Mapping Strategy

Forecasting Their Way to Durable Profits

From the front page of today's Wall Street Journal [emphasis added]:

...a little-known tool of the insurance world: Computerized catastrophe modeling. Crafted by several independent firms and used by most insurers, so-called cat models rely on complex data to estimate probable losses from hurricanes.

But regulators and other critics contend that the latest cat models -- which include assumptions about various climate changes -- are triggering higher insurance rates.

Starting in the early 1990s, cat models began to replace the industry's older tools. Previously, insurers based their rates and underwriting policies largely on historical records of past claims.

Never mind if the forecasts are accurate or not. They work. To increase profits. That's not a pejorative statement as far as the insurance companies are concerned. It's their fiduciary responsibility to maximize return for their shareholders by any legal means available. It does however, set up an interesting, albeit very long-term test of the credulity and patience of their customers. I.e., should the forecasts prove inaccurate. More from the WSJ piece [emphasis added]:

Underlying the newer cat models are scientific theories that rising sea temperatures will result in more intense, and possibly more frequent, hurricanes. The hypotheses suggest that catastrophic hurricanes like 2005's Rita, Wilma and Katrina weren't an aberration, but rather the shape of things to come.

Large reinsurance companies... were early converts to theories of global warming and cite warming of the earth's oceans when predicting massive damages from future storms.

Well of course they were! Think about it for a second.

You're the CEO of a big reinsurance company. Two scientists walk into your office. One says he's got a new computer model that predicts big future costs due to storms.

"Of course I may be wrong," says scientists number one, "After all, I'm a scientist. We deal in hypotheses. You're the executive. You need to make the call on what to do with this."

"So let me get this straight", says the CEO. "If your models are right, we get to raise our rates because our competitors, customers, regulators and the general public are all looking at the same stuff, convinced that your predictions are true? Is that what you're saying?"

Hmm, she thinks to herself (the CEO). This is pretty good. If this guy is wrong, we get to pocket the difference for decades. It would take that long for anyone to prove this guy wrong. Even better, it's not that hard a sell to the general public. We've got cover. If he's right, well... we'll keep him on retainer. Keep his firm in good shape. Drop them some plum projects to keep 'em happy... maybe even kick this over to mar-com to manage. Under that scenario, we've at least got a decent business until I retire -- no better or worse than before. All upside. No downside. Suhweet!

The CEO smiles. She turns to scientist number two. "And what do you have to say for yourself?" she asks.

"Um...", stammers scientist number two (a geologist by training), "...the fossil record over several million years, and agricultural evidence over several millennia would tend to suggest that storm activity actually decreases during warmer climatic periods. And we're just getting this new data in from deep ocean probes suggesting that 80-90% of the ocean volume is staying the same temperature or maybe getting colder. It would be a bit hasty, in my opinion, for you to raise your rates based on a set of theories and prospective models that claim precise predictability when what we're really dealing with here is massive uncertainty. If you just sign the proposal we put on your desk to study this a little further, we can refine the numbers..."

"Thanks for your advice", asks the CEO. "I'll look into it. Have a nice day."

That's a fantasy dialogue, obviously. Read the WSJ article and draw your own conclusions [emphasis added]:

Perhaps the most prominent critic to surface is Karen Clark, an economist who founded one of the first cat-modeling firms two decades ago. Today, she warns about the programs' misapplication...

Companies that rely too heavily on cat-model data "are subjecting their businesses and their customers to the volatility of computer models," says Ms. Clark, who now runs a Boston cat-model consulting business. "The models are being used as if they produce definitive answers rather than uncertain estimates." Ms. Clark says she advises clients to use them in conjunction with other factors, such as broad historical data.

To be sure, insurers themselves are facing higher rates from the reinsurance companies that backstop their claims. The reinsurers, and the financial ratings agencies that assess the health of carriers, are also using the controversial newer models.

I just love this last bit:

...some models now attempt to estimate future losses over a shorter period of time. In doing so, they may also use selective historical data. One model, for example, was reprogrammed to give greater weight to years in which ocean temperatures were particularly warm and hurricane rates were high, such as the period from 1930 to 1945 [prior to broad industrialization and CO2 increases]. That particular model resulted in higher loss estimates for the near-term.

...and therefore higher rates. The logic is circular.

Sometimes The Best Strategy is Patience

From a recent IBM press release [emphasis added]:

A complex physics calculation that will take [the new] Roadrunner [supercomputer] one week to complete, would have taken the 1998 machine 20 years...

Put that in the back of your mind. Now step back and ponder the following hypothetical situation:

You're a business executive (or government official) facing a big, gnarly problem that absolutely, positively must be solved in ten years. If you don't make the deadline, something bad will happen. You'll go out of business, you'll fail to win re-election, your shareholders will revolt, the Huns will invade. Something bad. You've got to solve this problem. Your career (and maybe more) depend on it.

With current technologies, processes and general know-how though, you know you can't make it in time. Sure, you can reach the goal -- in theory, eventually -- but it's going to be really really expensive. And it's certain you're going to be late. Ten years late, in this case.

The problem will get solved, but the Huns will be running the place by that point. You'll be out of a job, maybe worse: lawsuit, jail, early retirement, HBR case studies that make you look like a complete idiot and laughingstock in hindsight... that sort of thing. Your grandkids will still love you... until they get to business school and read the case. But I digress.

So, you have two choices: 

Start throwing wheelbarrows of cash and thousands of bodies at the problem. Or invest judiciously in innovation -- and, at least as importantly, an innovation culture -- and wait patiently for those to bear fruit. (There's a third option: waiting for others to innovate for you, but we'll leave that aside for now because it only applies in limited situations where you can be fairly certain someone is working on the thing you need and that you'll be able to buy whatever it is they come up with.)

In either case, (#2 or #3, that is) you may have to wait years. You won't have much to tell your shareholders in the meantime, other than "we've hired the best, given them the best resources, incented them up the wazoo and tried not to be too meddlesome -- those creative folks don't take well to corporate bureaucrats, y'know." But when something does come along, you'll be able to really impress them: "You remember that problem we thought would take twenty years to solve? We finally got started on it, and we're targeting completion... next Wednesday." Sounds like promotion material, doesn't it? And your grandkids won't be laughing at you when they get to business school.

The tough part, of course, is all the verbal bobbing and weaving for the first ten years when nothing seems to be happening. No highly visible hordes of minions toting 10-ton blocks of stone up pyramids on wooden rollers (and dying by the dozens, getting you in trouble with OSHA). Just a bunch of folks wandering around in lab coats (or, more likely, surf shorts and goatees, playing foos-ball between all-night stints at the white-board).

Sometimes waiting (and investing) is better than charging ahead, using up all of your cash on old, dumb, slow methods that you know will be a day late and a dollar short.

H/T: Irving Wladawsky-Berger

Thinking Exotically

With air travel on my mind after a West Coast trip last weekend, the following strikes me as positive, (and not just because I'm a marathon runner who tends to pack light). Rather, it's the kind of thinking we do routinely when we help clients develop scenarios. E.g., what if industry 'A' adopted the business model of industry 'B'? Emphasis added:

Imagine two scales at the airline ticket counter, one for your bags and one for you. The price of a ticket depends upon the weight of both.

That may not be so far-fetched.

"You listen to the airline CEOs, and nothing is beyond their imagination," said David Castelveter, a spokesman for the Air Transport Association, a Washington, D.C.-based trade group. "They have already begun to think exotically..."

With fuel costs almost tripling since 2000, now accounting for as much as 40 percent of operating expenses at some carriers, according to the ATA, airlines are cutting costs and raising revenue in ways that once were unthinkable...

Singapore Airlines... is "trying to eliminate unnecessary quantities of extra water" to save weight, Chief Executive Officer Chew Choon Seng said in an interview...

Robert Mann, head of R.W. Mann & Co., an aviation consultant based in Port Washington, New York [said,] "If you look at the air-freight business, that's the way they've always done it... We're getting treated like air freight when we travel by airlines, anyway."

This signals that an industry long characterized by minimal innovation starting to think beyond convention (but... we've always done it that way!) The shock is external and pressing in this case (fuel costs). It needn't be so. Better to do this kind of thinking every day.

Best-practice use of scenarios involves doing this systematically, comprehensively and, over time, making it an ingrained habit of strategic thinking among management. (How could we change the rules to our advantage?) Having several contingent strategies already fully 'baked' when a crisis hits can move one ahead of more flat-footed competition who must do all that thinking while under existential, immediate pressure.

Next up: refunds for in-flight diuretics.    ;-)

Obstacles to Prediction Market Adoption

Harvard Business School Associate Professor Andrew McAfee asks, in a recent blog post, why prediction markets haven't been taken up more readily in the corporate world. He writes [links added]:

After writing the [Google prediction markets] case, teaching it a few times, and spending some time understanding the mechanics and utility of prediction markets, I share the puzzlement articulated by James Surowiecki in his book The Wisdom of Crowds:

". . . the most mystifying thing about [prediction] markets is how little interest corporate America has shown in them... companies have remained, for the most part, indifferent to this source of potentially excellent information, and have been surprisingly unwilling to improve their decision making by tapping into the collective wisdom of their employees."

Why is this? It’s not because the technology is hard to acquire... what is the real stumbling block? Is it that companies don’t really want the most accurate information about future events to come out and be widely known?

Sean Silverstone Silverthorne, the editor of HBS Working Knowledge expands on the question in a post last week, outlining what he sees as "three initial roadblocks":

• Unfamiliar with the Concept -- Most managers, especially the veterans, grew up hearing not to trust crowds, to avoid mob rule. Instead, they were encouraged to value expertise and be ready to pay consultants. It’s a major brain-bender to suddenly believe crowds can be smarter than experts.
• Internal Friction -- Corporate prediction markets rankle folks like product managers when company employees start betting on a bad outcome for their product.
  
  
• Implementation Issues -- Until recently, there wasn’t a whole lot of best practices around how to run efficient prediction markets. Results can be seriously skewed by having too few participants, asking the wrong type of questions, or by running a market too long or too short a time.

From my experience working directly with prediction market vendors and corporate implementers, and having watched, thought about and pontificated on this phenomenon for over a dozen* years, Silverstone's points seem reasonable as high-level headings. Several caveats and elaborations may be useful on this rapidly-moving target, however. *(I first heard about them in a pitch by Ken Kittlitz to the Digital Commerce Society of Boston circa 1995/96 -- shortly after I nominally helped co-found that late and loosely constructed organization, and shortly after Kittlitz founded the Foresight Exchange)

First, the number of managers completely unfamiliar with the concept of prediction markets has dwindled markedly over the past five years -- and at an accelerating rate. Silverstone is, I'm sorry to say, somewhat behind in writing that:

A relatively small number of companies including Google, HP, Intel, Yahoo, and Eli Lilly have jumped on this idea by creating prediction markets to aggregate the opinions of employees...

That was true five years ago. It is not the case today.

Doing some research into this recently, I was pleasantly startled to discover that there are at least fifty large corporations running prediction markets 'for real' now, and countless others running small, informal and for the most part un-trackable pilots. (Virtually all of the major vendors offer such trials, though with varying degrees of publicity and enthusiasm as well as varying degrees of ease in setting them up -- though none are terribly difficult). And that's not counting the number of academic institutions and news media organizations that run prediction markets for other reasons (e.g., experimental learning and securing reader attention, respectively).

Add to that the fact that HBS and HBR are covering this topic, conferring a halo of corporate legitimacy, that Surowiecki's book continues to thrust the key concepts into business management if not lay-public consciousness, that barely a week goes by without a major publication giving some ink to the subject (I've stopped even blogging them individually, there are so many) and that the proportion of people in the management class who trade various financial instruments is very high. I.e., they recognize, at some level, that prices represent crowd wisdom -- a body of information to which they may or may not be privy, but which they ignore at the peril of their 401K.

So, given that backdrop, I would turn Silverstone's point around and ask: What excuse can a manager offer, in May, 2008, for being totally 'unfamiliar with the concept' of prediction markets?

Wait. Don't answer that. If your boss isn't reading this, your CEO or your board very well may be, and that's not an idle quip. Some of the most important prediction market initiatives I've seen have come down from the very top, rather than from middle management...

...which leads me to Silverstone's second point: "Internal Friction". That would be putting it mildly.

Some cultures palpably chafe at the idea of unleashing democratic forces within their walls. This is not a scintillatingly deep or novel insight. We know this to be true for countries, and families, as well as for bowling leagues and PTOs for that matter. (I've also observed this when running scenario workshops within certain corporate and, treading lightly here, societal cultures).

Humans almost universally seek some kind of power or at least stability. They work hard for it. And they're loathe to give it up to some impersonal, anonymous and (it can sometimes appear) fickle and BS-penetrating tool (however powerful) that doesn't even have to justify its reasons. (Imagine an election in which each voter had to justify his or her reasons -- to someone -- before casting a vote and you'll understand what I mean.)

Which is all to say that the issue goes well beyond friction to questions of culture, organizational structure, leadership, and the role or perceived role of middle managers. Classically the role of 'middle management' (even the term has taken on a derogatory ring) has been to act as filters and conveyors of information moving between senior management and workers.

Prediction markets usurp some of that role and that can be, quite understandably, unsettling to them. Nobody ever said business was fair. But, bottom line: it is a real reason why prediction markets need to be contextualized carefully in order to succeed. Repeat after me: it's not about the software (mostly).

Which brings me to Silverstone's last point: Implementation Issues. Let's be clear about one thing: 'implementation' here does not refer to the kind of thing one thinks about when installing a big new software system. The software for these things is pretty darned simple in that regard.

The much bigger questions have to do with organizational issues. And those can be sweeping.

For example:

What questions is an organization, a) truly interested in knowing the answer to (strategic), b) willing to tell the employees they might not know the answer to (potentially humiliating) and, c) willing to feed back information on how and why we did or did not act on that input (empowering of morale and loyalty... or cynicism... depending on how it is handled). Just the first part of that is a big, hairy strategy question that goes right to the heart of what a company wants to be when it grows up and where it thinks it is currently 'stuck'.

Bottom line: prediction markets harbor all the paradigm-changing, employee-empowering breakthrough-idea-generating, process-improving, cost-saving potential of the suggestion box... as well as all of its potential as a reason to deride management.

Here are the twists that can and do cause managers to think carefully about the transparency they're prepared to unleash: prediction markets are real-time, they're at least nominally public, and the information they generate isn't really a suggestion (casual) so much as it is a serious investment of time, thought and expectation on the part of those asked to participate. One has to ask then: With all of that potential for discomfort, why have so many big companies begun using them in earnest?

David Einhorn on the Financial Crisis, Government Complicity and Why Rating Agencies Aren't Much Better Than USAToday

Working deep inside Wall Street these past eight months or so, I've had a privileged vantage point from which to observe some of the most tumultuous quakes in the industry in a generation -- and arguably in a century. (Given the near-catastrophic, and still potentially catastrophic nature of those changes, one might legitimately argue with whether 'privileged' or 'punitive' would be the right word to describe my seat in the proverbial bleachers on this one. Since I'm still being paid -- for now -- I'll stick with the former, if only because it has been quite an education.)

Readers without a direct interest in the inner workings and accounting arcana of the financial services industry though, should take a gander at David Einhorn's ten-page pdf/speech: "Private Profits and Socialized Risk". [emphases added in the excerpts below] H/T: Bob Weber.

On the credit-rating agencies:

The market perceives the rating agencies to be doing much more than they actually do. The agencies themselves don't directly misinform the market, but they don't disabuse the market of misperceptions -- often spread by the rated entities -- that the agencies do more than they actually do. This creates a false sense of security and in times of stress this actually makes the problem worse...

It is hard for me to see how the rating agencies survive this debacle with their franchises intact.

On the failure of Bear Stearns and how the SEC has enabled the entire mess:

Rather than looking at its own rules which permitted increased leverage, lower liquidity, greater concentrations of credit risk and holdings of no ready market securities, the SEC is conducting an investigation to see if any short-sellers caused the demise of Bear by spreading rumors.

Of course, Bear didn't fail because of market rumors. It fell because it was too levered and had too many illiquid assets of questionable value and at the same time depended on short-term funding.

On how none of us are really spectators in this

...before Bear Stearns failed... I [had] planned to speculate that regulators believe all of these [major investment banks] are too big to fail and would bail them out, if necessary. The owners, employees and creditors of these institutions are rewarded when they succeed, but it is all of us, the taxpayers, who are left on the hook if they fail. This is called private profits and socialized risk. Heads, I win. Tails, you lose. It is a reverse-Robin Hood system...

As night follows day, it is certain that in the absence of tremendous government regulation, this bailout [of Bear] will lead to a new and potentially bigger round of excessive risk-taking...

On the counter-party credit system

In effect, [with Bear] the government appears to have guaranteed virtually the entire counter-party system. The message is that if you are dealing with a major player -- anyone in the "too big to fail" group -- you don't have to worry about that player's creditworthiness. In effect, your risk is with the U.S. Treasury...

...regulators should consider dismantling the counter-party system... require the posting of all derivative trades, clearing them through a central system and regulating margin requirements...

Sobering stuff, with a few funny bits (check out his water-vs-Coke analogy), along with some interesting long- and short- stock picks near the end. I urge you to read it all. The reason I post it here (a scenario- and big-picture-oriented blog) is that it will eventually touch pretty much everything in the global economy. Ignore it at your peril. Understand it and you'll at least be able to tell the difference between a two-by-four and a rock when it hits you (and all of us) in the back of the head.

Core vs. Context: CBS Talks to CNN

This from today's NY Times [emphasis added]:

CBS... has been in discussions with Time Warner about a deal to outsource some of its news-gathering operations to CNN... reducing CBS’s news-gathering capacity while keeping its frontline personalities, like Katie Couric, the CBS Evening News anchor, and paying a fee to CNN to buy the cable network’s news feeds.

 

For CNN, a deal with a broadcast network would mean a new revenue stream without having to add much in costs. For CBS, an arrangement with a cable channel would allow it to cut costs while maintaining the CBS News brand...

Structural change. In any industry, it can be a long time coming. But then, when it does, it's seemingly so sudden and so obvious in hindsight, leaving many to wonder what took so long and what all the fuss was about. Acknowledging that every industry is different and that this particular deal may have at least as much to do with tactical stumbles on CBS's part as it does any notion of industry grand-strategy, please allow me a quick diversion into another industry to illustrate the point.

Once upon a time, the computer industry was vertically integrated: chips, disk drives, monitors, software, printers and even keyboards were all manufactured--quite literally--by the same company that sold them under its own brand. Digital, Wang, Prime, Data General, IBM, Hewlett Packard, Unisys (and others more easily forgotten) asked customers to settle for second- (or third-) rate components in one area in order to get the pieces they had to have and ensure it all worked together properly. (I had the privilege of consulting to all of these folks between 1988 and the mid '90s, and while I had a ringside seat for only some, I was in the tent with a clear view for all.)

Then suddenly (it seemed, but not really) along came Intel and Microsoft and Dell and Canon (whose printers account for nearly all of what's sold under the HP brand) and people scratched their heads and wondered why they'd ever settled for less, and what on earth had sustained all these second- and third-rate products hidden inside these vertically integrated 'stacks'. (Yes, I know, what came next was not necessarily the perfect competition I'm making it out to be in every case, at every layer, but it was -- at least -- a more horizontally-oriented industry all-of-a-sudden (think pancakes) instead of a bunch of soup-to-nuts, buy-it-all-from-us-or-else brands with their own factories for everything.

Similar tales can be told about the automobile and aircraft industries (among others) however I don't have firsthand knowledge of those and so I leave such analysis to others. All are different. And yet at a very high level, all have been the same:

It becomes clear who's really really good at one particular 'horizontal' aspect of the business (e.g., working 'backwards': customer relationships, brand, distribution, manufacturing (yes, even in services), product development, R&D, etc.) and who's just pretty good. Everyone knows these things, but it's hard to tell because the pieces don't compete with each other directly ('pure plays'). Instead, they're all part of larger packages.

Everyone knows though, who's passionate and committed and has totally nailed a particular discipline and is climbing a learning curve and getting to scale faster than everyone else... and who's just in it (a particular horizontal layer) because they have to be to stay in business and pride or lack of imagination have kept them from having the hard conversation about what is core and what is context.

Internal factors play a huge role here. Pride and corporate politics can be overwhelming. Managers develop fiefdoms and want to stay attached to the mother-ship because they know also that it's cold out there and that their 'stuff' is not really competitive globally.

A quick side story on the side story:

I once consulted to a large computer company that made it's own keyboards (this was ~1991). They'd commissioned me to do a study of a) what customers liked and b) how cost-competitive they were. It was the first time they'd done either thing (telling in itself) and we cast the net very wide indeed to get it right.

Long story short: their manufacturing costs for these keyboards were roughly 8X what others were charging in bulk including shipping from faraway places with factories that had dirt floors and workers who subsisted on meager if not wholly inadequate diets.

But the clincher was this: when we did focus groups, the customers liked the cheap stuff better. It 'felt' better on their fingers. The keyboards were lighter. They could type faster. Case closed. With a vengeance. (I recall the client choking on their diet sodas behind the glass, struggling for something positive amidst the rubble of damning customer consensus... and then explaining to me why their stuff was technically much better and they had the charts to prove it. It didn't matter.)

My client had all but exited the business within a year, redeploying the capital to focus on other things they did much better (and still do).

What does any of this have to do with CBS and CNN? Why the rant?

Just this. As it becomes easier to coordinate assembly of the 'pieces' in a particular industry -- due to better communications, global capital flows and a host of other things. I.e., when the efficiency-driven, convenience-driven reasons for being vertically integrated and settling for best-in-house instead of best-of-breed begin to fade away and Coase's Law and the theory of the firm begin to dissolve (just as he predicted seventy years ago -- he's still with us, btw), we begin to see these kinds of changes in these headlines.

An organization with kick-butt field reporting and the scale to support it far into the future beats out an organization that once used to be known for that... but no longer. And the firm with the pretty faces decides that that's not such a bad thing to be... the face to the 'customer'. The brand. It's a perfectly honorable (and sometimes even very powerful) position to be in (though my personal opinion is that CBS may eventually lose that also). But regardless, clarity is good: You do this. We'll do that. And let's do business together because we're not really competing anymore.

Firms re-invent themselves as pancakes rather than bamboo shoots... horizontally-oriented, best-bar-none specialists rather than vertically-oriented best-in-house behemoths.

Yeah, I've taken some liberties here. Coase talked about other aspects as well. And CBS may merely be in its long-awaited death throes as CNN ascends. But this is a blog post, not a masters thesis.

Here's what I find funny: the obligatory denial-of-reality that's needed to keep the staff of the doomed division from defecting en masse and decimating any negotiating leverage that may remain:

Sandy Genelius, a spokeswoman for CBS News, said, “We are extremely pleased with and proud of our news-gathering operation. No outside arrangements are being negotiated...”

Someone's got to play calming music for the passengers as the deck tilts steeper and steeper.

Scenarios as Vehicles for Fear-Mongering

When we develop scenarios with clients, we emphatically avoid the kind Frank Furedi (rightly) decries here [emphasis added]:

From global warming to obesity, bird flu to terrorism: 2007 was the year when the threat of an apocalypse became an everyday, even banal public issue... The fear market in apocalyptic scenarios continued to flourish in 2007. Almost every week we were told that ‘the situation’ is far worse than we originally thought... Public figures appear to have lost the capacity to reassure or lead people. Instead, they frequently opt for evoking frightening futuristic scenarios where the line between fiction and reality become unclear.

One consequence of Western societies’ obsessive preoccupation with the apocalypse-to-come is that less and less creative energy is devoted to confronting the all too important problems that exist in the here and now. Take the global credit crunch unleashed by the sub-prime home loan crisis this year for instance.

In terms of its material impact, this was arguably the most significant event of the year. After more than a decade of economic stability, the world economy faces the threat of a major recession with important implications for people’s lives. This threat may not make an exciting plot for a sci-fi movie, but it has a direct bearing on the quality of life of millions of people. It also raises important questions about an economic system that is so heavily reliant on using fictitious capital to reproduce itself.

Events over the past 12 months suggest that what we think and how we think influences how we experience our reality.

Some rules and questions we use to avoid these traps and test whether scenarios are useful include:

Are scenarios sufficiently orthogonal to and distinct from one another? Does each embody both 'good' and 'bad' elements? Real world developments are seldom all good or all bad at the same time and from the same perspective. If participants in a scenario workshop find it trivial to line up the scenarios in the same way from "good/easy for us" to "bad/frightening for us", we haven't done our job of representing real-world nuance in hypothetical future stories.

Do scenarios incorporate "here and now" events and choices? (We usually embody these in what we call 'events', a component of modular scenarios). Scenarios entirely about some far-off, visionary 'place' with no explicit ties to current issues are seldom useful beyond the fiction stacks.

Are scenarios directly comparable to current conventional wisdom? (I.e., as Furedi puts it, "how we think... [and] experience our [present] reality"). Without a concrete "you are here dot" scenario that represents what constituents are thinking and assuming, it's impossible to describe how "far away" hypothetical future scenarios really are, or what change they imply. If I'm contemplating a trip to Miami, it helps to know (in terms of budgeting, preparation and mode of transport whether I'm currently in Juneau, Ft. Lauderdale or Tiera del Fuego.

The Future Will Be... Different From What We Expect

Great insights from science writer Ed Regis [emphasis added]:

...after watching all those forecasts not come true, and in fact become falsified in a crashing, breathtaking manner, I began to question the entire business of making predictions...

...the source of this incredible mismatch between confident forecast and actual result [is that] the universe is a complex system in which countless causal chains are acting and interacting independently and simultaneously... all of them running in parallel, and each of them often affecting the course of the others...

It is therefore

...hopeless to try to specify in advance what's going to happen as they jointly work themselves out.  In the face of that complexity, it becomes difficult if not impossible to know with any assurance the future state of [a] system...

(other than tides, moon phases, etc.)

Further, it's an illusion to think that supercomputer modeling is up to the task of truly reliable crystal-ball gazing.  It isn't.  Witness the epidemiologists who predicted that last year's influenza season would be severe (in fact it was mild); the professional hurricane-forecasters whose models told them that the last two hurricane seasons would be monsters (whereas instead they were wimps).  Certain systems in nature, it seems, are computationally irreducible phenomena, meaning that there is no way of knowing the outcome short of waiting for it to happen.

After accepting this reality (something that's as or more true for systems in which human beings play a role, e.g., social, economic, political, business, etc.) the next best things to knowing and predicting are imagining and recognizing:

• imagining multiple, archetypal ways in which the future might evolve, and
  
• collectively being able to recognize (if only a little bit faster than the other guy with whom you are competing) the key events and sequences of events that signal (if only weakly) that one or more paradigms (and not others) are starting to unfold.

Whither the 'Music' Business?

Check out David Byrne, making plenty of sense in the December issue of Wired:

I have seen this business from both sides. I've made money, and I've been ripped off... What is called the music business today, however, is not the business of producing music. At some point it became the business of selling CDs in plastic cases, and that business will soon be over. But that's not bad news for music, and it's certainly not bad news for musicians...

H/T: Kaushik Krishnan and Ajay Shah at the latter's blog

Corporate Prediction Markets Pass Major Milestone

The airtime that Newfutures' North American VP, Norris Clark got on CNBC with Maria Bartiromo last week is a milestone unto itself in the steady rise of prediction markets into mainstream consciousness and respectability. What he had to say put an exclamation point on that milestone [emphasis added]:

"...we've been setting up over 100,000 prediction markets for our corporate clients."

It's unclear how that statement squares with the undated claim, on Newfutures' website, that they have

"...created and operated more than 100,000 markets on a wide variety of topics both public and corporate."

But by any measure, it's a big number, begging the question: Have we reached, or are we already beyond a tipping point in the corporate use of these tools for strategic competitive advantage?