“Everything is 50/50. Either it will happen or it won’t.” — Unknown

Strategists commonly analyze best-case, worst-case, and most-likely scenarios before making a significant strategy decision. That covers about 0.000007618% of the possibilities. I’m not making up that number.

Here we’ll talk about why there are so many scenarios, why the “most-likely” scenario is hardly likely at all, and why we won’t get better predictions by getting better precision. We’ll end with some ways to make better decisions anyway.

So many scenarios

I ran a business war game with a company in which we took into account the potential actions of several organizations: their business, key competitors, government agencies, and others with vested interests in the industry’s evolution. Teams of executives role-played the organizations in accelerated real time. I designed the war game to… well, we won’t go into that.

Before the war game began I asked each team to spend 15 minutes listing the potential actions they could take. The shortest list had 8. The longest had 17. All told, there were 39,382,200 possible combinations of potential actions. If we were to look at three scenarios — best, worst, most-likely — we would be looking at 0.000007618% of the possibilities. (I told you I didn’t make up that number.) That is, assuming we could actually identify which were best, worst, and most-likely.

To put it another way, hoping that those three scenarios would tell us something meaningful about the competitive landscape would be like clipping a tiny part of a single letter in this essay and using it to infer what the essay was about.

Sidebar. The purpose of that war game wasn’t to evaluate their best, worst, most-likely (BWML) scenarios. I brought up the war game because I have enough information to calculate the number of scenarios and because the company is hardly unique in the number of possible futures it faced, which tells us that a conventional BWML analysis generally looks at a minuscule sample of what could happen. Even if a BWML analysis could assess a scenario with perfect accuracy, the odds that it would perform its perfection on the right three scenarios are vanishingly small. End of sidebar.

Of course the 39,382,200 possible scenarios were not equally likely. If a government agency issues a Thou Shalt regulation, it would presumably be much more likely that businesses would obey the rule than defy it. The combination of actions in some scenarios might be silly or even impossible. But still, however we cut it we’ve got 1) a large number of scenarios and 2) little confidence that the BWML scenarios will give us a representative picture.

Is there any hope? Let’s see how far we get with heroic assumptions. Let’s say we could:

• Efficiently and correctly eliminate 99.9% of those scenarios as being impossible, silly, redundant, or trivial. That’s probably too optimistic but we’re just thought-experimenting here. 
• And then somehow view the remaining 39,382 scenarios. It’d take roughly 600 pages if we choose to print them.
• And then efficiently and correctly spot the most-likely one.
• And then achieve consensus that we’ve spotted the right one.

So, we’ve chosen the most-likely scenario. How likely would it be? Remember, we’ve already eliminated impossibilities, redundancies, and trivialities. Would it really be likely enough for us to call it “most” likely in any meaningful sense?

Precision not to the rescue

Precision doesn’t help much. That’s because scenarios can differ not only in degree but also in kind.

A business might change its price by +2.0%, +2.5%, or +3.0%. Those are differences in degree. A business might change its price, exit the market, launch a new product, merges with a competitor, vertically integrate, and more. Those are differences in kind.

It is reasonable to assume that the outcome of its 2.5% price increase would be somewhere between the outcome of the 2.0% increase and the 3.0% increase. It might not always work out that way but it’s a defensible shortcut.

It is not reasonable to assume that the outcome of merging with a competitor is somewhere between the outcome of launching a new product and vertically integrating.

The differences-in-kind issue means we don’t get to reduce the conceptual or computational load by looking at a few representative scenarios. (Representative of what?) It means too that we won’t know which scenarios are “best” or “worst” without evaluating them all. (Which isn’t impossible. That’s what ACS strategy decision tests do. Evaluating 39,382,200 could take as little as 30 minutes.) And that’s why we won’t solve the strategy-decision problem with a BWML analysis even if we deploy a microscope through which to peer more precisely at those three scenarios.

We don’t need a microscope to dissect a few scenarios. We need a wide-angle lens to explore a lot of scenarios.

Preparing versus predicting

Predicting the future is tremendously difficult partly because of the number-of-scenarios and differences-in-kind problems. It’s difficult also because it is hard to know what other organizations will do. They themselves may not know yet. They may be trying to make decisions at the same time you are. They may even be waiting and seeing what you do.

Still, you must make decisions and those decisions will affect your future. Fortunately, preparing can help you make decisions with much better odds of success, and fortunately too, preparing is easier than predicting.

In a future essay I’ll discuss how cool analysis can help you prepare. For now we’ll end with some shortcuts that don’t require any technology more advanced than thinking caps.

Scope out the terrain. You and I might not be able to assess 39,382 scenarios in our heads — actually, there’s no “might” about it — but merely knowing something about the possibilities is useful. In the war game it was strategically stimulating to know how many options each organization had. That knowledge prevented the executives from falling into overconfidence and tunnel-vision traps.

Apply competitive intelligence. CI can hint at or even reveal which actions the other organizations might adopt. If you can eliminate a few of the potential options, you can focus your attention on those that are left.

Keep asking what if. You are considering actions A, B, and C. How would each be affected if other organizations do X, Y, and Z? Your new product might not be materially affected if another company vertically integrates, but it might be affected a great deal if that company merges with a competitor. Are some of your options less vulnerable than others to what other organizations might do? I’ve seen exactly that, many times: an initially preferred strategy was discarded when a simulation showed it was highly vulnerable to competitive response.

Remember that your actions affect theirs. As you contemplate your moves, ask yourself not only what wonderful results you hope to achieve but also how your moves might influence others’ actions. You might not get those wonderful results if you provoke them to respond. Nobody intends to start a price war but price wars get started. On the happier hand, well-chosen action may lead them to behave as you want.

Talk it through. Say you want to do X. What happens after you do it? (“What happens” is about them as well as about you.) What happens after that? After that? After that? After that? Business war games can do a great job here. Rigorous, contrarian-rich conversation can too.

Notice that those approaches start by broadening the range of what you think about; in other words, they consider multiple scenarios. Contrast that with techniques that quickly and/or invisibly encourage you to narrow your field of vision. Best, worst, most-likely analysis does the latter: it defines the future in terms of three outcomes in which many factors — such as your competitors’ actions — are necessarily assumed to be known or irrelevant.

Look through your wide-angle lens first, the microscope later.

I wrote and you may have read an essay called Predictable Competitors. In that essay we explored the assumption of predictability and the easy-to-fall-into traps of using competitors’ previous behavior to predict their future behavior. We also discussed how to avoid those traps and, in so doing, how to open up promising opportunities to make better strategy decisions. We did it in the context of pricing.

Here we’ll talk less about illusions of predictability and more about delusions of predicting. We’ll do so in the context of a pricing tournament in which over 250 able strategists participated and for which I ran millions of simulations. The pricing tournament was a kind of massive business war game using humans’ strategies and a computer’s calculations.

Unless you already know what I’m going to say — if you think you can predict what I’m going to say, write it down so you can check later — you will have two surprises by the time this essay is over.

The case

Here’s the situation I presented to those able strategists.

You are a pricing strategist for a company with businesses in three industries. You will develop pricing strategies for each of those three businesses, covering 12 quarters (three years). In each industry your business has two competitors, and in each industry your business and your competitors’ businesses start from identical positions. You define success: you decide how much you care about profitability and market share.

I proceeded to describe the three industries in some detail. Those details included these and more:

• The Ailing industry was shrinking, had somewhat price-sensitive customers, and was capital intensive.
• The Mature industry was growing slowly, had relatively price-insensitive customers, and was labor intensive.
• The Fast Growth industry was growing rapidly, had price-sensitive customers, and was a bit on the capital-intensive side.

Because the businesses in each industry began from identical positions, everyone had an equal opportunity to win. The only thing that would determine who won was the quality of their pricing-strategy decisions.

Participants selected a pricing move for Q1 (the first of the 12 quarters), a pricing strategy for Q2-4, another pricing strategy for Q5-8 (year 2), and a third pricing strategy for Q9-12 (year 3). The three multi-quarter pricing strategies could be the same or different, in any combination. The Q1 pricing move would be cut, hold, or raise (see picture); the three subsequent pricing strategies would be selected from a list of strategy options. Just as in real life, participants had to select their strategies without knowing what their competitors would do.

(I know what you’re thinking. Rest assured, there are analytic nuances, technological marvels, and good answers for your good questions, none of which will we go into here. What we’re about to cover doesn’t depend on those nuances, marvels, and answered questions.)

What would you do?

Let’s focus on the first move, the one affecting only Q1, where in each industry (Ailing, Mature, Fast Growth) you could cut, hold, or raise your price.

What would you do in each industry?

Write down your answer, or, if you’re telling yourself you’ll remember your answers, record them legibly in mental ink.

Of course I haven’t given you as much information as I gave the tournament participants. Still, though, you probably have some idea of what you’d do, something along the lines of “in a declining market it is best to _____ prices” or “I’d calculate the effect on profitability of _____ my prices and then decide.”

Second question. I didn’t directly ask this one in the tournament but it’s relevant for our discussion here.

What do you think your competitors (i.e., the other strategists participating in the tournament) will do for the two competing businesses you’ll face in each industry?

Record those answers too.

Notice how that question explicitly focuses your attention on competitors. An approach such as “in a declining market it is best to _____ prices” addresses competitors only obliquely: you’d probably consider the role of competition in a declining market, but perhaps not think about their specific actions.

Now this third question:

Did you think your competitors would do something different from what you chose to do?

In my experience strategists tend to assume competitors will behave as they wish them to or as they have behaved in the past. Who knows, they might even assume competitors are not as clever, quick, or attentive as they are. Hence that third question. But why would you think your competitors will do something different from you in scenarios when we have explicitly said they start from positions identical to your own?

Having now encountered that question, would you change your answer to the first question, the one about what you would do in each industry?

Our first surprise

Unless you correctly predicted me (did you?) and so there was no surprise, we’ve completed our first surprise: the realization that we make assumptions unconsciously that we wouldn’t make deliberately. (Remember, you heard it first here.) Further evidence appears as the subject of our second surprise.

Our second surprise

If the right pricing strategy were obvious, we would expect our able strategists to be pretty close to unanimous in their strategy selections for the tournament. They were not. In other words: surprise, we’re all over the map on how to kick off an effective pricing strategy.

The chart above shows the percentage of strategists who chose to cut, hold, or raise price in their Q1 pricing decision for each industry. Even the most-popular choice — hold price in Q1 in the Mature industry — was preferred by only 57% of the strategists. No move even got a majority in the Ailing and Fast Growth industries. In the Ailing industry, roughly equal percentages of strategists thought it would be best to cut or to raise their prices!

I mentioned earlier that the strategists indicated their performance objectives: market share, profits, or any combination. Perhaps if we control for their objectives we’ll see something closer to consensus. Here’s as close as we get to consensus:

Sixty-seven percent of the strategists who wanted a mix of share and profit in the Mature industry chose to hold their prices in Q1. No other pricing decision, in any of the industries, got anywhere near that level. For instance, here’s the Ailing industry:

The highest we see is 49% in favor of holding prices in Q1 to achieve a mix of share and profit, and 49% who’d cut price to gain share.

We do see an effect we might expect. The strategists who preferred market share as their performance objective were more likely to cut price than to raise it, and those who sought profit were more likely to raise price than to cut. Even so, those effects seem muted: raising or cutting prices didn’t win a clear majority of strategists. (That changed only when we looked at the most-extreme of the share-seekers, and we’d have to get really extreme — the 5 or 10 most share-happy strategists, out of over 250 — to approach a consensus.)

(Sidebar. Since objectives have a demonstrable effect on pricing decisions, we might argue that predicting competitors’ pricing moves translates, at least in part, to understanding their objectives. We would come to the same conclusions and the same surprises, though, because the strategists were far from unanimous about objectives too. There were many in the Ailing industry, for example, who wanted growth, and many in Fast Growth who wanted profits. We’ll talk more about objectives presently. End of sidebar.)

Let’s flip the numbers around a bit. What are the odds that you would be wrong if you predicted your competitors would make the most-popular move? At best, you’d have a 33% chance of being wrong, if you were in the Mature industry and knew your competitors wanted some share and some profit. At worst, you’d have a 63% chance of being wrong for profit-oriented competitors in Fast Growth. (Look at the chart below. The odds of being wrong equal 100% minus the most-popular choice, which is 37% for “hold.”) Not good odds.

So far we’ve focused on one quarter’s pricing decisions. In the tournament our able strategists could choose from a longer list of strategies that ranged from aggressive to reactive, cooperative to confrontational, tend-to-raise to tend-to-cut, and so on, for their moves after Q1. There was nothing approaching consensus or even popularity in those decisions.

Why might real life feel different?

Here we’ve seen that hundreds of strategists are far from agreement when they predict what pricing moves would work and, therefore, why it would be difficult to predict what any of them would do. So why might it seem that competitors are predictable in real life?

Here are a few ideas. Although I don’t have data to prove or disprove them, they are consistent with my experience working with thousands of strategists around the world, and consistent with the way that competitive-strategy tools think. (See Further Reading, below.)

• Clean slate. In the pricing tournament, the strategists started with clean slates: no history, no politics. No one could rely on, or had to defend, previous decisions. In real life, that’s not the case.
• Safety. In the tournament, the worst that could happen was that a person wouldn’t do terribly well in a simulation. Big deal. In real life, a person could lose his or her job for a change that seems to backfire. There’s perceived safety in consistency: don’t blame me, this strategy has worked well for [fill in suitable time span and/or credible other people]. In the tournament, no strategy has an inside track.
• Clarity. In the tournament, all the facts were laid out and the decisions were relatively simple. In real life, there’s more complexity and ambiguity, which might make strategists wary of upsetting a precariously balanced system.
• No tradition. In the tournament, strategists were free to define success according to their own preferred combinations of market share and profit, unburdened by how we do things around here. In real life, different companies may assign similar missions to their businesses (e.g., “fly full” in airlines). We’ve seen that definitions of success — objectives — influence pricing decisions. That effect is doubtless magnified by real-life industries’ oral traditions of what it takes to achieve objectives.

In real life, competitors often emulate others’ moves. That happened in the tournament too, in various ways and for various reasons. However, that affected pricing after Q1, and here we focus mostly on the Q1 decisions. The phenomena after Q1 are fascinating but this essay may already be long enough to try your patience, and certainly mine.

What our surprises mean

Strategists face the problem of predicting competitors. We have just seen why doing so may be harder than we might have thought. Our first surprise suggests that we don’t ask questions that might help us predict competitors (more on that quite soon). Our second surprise suggests that our competitors may not be so easy to predict, unless the slate isn’t clean, jobs aren’t safe, issues aren’t clear, and tradition is binding.

The predicting problem has two elements, carbon and silicon, a.k.a. humans and software.

On the human side, we’ve seen that we are prone to make optimistic, or at least unexamined, assumptions about what competitors will do. We know we assume, and we work in good faith not to do so. Take, for instance, SWOT (strengths, weaknesses, opportunities, and threats) analysis. There we endeavor to give equal time to our competitors so we don’t get too convinced of our infallibility and invulnerability.

But let us look at the SW of SWOT. Its strength is that it is easy, fast, portable, and potentially insightful. (I say “potentially” because whether we get insight depends as much on what we receive as on what it transmits.) Its weakness is… well, let’s illustrate by comparing SWOT to business war games. In a business war game you walk in your competitors’ shoes. In SWOT analysis you merely look at them.

Ways to do better:

• Use competitive intelligence to learn about your competitors’ objectives. A change in objectives may well upset any pricing (or other) “pattern” you may have observed.
• Also use CI to learn about new management. Our able strategists have demonstrated that smart people differ in what they think will work. A change in management may foretell a change in strategy. That’s true especially because “now under new management” rarely means “still under previous thinking.”
• Competitive dynamics resemble chess more than accounting or trend lines. Practice your game before the big real-life match. Business war games let you do that. In my experience, surprises like those we’ve explored here are the rule, not the exception, in business war games. The good news is, it’s a lot cheaper to get surprised during practice.
• During strategy debates, ask if you were our competitors, how would you take advantage of our move. Ask what could go wrong. Ask what are we assuming and do we believe what we are assuming.

Then there’s the software side. Our software thinks like us. (Who else would it think like?) We tell it how to think. We tell it that profit equals revenue minus costs. We tell it demand will change X% for a Y% change in price. We tell it how to combine those thoughts, and others, to figure out the bottom-line effects of price changes.

Since our thinking includes assumptions, so does the thinking of the software that calculates on our behalf. Some of the able strategists, participating in the tournament at a pricing conference I addressed, actually wrote down their spreadsheet-style calculations on their strategy-decision forms: at this price, with these fixed and variable costs, here’s how much I’ll make. If such people were in their offices, it’s likely they would run those calculations in Excel to guide their decisions. Those people chose a paradigm for how to decide before deciding on pricing strategies. That paradigm, like all others, has its paradigm-specific assumptions. In that case, the paradigm assumed competitors were simply irrelevant.

Ways to do better:

• Ask your tools what assumptions they make. They’re not talking? Okay, ask the tool-designers or -wielders what assumptions their tools make. Ask especially how competitors’ moves would be taken into account.
• Ask about competitive dynamics. What gets held constant over time, what doesn’t.
• Ask about what-if. If there’s anything we should take away from the Q1 pricing decisions we explored, let alone the similar variation in the other pricing decisions that we didn’t explore, it’s that we need to test the very real possibility that they will move in a surprising direction. (Subtle point: they may even want to move in a different direction, and may be watching us before they commit.)
• Worry more about the what-ifs than about precision. Who cares if the 63% chance of being wrong for the profit-oriented competitors in the Fast Growth industry should really be 62% or 64%? It’s far more important to explore your chess opponent’s options than to measure precisely where each piece is in its square.

