigating complex situations where we cannot frame the likely outcomes, or at least can’t know if we’ve framed them right. Also while prediction markets do help us, on aggregate, avoid some perceptual/cognitive fallacies, they are as likely as any other predictive tool to fall into the Zeitgeist effect. More on this soon…

A good list of articles on prediction markets is available here: http://www.midasoracle.org/best/

This was a fairly typical example of the Intro Workshop in Scenarios program that I run, so I’ve decided to post it on SlideShare, see link below. Let me know what you think.

What is different about this course at this venue, particularly, is that the attendees come from a wide spectrum of industries and sectors (from Nestle strategist to the Canadian military planners, to Mauri sustainability experts, and beyond), and have a very wide background/preparation in futures tools and methods. There were relative experts in the room, and some absolute novices. … nothing like a challenge for the facilitator!

Slides from the day are at http://www.slideshare.net/adgo/how-to-build-and-use-scenarios-day-workshop

some pix: