Alex Pang's Future2: The Future of Futures

Another unintended consequences data point

From Paul Krugman:

In general, economists love models in which smart people make individually smart choices that end up being collectively dumb.

Automatically trading on the news

From the New York Times, this piece about using analysis of unstructured data in automated trading:

Math-loving traders are using powerful computers to speed-read news reports, editorials, company Web sites, blog posts and even Twitter messages — and then letting the machines decide what it all means for the markets.

The development goes far beyond standard digital fare like most-read and e-mailed lists. In some cases, the computers are actually parsing writers' words, sentence structure, even the odd emoticon. A wink and a smile — ;) — for instance, just might mean things are looking up for the markets. Then, often without human intervention, the programs are interpreting that news and trading on it.

Given the volatility in the markets and concern that computerized trading exaggerates the ups and downs, the notion that Wall Street is engineering news-bots might sound like an investor's nightmare....

Many of the robo-readers look beyond the numbers and try to analyze market sentiment, that intuitive feeling investors have about the markets. Like the latest economic figures, news and social media buzz — "unstructured data," as it is known — can shift the mood from exuberance to despondency.

Tech-savvy traders have been scraping data out of new reports, press releases and corporate Web sites for years. But new, linguistics-based software goes well beyond that. News agencies like Bloomberg, Dow Jones and Thomson Reuters have adopted the idea, offering services that supposedly help their Wall Street customers sift through news automatically.

Some of these programs hardly seem like rocket science. Working with academics at Columbia University and the University of Notre Dame, Dow Jones compiled a dictionary of about 3,700 words that can signal changes in sentiment. Feel-good words include obvious ones like "ingenuity," "strength" and "winner." Feel-bad ones include "litigious," "colludes" and "risk."

Models as self-defeating prophecies

Alex Pollock at the American Enterprise Institute writes about the role of models in economic science (or what "would be" a science "if it weren’t for the people") and financial decision-making. He argues that the widespread use of models tends to lead to their obsolecence:

Perversely, the more everyone believes the model, and the more everyone uses the same model, the more likely it is to induce changes in the market that make it cease to work.

In this cycle, the market and the regulators became enamored of the statistical treatments of risk, whereas the most important issue is always the human sources of risk. These human sources include short memories and the inclination to convince ourselves that we are experiencing "innovation" and "creativity," when all that is happening is a lowering of credit standards by new names.

As I understand his argument, there are a couple reasons for this. Some models-- ones that deal with very specific pieces of the future-- only work if they're obscure: if everyone "knows" that the price of magnesium is definitely going to rise, and everyone buys magnesium futures, the future price of magnesium changes. Models reinforce the belief that "this time it's different," and help people unlearn old, hard-won lessons. (As Pollock puts it elsewhere, one of the differences between science and finance is that scientists don't forget previous errors-- astronomers haven't gone back to geocentrism, and old ideas tend to die with old scientists-- while generational change in finance tends to wipe away wisdom, leaving only hubris and a belief in one's own youthful invincibility.) Models also tend to obscure the continued, lurking presence of uncertainty:

Because uncertainty is fundamental, sometimes disastrous mistakes will continue to be made by entrepreneurs, bankers, borrowers, central bankers, government agencies, politicians, and by the interaction of all of the above.

[Economics Frank] Knight wrote: "If the law of change is known, no [economic] profits can arise." Likewise: "If the law of change is known, no financial crises can arise." But in economics and finance, the law of change is never known. So change reflecting uncertainty goes on, bringing booms and busts periodically, and Adam Smith’s "progress of opulence" on the trend.

Have economists have tried to measure the impact of the popularity of models on markets? The Knight quote comes from his 1921 book Risk, Uncertainty, and Profit, and I have to assume that economists have tried to measure (a model, if you will) how widespread use of, say, a statistical model affects markets and either increases or decreases the reliability of that model. It seems to me that this would be one of those things that people would have tried to study, but I don't know enough about the field to know.

The New Abnormal in investing

NetNet, which is kind of like Matt Taibbi without all the swear words or Michael Lewis without the humor, has an interesting piece on investing in 2020, based on the work of investment strategist Nick Colas:

Colas draws some common-sense conclusions based on all the inflation likely to be pumped into the system and the higher tax rates that will be needed to pay for the trillions in sovereign debt floating out there and coming to maturity in the next decade.

Viewed through that prism, a 10-year forecast doesn’t sound so silly after all.

“I don’t think you can crush the US housing market and global banking sector, replace it with central bank liquidity on an unprecedented scale, see historically high and sticky unemployment, and witness rolling mini-crises of sovereign debt concerns without thinking that the landscape is going to be very different for a long time,” he writes.

Most of what I hear from really thoughtful people makes me very pessimistic about the long-term prospects for investment. My bad habit of not putting as much money in my retirement as I meant to (hey, everyone does it) has started to look shrewd.

Moving R&D overseas

Ed Luce in the Financial Times worries about the shift of R&D in American (or American-founded) companies to China, and hopes that the departure of Larry Summers will occasion a rethinking of neoliberal trade policies.

Take Applied Materials, a big US manufacturing company, which earlier this year shifted its chief technology officer and research and development operations to China. The company said it needed its R&D to be close to the source of its manufacturing operations and to its biggest future market. This is the opposite of what is supposed to happen. America was meant to keep the high-end jobs at home, while China would get all the low-value added production.

When I was at IFTF I wrote about the gravitational pull of manufacturing, and argued that it's actually very hard to disaggregate R&D and product development (the high-end creative stuff that we were supposed to keep here in California) from manufacturing (which could be done by teenage girls and brown people in-- well, who gives a damn where it's done). This is true for several reasons. Historically, some of the biggest innovations have emerged in the course of working on production problems (Bill Leslie's work explicates this relationship nicely). As designers know, creating something that can be mass-produced is not a trivial intellectual exercise: there's a special kind of genius necessary to make prototype (like the computer mouse, say) and turn it into a product. Manufacturing high-tech objects can be pretty damn hard, and requires more tacit knowledge and skill than we usually realize (Intel's Copy Exactly program is a great example of a company's attempt to get its hands around this fact). Finally, if a company has to choose between having imaginative research and getting products out the door, it's going to subordinate the former to the latter.

Not everyone sees this as a bad thing. Matthew Yglesias, for example, argues that

the alleged need for R&D to be proximate to manufacturing options (plausible) cuts in both directions. Conventional wisdom is that manufacturing operations will all drift to low-wage countries. But if the USA is a better location for R&D than China, and if it’s strongly desirable to co-locate R&D and manufacturing operations, then many firms will want to retain manufacturing operations in the United States of America. So if this story is right, then more and better education for America is the key to retaining high-wage manufacturing jobs.

I think this is wrong for two reasons. First, it's a lot harder to move the infrastructure for manufacturing than it is to move R&D, and it's very hard to get a factory back (or just as important, the skill necessary to manufacture things) once you've lost it. It's much easier to have production engineers fly to Taiwan or Shanghai every 6 weeks (though it sucks for those people). Second, it raises the question of why manufacturing jobs have left the U.S. at all, if R&D can attract manufacturing?

File under "self-fulfilling prophecies"? The Hindenburg Omen looms

We'll know in September. From the Wall Street Journal:

Forget about Friday the 13th. Many on Wall Street took to whispering about an even scarier phenomenon—the "Hindenburg Omen."

The Omen, named after the famous German airship in 1937 that crashed in Lakehurst, N.J., is a technical indicator that foreshadows not just a bear market but a stock-market crash. Its creator, a blind mathematician named Jim Miekka, said his indicator is now predicting a market meltdown in September.

Wall Street has been abuzz about whether the Hindenburg Omen will come to bear, with some traders cautioning clients about the indicator and blogs pondering all the doom and gloom.

There's a technical but still accessible explanation of it on Zero Hedge. I confess I haven't heard of it, but apparently the fact that it's "easily the most feared technical pattern in all of chartism," combined with the fact that warnings about it came out on Friday the 13th, provides something for everyone to worry about.

On the use of economic forecasts

Tyler Cowan asks, "Why does anyone support private macroeconomic forecasts?"

I've long found the market for such forecasts to be a puzzling practice.... Even if some forecasts are quite useful, what's the value of supporting a marginal or additional forecast? Is the next forecast to come along so much better? Forecasts would seem to be the classic example of a public good.

Given my interest in turning scanning from a private resource into a public good, Tyler's question caught my eye. He suggests that it's not about getting better knowledge at all, but about something more like social status: perhaps

outsiders pay for the forecast to join a more exclusive club of clients with other privileges. It's a bit like how art galleries won't sell their best pictures to complete outsiders but instead ask that you "pay your dues" by being a loyal customer for years. In other words, it's an arbitrary fee to enforce price discrimination, backed by some plausible pretext.

Under a related model, the firm pays for the forecast as a means of generating publicity, signaling its size, seriousness, and audience, and in general marketing itself to outside clients. It is unclear who bears the final incidence of these expenditures, the firm or the clients, but still "forecasting isn't about knowledge," as Robin Hanson would have said to the oracle at Delphi.

In other words, the "utility" of macroeconomic forecasts doesn't come from the actual information you get by buying them, but from the signal buying the forecasts sends to competitors about your seriousness. I've heard similar kinds of arguments about futures and scenario work-- that Hermann Kahn's work on nuclear scenarios in the 1960s was intended not just to improve the Joint Chiefs' or SAC's thinking, but to make the Soviets worry about what we going to do in the future-- and I think there are almost always local reasons for engaging futurists that don't have much to do with the official reasons you hire futurists. (Indeed, the really good consultants are able to figure out these reasons, and to adjust their engagements accordingly.)

Bruce Bartlett proposes another answer:

Why Tyler may not realize is that forecasting companies do far more than generate aggregate data; they also produce a vast amount of industry specific data that is enormously useful for investors, managers and others that need to know how a particular industry is expected to perform given the forecast for GDP, inflation etc.

In some cases, the industry data may even contribute to price collusion.

I should note that there's an excellent study of futures orientation, forecasts and uncertainty in forestry-- a profession that sees itself as managing processes that play out over centuries-- but we could use many more studies of how our work is really used.

Another unintended consequence

The Miami Heat will be able to offset Lebron James' huge salary by not having a season ticket sales staff:

The Miami Heat easily sold out its season tickets after LeBron James announced he was joining the team. That turned out to be bad news for the ticket-sales staff, which the Heat fired Friday.

(via Marginal Revolution)

The very long shadow of the history of technology

A confession: when it comes to thinking about the future, I hold two views. On one hand, I find the black swans work of Nassim Taleb-- the argument that the speed and complexity of the modern world has left it vulnerable to more, and more unpredictable, crises-- pretty convincing. (Call this the New View.)

On the other, I also believe that much of what we claim is novel about this modern age is not so new. Many facets of globalization-- the importance of migration, global trade, etc.-- are actually as old as civilization. I also believe that other things, like a belief in greater vulnerability to epidemics and financial panics (or just as worrying, the belief that we are now immune from such things), are a product of a relatively short-term view of history. You could better understand our current world, and think more clearly about the future, if you stretch your view of the past from the last 50 years to the last 500 or 5,000. (Call this the Long View.)

Obviously, the New View and the Long View are contradictory. I get around that by not thinking about both of them at the same time. But I'm trying to construct a framework that fits them together.

This morning I ran across another data-point in the Long View: a new piece by Diego Comin, Erick Gong, and William Easterly looking at very long-term trends in technology and economic development. As Easterly explains,

We collected crude but informative data on the state of technology in various parts of the world in 1000 BC, 0 AD, and 1500 AD.

1500 AD technology is a particularly powerful predictor of per capita income today. 78 percent of the difference in income today between sub-Saharan Africa and Western Europe is explained by technology differences that already existed in 1500 AD – even BEFORE the slave trade and colonialism.

From the abstract (pdf):

The emphasis of economic development practitioners and researchers is on modern determinants of per capita income such as quality of institutions to support markets, economic policies chosen by governments, human capital components such as education and health, or political factors such as violence and instability.

Could this discussion be missing an important, much more long-run dimension to economic development?... Is it possible that history as old as 1500 AD or older also matters significantly for today’s national economic development? A small body of previous growth literature also considers very long run factors in economic development.... This paper explores these questions both empirically and theoretically. To this end, we assemble a new dataset on the history of technology over 2,500 years of history prior to the era of colonization and extensive European contacts.... We detect signs of technological differences between the predecessors to today’s modern nations as long ago as 1000 BC, and we find that these differences persisted and/or widened to 0 AD and to 1500 AD (which will be the three data points in our dataset, with 1500 AD estimated from a different collection of sources than 1000 BC and 0 AD). The persistence of technological differences from one of these three “ancient history” data points to the next is high, as well as robust to controlling for continent dummies and other geographic factors.

Our principal finding is that the 1500 AD measure is a statistically significant predictor of the pattern of per capita incomes and technology adoption across nations that we observe today.

Of course, one can get into how this is a different set of forces than most futurists are interested in-- but to the degree that it serves as a corrective to the tacit view held some futurists that history doesn't matter at all-- a kind of social science version of transhumanism, in which thanks to technology (or migration or whatever) we're able to ignore the past and its gravitational pull-- it's worth reading and pondering.

Pro-poor foresight

I seem never to have pointed to IAF's Foresight for Smart Globalization project, which I read about ages ago and found very impressive. From the report's executive summary:

Pro-poor foresight is forward-looking analysis that focuses on poor and marginalized people by expanding their social and economic opportunities and by enhancing the social, economic, and ecological resilience of human society. Yet foresight, as generally applied within government, industry, and the non-governmental sector, rarely includes an explicit focus on poverty. While foresight exercises typically take into account the impact of long- term political, economic, social, and technological trends, the differential implications of these factors for the lives of poor people tend not to be addressed. Poor communities, however, will be disproportionately affected by the myriad and intractable problems of the 21st century, including climate change disasters, weak governance systems, financial crises, security threats, and societal disruptions....

The report explores three main ideas at the heart of the workshop: pro-poor foresight, anticipatory governance, and smart globalization. It also summarizes the real-world experience of participants in conducting foresight in different geographical regions and the barriers faced in applying foresight for decision-making. Subsequently, it describes three interlocking issues—energy and climate change, science and technology, and economic governance—that were discussed in tandem at the workshop.

In conclusion, pro-poor foresight provides an opportunity to approach the problems of developing countries in the Global South in a unique, interconnected, and more effective manner. Pro-poor foresight can catalyze insight in the minds of communities and decision-makers, forge new paths for action, and lead to understanding and embracing complexity. In short, it serves as a survival tool through which we, as individuals, as communities, and as a species can escape the bounds of present circumstances and achieve a measure of freedom of choice about our destinies.

Nils Gilman on reset versus reform

At a workshop in early 2009, I first heard someone (a very smart guy in the science parks world) say, "We're not in a recession, we're in a reset." Since then, I've seen variants of this phrase crop up here and there: Richard Florida titled his latest book The Great Reset. This morning, on Nils Gilman's blog, I came across this bit about David Harvey's current work on the crisis:

Whether you buy the Marxist analysis, much less the Marxist prescriptions, you can't deny the centrality of a point Harvey only makes in passing, which is that there's no way we can get out of this crisis the way we did the last time (that is, in the 1970s/80s), namely by re-disciplining labor to rein in costs and supporting aggregate demand by issuing lots of cheap credit. Those byways have been exhausted, and so how (or if) we can get out of this crisis remains radically unknown.

A smart summary-- just what you'd expect from a former David Hollinger student.

It strikes me that every group I've worked with in the last couple years has subscribed to the "reset not recession" school, which in retrospect is not at all a surprise. If you mapped where politicians and pundits were on the Reset/Recession scale, I wonder what you'd get? My sense is that most Republicans and Wall Street types are on Recession side: the former have proposed relatively conventional responses to the crisis (cut marginal tax rates again! privatize/"save" Social Security!), and the latter have been lukewarm at best to any proposals that reduce their freedom of action. Advocates of clean energy, sustainability, etc.-- the people who followed the back and forth of the Copenhagen conference most avidly, and were most disappointed by its failure-- are definitely on the reset side. You could keep going.

Can democracies deal with climate change?

That's the question posed by German academics Claus Leggewie and Harald Welzer. Unfortunately their book It's the End of the World as We Know It (yes, it's borrowed from REM) isn't available in English, but there is this essay:

Rising energy costs and the eco-social consequences of climate change are causing anxieties about the future to increase, while trust in the ability of political elites to solve these problems is evaporating. Reaching eco-political targets calls for more participation of citizens as active architects of their society.

Also read their interview, "Jolly Eschatology."

Felix Salmon on climate

Felix Salmon has a nice piece in Slate looking at corporate adaptation strategies to climate change-- or more accurately, the absence of such strategies. Why have few companies developed such things?

Adaptation strategies have essentially zero PR value. They have nothing to do with saving the planet. Instead, they're all about trying to thrive if and when the planet starts to fall apart.... Climate change takes place over decades, and corporate timescales generally max out in the five-to-seven-year range.... It's easy to talk about how hotel companies with coastal property might have to face more hurricanes or rising sea levels. But it's quite hard to know what is going to happen to any given beachfront resort with a sufficiently high degree of certainty. ... Finally, even if the effects of climate change are foreseeable, they can be impossible to hedge.

The behavioral economist Dan Ariely, author of Predictably Irrational, likes to say that climate change is a problem that is perfectly designed to make people do nothing: It happens far in the future; its effects will be felt most greatly by other people; and the efforts of any one individual are minuscule.

You could say the same for governments-- very few are capable of looking or acting decades into the future. However, I think we know enough-- thanks to people like Ariely, as well as the better strategists and business writers-- to construct tools that help us start to create the long-term perspectives-- think of them as counter-perspectives-- that would make it possible for us to deal with such problems.

When being wrong shows you're right

The recent New Yorker piece on Paul Krugman may have the most beautiful example of an "I got the future so wrong, which is why I'm so right" argument I've ever seen:

Last fall, Krugman wrote an article for the Times Magazine, “How Did Economists Get It So Wrong?,” about the profession’s failure to anticipate the financial crisis, and what that revealed about its failings in general. He accused his colleagues of mistaking beauty for truth. They were so enamored of the elegance of their models and the consistency of their logic, he wrote, that they had come to believe that assumptions that were originally adopted merely as tools (perfectly rational individuals, efficient markets) by Milton Friedman’s generation were so sacrosanct that economics wasn’t economics without them. Freshwater types, in particular, had forgotten the Depression, forgotten what Keynes had said about the resemblance of financial markets to casinos. So attached were they to the idea that markets always got things right that some actually suggested that unemployment must be a consequence of workers’ choosing not to work. Saltwater economists were less blinkered in their view of markets and the rationality of investors, Krugman wrote (Larry Summers, a saltwater type, once began a paper on finance by declaring “THERE ARE IDIOTS. Look around”), and had retained a Keynesian view of recessions as crises of insufficient demand. But even saltwater models had no room for such wild imperfections as bubbles and banking-system collapse. “Economists will have to learn to live with messiness,” Krugman concluded.

Reactions to his article were quick and outraged. “Who are these economists who got it so wrong?” a Washington University economist, David Levine, wrote. “Speak for yourself kemo sabe. . . . It makes me feel physically ill that a distinguished economist could be so ignorant of his own profession.” “How sad,” John Cochrane, of the University of Chicago, wrote. “Don’t argue with them, swift-boat them. Find some embarrassing quote from an old interview. Well, good luck, Paul. Let’s just not pretend that this has anything to do with economics.” Levine and Cochrane maintained that the fact that freshwater economists had failed to predict the financial crisis was not an embarrassment to their theories but a confirmation of them: “The central empirical prediction of the efficient markets hypothesis is precisely that nobody can tell where markets are going—neither benevolent government bureaucrats, nor crafty hedge-fund managers, nor ivory-tower academics,” Cochrane wrote. If professional economists failed to predict or understand the crisis, how could it make sense for Krugman to argue that bureaucrats would do a good job of curing it? [emphasis added]

Also, James Galbraith follows up on Krugman with a piece about the ones who did get it right. And this Michael Lewis piece about Wall Streeters who saw it coming is also great (as is everything Lewis writes).

Making calculation social. Or, how the iPad might reshape financial markets

The always-interesting Daniel Beunza has an excellent post on socializing finance asking "What possibilities does a tactile, mobile device like the iPad bring to securities trading?"

I’ll venture a guess. By bringing together calculation and social interaction, numbers and people, judgement and logic, the iPad may rejuvenate financial exchanges, making them more calculative. And also reinvigorate trading rooms in banks, freeing arbs from their chains of their desks....

New affordances create new needs. The challenge is to imagine those needs before they arise. Interestingly, Steve Jobs does not get this simple point. Or at least that’s what I got from watching his presentation of the iPad. For the tablet to be justified, Jobs said, it should let you browse the web better than a computer and a phone. Actually, it’s the opposite. The tablet should focus on new things that only a widescreen mobile wireless device can do. Social web browsing, for instance. Or situated problem-solving. Marrying mobility and Excel, flicker and pubs.

The whole thing is well worth reading. To me, it's a very nice example of how people interested in information technology and the future should think: a thoughtfulness about the many and various (and sometimes contradictory) ways technologies are used in the workplace, an awareness of the importance of affordances and ergonomics, and a willingness to think about the various ways technologies could play out in a context. Far too often futurists think about technology's impact in terms that are at once overly narrow and overreaching-- e.g., "the next turn of Moore's Law will bring about a collapse of the pet food industry!"-- and don't do justice to contingency and agency.

The "reverse Lake Wobegon effect" in forecasting

Forex recently reprinted a July 2009 article from Time asking "Why Are Economists So Bad at Forecasting?"

A 2003 study by researchers at the Federal Reserve Bank of Atlanta found that the Blue Chip Consensus Forecast, which polls some 50 economists each month, is consistently better than any of its individual members. The researchers dubbed that result a "reverse Lake Wobegon effect": everyone was below average. During economic turning points — like the one we're currently in — the individual forecasts veered further off the mark....

The problem is twofold. First, it's hard to predict the future. Second, it's really hard to predict the future when so many parts of the economy are in flux. "This has been an extraordinarily difficult period for forecasters," says Harvard economist James Stock. "Our models aren't really designed for predicting massive changes." Philip Joyce, a professor of public policy and administration at George Washington University, figures that in normal times, budget projections a couple of years out tend to be pretty reliable, at five years less so and at 10 years not much at all. "But these aren't normal times," he says. "In recessions, even the short-term numbers aren't very good, because a lot of the factors that go into them are based on assumptions that the economy will behave within some narrow band of reality, and the way it behaves is outside of that band."

The fundamental problem is that economic models — series of equations meant to describe how different parts of the economy fit together — depend on historical data. If you want to know how high unemployment is going to be six months from now, you start with how high it is today. If the economy isn't stable and the old relationships don't hold as well as they used to, then the models break down. In recent years, there have been great advances in economic modeling, says Stock, but forecasts haven't necessarily gotten much better since the economy itself has grown more complex.

Maybe the cruelest thing I've read since the Evil Futurists Guide to World Domination

Rortybomb's (tongue-in-cheek) proposal to exploit cognitively weak customers' challenges with complex financial services. You've got to read the whole thing to really appreciate it.

Of course, unfortunately this isn't far from what some industries already do.

In addition to the moral point, it also flags a new study warning how "Dementia threatens aging boomers’ portfolios"-- the sort of problem that's only going to get worse with time, particularly as the aging in place movement grows.

Learning from economists' failure to see the Great Recession

Carmen Medina (director of the CIA's Center for the Study of Intelligence) and Rebecca Fisher have an interesting article examining why economists had trouble forecasting the housing crisis / financial meltdown / etc. that we're living through, and what it suggests for other kinds of intelligence-gathering and analysis:

“Why did economists not do a better job anticipating the crisis?” was the question everyone seemed to be asking as the global economy began to unravel last fall. The consensus seems to be that most economists not only failed to see the crisis coming but also were downright hostile to the few who argued that The Great Moderation—the era of economic stability brought about by modern banking system controls—wasn’t so great after all....

The fact is that most economists and business experts did not anticipate this economic regression, or its particular timing, with any great degree of specificity, despite the astute analysis of Larry Summers and a few other highly regarded theorists. Economist James Galbraith estimated that, out of thousands of economists, perhaps only eight or 10 individuals really saw the crisis coming....

Leaving behind the issues of bias on the part of economists (which has already been discussed among intelligence officers, along many dimensions) and “group-think” because, again, we are deeply familiar with these pitfalls, six lessons from the economists’ experience seem to have unique applicability to what we, as intelligence professionals, do.

The six lessons are:

• There are no easy, obvious, straightforward policy responses to the economic crisis.
• We are overly sanguine about how close our information and intelligence sources approximate reality.
• Traditional economic analysis has trouble dealing with human irrationality.
• Timing is very different from analysis.
• How we think about causality in the world has great bearing on the priorities we set as an intelligence service and as a nation.
• The complexity of the modern world is overwhelming our existing intellectual and informational models.

Accounting for the future

Some of the most interesting things I've read in the last year are books like Daniel Gilbert's Stumbling on Happiness and Eliezer Yudkowsky's work on cognitive biases and risks [pdf], which take a close look at the psychological limitations of thinking about the future. Of course, the claim that futurists-- or OR people, accountants, etc.-- make is that the tools they use can correct for these biases.

But a Wharton accounting professor's recent studies suggest that even the apparently most rigorous quantitative tools can contribute to our holding faulty views of the future:

Accounting techniques like budgeting, sales projections and financial reporting are supposed to help prevent business failures by giving managers realistic plans to guide their actions and feedback on their progress. In other words, they are supposed to leaven entrepreneurial optimism with green-eye-shaded realism.

At least that's the theory. But when Gavin Cassar, a Wharton accounting professor, tested this idea, he found something troubling: Some accounting tools not only fail to help businesspeople, but may actually lead them astray. In one of his recent studies, forthcoming in Contemporary Accounting Research, Cassar showed that budgeting didn't help a group of Australian firms accurately forecast their revenues. In a second paper,he found that the preparation of financial projections added to aspiring entrepreneurs' optimism, leading them to overestimate their subsequent levels of sales and employment.

"It's been shown in many studies that people are overly optimistic," Cassar says. "What's interesting here is that, when you use the accounting tools, the optimism is even more extreme. This suggests that using the tools, which a lot of academics and government agencies say is good practice, can lead to even bigger mistakes."

The second of the two studies is more interesting here, because it asks whether doing things like writing business plans and creating sales projections make entrepreneurs more realistic or less realistic in their views of the future. He draws on the work of behavioral economists, who

have documented a number of mental shortcuts and biases that can lead people to depart from the logic that traditional economic orthodoxy would suggest. One of the concepts, for example, introduced by Nobel Laureate Daniel Kahneman and co-author Dan Lovallo, is that "an inside view" can distort decision making. A person who adopts an inside view becomes so focused on formulating his particular plan that he neglects to consider critical outside information, like other people's experiences in pursuing the same goal.

"Individuals form an inside view forecast by focusing on the specifics of the case, the details of the plan that exists and obstacles to its completion, and by constructing scenarios of future progress," Cassar summarizes. "In contrast, an outside view is statistical and comparative in nature and does not involve any attempt to divine the future at any level of detail."

Doing financial projections for an entrepreneurial venture, Cassar realized, entails the creation of an inside view. The entrepreneur builds a storyline of success in her head and then plays it out in her spreadsheet, showing rising sales year after year. "Humans are good at storytelling and building causal links," Cassar notes. "They think, 'I'll go to college, I'll write a business plan, I'll raise some capital and then I'll go public or sell out to a big competitor.' There's a probability attached to each of these steps, but they don't think about that. They put all the links together and evaluate the likelihood of success at a much higher probability than is realistic."...

People who did financial projections were the most likely to overestimate the future sales of their ventures. In other words, "the same management activities that entrepreneurs rely on to cope with uncertainty appear to be causing individuals to hold optimistic expectations," he writes. Interestingly, writing a business plan also led to optimism about the likelihood of success, but it didn't lead to overly optimistic expectations because it's also "positively associated with the likelihood that the nascent activity will become an operating venture," he adds. Put another way, people who write plans are more likely to start companies, thereby justifying their optimism.

The details are worth looking at, but the basic moral is clear: what seem like rigorous tools can lead people astray, through a combination of their apparent rigor, and the ways their use generates inside views. The interaction of users and tools co-produces what looks like objective data, and a particular way of looking at that data that's likely to exaggerate the odds of success and downplay the odds of failure.

Now for entrepreneurs you can argue that this is a good thing, and that entrepreneurs aren't successful if they dwell too much on the risks of failure. Starting your own company is an exercise is controlled, disciplined self-delusion. But how much do these kinds of biases-- ones generated by a combination of apparently good information and processes that generate inside views-- affect the work of futurists? I wonder.

Forward thinking cultures

Another interesting piece from the latest Harvard Business Review: Mansour Javidan's short article on forward-thinking cultures. It grows out of a much bigger global study Javidan has been conducting on culture and leadership.

By surveying over 17,000 middle managers in 61 societies, we have been able to discern clear differences in nine key areas. One of these is what we call “future orientation,” or the extent to which a culture encourages and rewards such behavior as delaying gratification, planning, and investing in the future....

We found that societies vary greatly in how oriented they actually are to the long term, but in most cultures people’s personal values and aspirations are similar and quite future oriented. What’s more, most people feel their cultures aren’t as forward thinking as they should be.

In our study, Singapore emerged as the most future oriented of cultures, followed by Switzerland, the Netherlands, and Malaysia. The least future oriented were Russia, Argentina, Poland, and Hungary. Squarely in the middle were Germany, Taiwan, Korea, and Ireland. Even more important, however, is our further finding that the greater a society’s future orientation, the higher its average GDP per capita and its levels of innovativeness, happiness, confidence, and (as the chart shows) competitiveness.


Source: Mansour Javidan, "Forward-Thinking Cultures," Harvard Business Review (July/August 2007)

I can say that Javidan's conclusions track reasonably well with our (or at least my own) more anecdotal experience at the Institute: we have a disproportionate number of clients or inquiries from Singapore (Paul Saffo, invoking Buckminster Fuller, writes about "Spaceship Singapore"), Switzerland, and Scandinavia.

The chart also wouldn't be a bad proxy for investment (or at least spending) on government futures and forecasting.