Bonus surprise

Did you predict there’d be only two surprises since that’s what I said at the beginning? Oh my poor student.

There’s one more surprise concerning the able strategists. We noted the variation in their pricing decisions. Every one of them believed that he or she was selecting the strategy that’d win; if she or he believed otherwise, he or she would have selected a different strategy.

Surprise: not all of them won. (I didn’t either. See Further Reading, below.)

That means that we strategists don’t know what will work. Actually, that’s a slight overstatement. Some did know what would work, and their strategies performed well in the tournament, in which I ran over 25,000,000 (no joke) what-if simulations. The problem is, we don’t know in advance whose strategies will work, nor do we know if they will be successful in the future. For what it’s worth, and to add a minor surprise: the person who did best, so far, isn’t a pricing expert. The person is a market-research practitioner.

It’s humbling and perhaps infuriating that we don’t know what will work. We may assume solace in thinking that we do know what’ll work in our industries, and those Ailing, Fast Growth, and Mature industries are pretty weird. Maybe that’s true, but I don’t think so. And I’ll end by asking one more annoying question: if we’re so good at pricing, where do price wars come from?

Update: this essay was published as “Predicting Competitors: Game Theory in Pricing” in the Journal of Professional Pricing, First Quarter 2010 (www.pricingsociety.com).

Further reading

Predictable Competitors, on using history and trends to predict competitors (or not)
Motor Swilling Forbidden, on how people use the same words and mean different things
When I Was Wrong, on the consequences of and opportunities from mistakes
House, MBA, on the envy two CEOs have for the other’s pricing strategy
The Rules, about surprises and assumptions
Decision Tournaments, on the technology behind the pricing tournament

Appendix

Participating in the pricing tournament 

If you would like to run a pricing tournament for your group, let us know. Strategies and scores will be held in confidence. For more information, please write to info@whatifyourstrategy.com.

A representative sample of strategists

In the essay I mentioned that 250+ real-life strategists have participated in the massive pricing simulation. One might ask, especially if one has been exposed to statistical analysis, whether those 250+ people constitute a representative sample. It’s a good question, and surprisingly hard to answer, and fortunately quite inconsequential.

First, what would be a representative sample? Pricing specialists? Pricing consultants? People with responsibility for pricing decisions? With how much experience? In what countries and industries? Big companies or small? Highly competitive markets, long-established markets, markets with many competitors, or not? It’s hard to know what’s representative.

It’s a little easier to know what’s relevant, which is what leads me to say the “representativeness” of our sample is inconsequential. What’s relevant is that you could be up against pricers of any kind.

The strategists in the pricing tournament:

• Came from several countries, mostly from the USA
• Came from many industries
• Included mostly corporate strategists, augmented by some consultants, academics, and MBA students
• Knew their strategies would be held in confidence.

My analysis so far shows little reason to believe that demographic characteristics (location, occupation, etc.) have a material effect on the strategy decisions the strategists made. In other words, the quality of thinking and strategizing doesn’t seem to depend much, if at all, on demographics. More research is needed.

It was a year ago that I wrote A Dark and Stormy Night. In March 2008 the Dow Jones Industrial Average was around 12,000, the NASDAQ was about 2,800, and the S&P500 circled 1,300. At the time those numbers were cause for concern. Now, late in March 2009, with the DJIA around 7,300, the NASDAQ near 1,500, and the S&P 500 at about 770, the March 2008 numbers look deliriously rich. How we would love to return to the bad old days.

A Dark and Stormy Night wasn’t about predicting the Dow, it wasn’t about putative lessons from the past, it wasn’t about technical analysis of fancy logarithmic ratios twice removed. It was about keeping a clear strategic head in a market downturn, especially when your competitors might be losing theirs.

In the year that has passed colossal sums have been lost. Stock markets have halved, reflecting a shift from overly giddy expectations to absurdly pessimistic expectations. (The giddiness and absurdities make more sense when we consider that the stock market has become less about reflecting the real prospects of companies than it is about making money in the stock market.)

No one knew a year ago what would happen in the year to come. There was no announcement that a genuine crisis had begun, no blinking red light that said “Go Ahead and Panic.” You could find predictions and pundits going in every direction known to Descartes and at every velocity known to Einstein.

Which pretty much describes today. No one knows now whether we’ve hit bottom or how long it will take to regain so-called stability, by which we mean our nostalgic, smoothed-out view of good/bad old days. There is no blinking green light that says “You Can Stop Panicking Now.”

That said, the non-apocalyptic among us generally agree that there will be a recovery. Just as we won’t ever be able to pinpoint when things started getting worse, we will never be able to pinpoint the date that things start(ed) getting better. Personally, I’m seeing positive hints not only in the market indices but also in the tenor of the news and investment advice. (NB: I’ve been wrong before.) Perhaps we’re even realizing that we may have vaporized 50% of our perceptions of wealth, as in the stock market, but not 50% of our actual wealth, as in the underlying value of industry.

The question is, as it always is, what’s a smart strategist to do in these turbulent times? (Saying “In these turbulent times” is customary but unnecessary. In over 30 years in the field of competitive strategy, I have never heard anyone ask what’s a smart strategist to do in these nice, calm times.)

What’s a smart strategist to do? First, and as we discussed in A Dark and Stormy Night, exactly this thing: Decide to think clearly and strategically. Today that means decide to contemplate the recovery. Decide to assess what’s changed and what hasn’t. Decide to put fear aside, even if only for a while; don’t worry, it’ll still be there if you want it back later. Decide to look for opportunity. In lieu of a blinking green light, decide what indicators you will use to launch your next moves. Decide to lead.

How’s a smart strategist to do what a smart strategist should do? Here are some ideas that ACS has applied to its own business as well as in our executive-education workshops and business war games.

• Brainstorming, but not just any brainstorming. First, think clearly and strategically about the questions you will ask brains to storm. How will we prosper in the recovery to come? How will we know when to act? How will we move ahead of our competitors? How has the crisis changed the game; that is, the market, our business, and our competitors?
• Brainstorm both sides: what could go wrong, not just what could go right.
• Separate assumptions from facts. Tough times sorely tempt us to jerk our knees. Fear makes us impressionable and susceptible. Just remember that a mighty big crowd fell into the crisis, while the few who said “it’s a bubble, folks,” and who acted accordingly, made money.
• Build your recovery and prosperity plans now even if you’re not ready to implement them. When the time comes — what if it’s sooner than you think? — you can act faster and more confidently than your competitors. Who knows, you might even discover moves you want to make right now.
• Get inside your competitors’ heads and your customers’ heads. Use business war-gaming, scenario planning, or your favorite technique that puts you in their shoes. (It’s different to be in their shoes than to predict them from your shoes. See page 3 of Learning Faster Than The Competition.)

Notice that none of those ideas involves terrifying capital outlays or irrevocable commitments. They merely require thinking and talking. Thinking and talking are bargains made for these turbulent times: they’re cheap and easy, and they buy you time and insight, the most precious of competitive advantages.

A year from now, more or less, when people notice that the green light has been blinking for a while, people will be writing stories about those prescient strategists who acted thoughtfully and decisively, the visionaries who found opportunity and led their companies to glory. Those stories will also mention the companies desperately scrambling to catch up. Those stories will be about decisions and actions begun now.

Update, 5 months later. The DJIA is over 9,500, the NASDAQ is over 2,000, the S&P 500 is over 1,000. And the Wall Street Journal describes some of those prescient, visionary, and scrambling companies in Slump Spurs Grab for Markets.

“Yahoo Posts Loss as New Chief Plots Strategy,” said the Wall Street Journal.

It is, of course, good news for Yahoo employees and shareholders that new CEO Carol Bartz is a capable person working hard to turn the company around. It is worth asking, though, what is reasonable to expect from Yahoo, and even whether a turnaround is possible. We’ll focus on Yahoo Search (the main prize Microsoft sought in its acquisition bid a year ago) in its competition against Google and Microsoft.

We often think of industries on a spectrum from extremely differentiated to commodity: art, cars, airlines, gasoline, raw materials. Most industries, including commodities, have room for multiple competitors. However, in some industries, there is only economic room for one.

There was room only for one high-definition video disc, which is why Blu-ray beat HD-DVD rather than the two co-existing. (We’re talking about competition between the Blu-ray consortium and the HD-DVD group, not about brands of players.) At this point Blu-ray is pretty much invulnerable to attack from a competing disc format. It is, however, vulnerable to radically different technologies, such as streaming video, that offer serious advantages to movie-studio and home-viewer customers.

The defining characteristic of such industries is standardization, not differentiation or commoditization. Industries may become standardized when there is increasing value to the customer as a product or technology becomes more-widely adopted. For instance, a major benefit of Microsoft Office is that everyone has Microsoft Office, which make it easy to share files and advice.

Conversely, industries may not become standardized when there is decreasing value to the customer if the market were to congeal around a single supplier. Airlines want both Boeing and Airbus to stick around because they want to buy better and better aircraft and retain some negotiating power.

Now think from the suppliers’ perspectives. If you’re the one on top in a standardizing industry, your customers become increasingly easy to keep and incremental sales are increasingly easier to make. The lagging competitor is of decreasing value to the customer because it’s the lagging competitor, and so customers get harder to keep and incremental sales get harder to make. Blu-ray and HD-DVD, neck and neck; then Blu-ray pulls ahead by a nose; then whoosh, Blu-ray zooms into the future and HD-DVD sinks into history. (We congratulate the HD-DVD backers for not wasting money dragging out the endgame.) The zoom looks sudden, but it isn’t. It’s a positive feedback loop: the more X happens, the more X happens.

In short, achieving critical mass in a differentiated or commoditized industry means you stay in the game, and achieving critical mass in a standardized industry means you’ve won the game.

Google reaps those increasing-value benefits among the advertisers to whom it sells. Google’s profits (see Gross Galactic Product) give it the wherewithal to improve its technology and offerings. Yahoo suffers from the benefits Google reaps.

Standardization means dominance but not necessarily monopoly. Office alternatives from Apple, Linux, and others aren’t dead yet, or even mortally wounded. Intel is inside but AMD isn’t locked out. Standardization does mean, though, that it is exceptionally difficult to dislodge the incumbent. Witness how hard it’s been for the USA to replace analog television with superior digital technology. Such difficulties are critical for setting performance expectations for any company in Yahoo’s position: standardization means that once the game is lost, a rematch is unusually and asymmetrically expensive for the challenger.

Perhaps Microsoft (suffering from its own search-engine blues) read this handwriting on the wall when it offered to buy Yahoo exactly one year ago for over $44 billion. The combined Yasoft or Microhoo search business presumably would have a better shot at taking on Google than would either company alone. (Which doesn’t mean that they’d win; it just means they’d have better odds.) Apparently former Yahoo CEO Jerry Yang didn’t think so. Apparently Ms. Bartz doesn’t think so either, because she said she didn’t join Yahoo to sell all or parts of the company. Of course, Mr. Yang and Ms. Bartz may be right; they know Yahoo and search better than I do. On the other hand, there is reason to believe that the ordinary tools of business analysis are not telling them what they need to know.

Conventional tools — history, financial analysis, trend lines, and so on — are inadequate to analyze or even recognize standardization. Historical sales trends, for instance, may reveal a modest gap in sales growth for us and our key competitor, and it seems logical that if we work hard we can close that gap. But if standardization is happening the presence and persistence of any gap at all makes it increasingly difficult to catch up.

Thus, conventional analysis with conventional tools can paint an overly rosy picture of Yahoo’s prospects to match or beat Google, which in turn is a recipe for wasting a lot of time and money.

How overly rosy is Yahoo’s picture? How much should Yahoo spend? How far can it go? I can suggest how Ms. Bartz and her colleagues might get valuable insight into those questions before they commit time and money.

What Yahoo strategists need (and perhaps already have) is strategy-simulation and –analysis models that take into account factors related to standardization.  The factors include “soft” customer purchase decision-making (e.g., the Office benefit of easy file sharing), customer loyalty, inertia, and switching costs (e.g., time to learn a new system), the effects of improved product features, and so on. ACS simulation models have taken such factors into account for many years, so we know it’s possible.

As a strategist you can learn even without such models simply by asking where in your industry there are positive feedback loops (things get more extreme) as opposed to negative feedback loops (things dampen out). It’s not about getting the right descriptive statistics from the past. Rather, it’s about thinking through why customers, competitors, and technologies interact as they do.

What’s in store for Yahoo’s search business? I’m not privy to relevant information and I have not performed a strategy analysis of their businesses. Also, the point of this essay isn’t Yahoo per se; rather, the point is that industry standardization is conceptually understandable, strategically important, and invisible to conventional analysis.

That said, if I were a Yahoo investor I would be concerned about danger signs.

• It is possible for a strong #2 to change the game and roar past #1. However, Google is not sitting still and would be tremendously difficult to derail even if they were sitting still. They’ve even become a verb, which further illustrates standardization.
• It’s not enough for Yahoo to be different from Google. It must be clearly, demonstrably superior in ways that matter to users (those of us who use search engines) or to customers (advertisers), just as streaming video poses a credible threat to Blu-ray because it offers instant access to huge libraries.
• I understand the public-relations and investor-management reasons for saying Yahoo won’t be sold or broken up, and I don’t know what people say behind closed Yahoo doors. If, though, those public statements reflect a true unwillingness to consider important strategy options, that’s not good.
• Ironically, pressure from investors for quick results can be self-defeating for the same reasons that pressure to make short-term numbers hurt Detroit.

More than a turnaround is in order when the core problem is standardization around a competitor. Yahoo must come up with a compelling reason why we need two search companies or why advertisers and consumers should switch to Yahoo from Google. And if they don’t eventually team with Yahoo, Microsoft must show why we need three.

Update, May 2009. Chrysler has declared bankruptcy and GM might be clearing its throat to declare the same. Both are rationalizing by shedding brands. We might take that rationalization idea one step further and wonder how much room there is for multiple American car makers. Yale President Rick Levin (also the Frederick William Beinecke Professor of Economics) had this to say in the May/June 2009 issue of Yale Alumni Magazine:

I think the optimum number of U.S. auto cmpanies is one. Maybe two, but certainly not three. The world-wide auto industry is going to shrink, to a Chinese firm, a Japanese firm, an Indian firm, a Korean firm, a European firm — and an American firm, I hope, but there’s a chance there will be zero U.S. auto companies if we don’t do this [the bailout] right.

I don’t believe he’s talking about the standardization effects I mentioned above. What’s germane, though, is the idea of how much room there is for American auto companies. What’s germane also is the need to think through what is rational to expect. The gap between expectations and performance is where fortunes are won and lost.

In Gilbert and Sullivan’s classic “The Mikado,” an emperor (a “true philanthropist”) sings “My object all sublime / I shall achieve in time / To let the punishment fit the crime / The punishment fit the crime.” His prescriptions extend to the “billiard sharp” who is condemned to play “On a cloth untrue / With a twisted cue / And elliptical billiard balls.”

But this essay is not about making the punishment fit the crime, nor is it about rhyme and rhythm, although if I could write like Sir William S. Gilbert it would be. No, this essay is about making the solution fit the problem.

I have mentioned the auto industry often in these e-pages, and I turn again to the proposed auto-industry bailout.

We hear a variety of conditions being proposed to accompany relief: limits on executive pay, change in management, financial oversight, and so on. All sound satisfyingly, righteously stern.

Let us note, though, that each of those conditions is a solution to a perceived problem. Before we start signing checks we should ask if solving those problems will solve the real problem, to wit, how to revive an uncompetitive industry that has resisted turning its steering wheel for, oh, 35 years or so.

Limiting executive pay is a nice symbolic move that might extend the Detroit Three’s life support by a few hours. Nonetheless, it is a nice symbolic move and it gets executives’ attention in case they have been unaware they’ve been running icons of American industry off the road for, oh, 35 years or so. (To be fair, of course management didn’t intend to do so. Why they have done so anyway, and why executives in other industries have done so too, is the subject of much other writing on this website.)

Change in management is a severe form of limiting executive pay. It is potentially a good move, though changing the driver is not enough if the steering wheel is welded in position. Note, by the way, that the D3 has had several changes in management over the last, oh, 35 years or so. There are problems deeper than bad management decisions, most especially the causes of those bad management decisions.

Financial oversight is a fine solution if the problem is corruption or some other sort of villainy. It is not clear that oversight, as in requiring approval for checks over a certain number of digits, solves the problem that the D3 face.

The problem the D3 face is strategic: they build a product that people don’t want to buy in numbers sufficient to cover their costs. Although limiting pay, changing management, and oversighting the checkbook may be helpful, only one, change in management, might be a solution that fits the D3’s strategic problem. The proposed limits and oversight are well-intentioned due diligence for a lender (which is not unreasonable) more than a new, improved competitive strategy for the D3.

I hereby re-recommend the proposal I made in To Bail or to Bail Out. This proposal is designed to see what it will take to turn around the D3, and thus what we, the taxpayers, should be willing to invest. In short:

• Give the D3 enough in loans to cover operating costs for the next couple of months.
• During that time, run business war games that involve the D3, labor, customers, dealers, simulated competitors, and so on.
• As a condition of receiving the loans, the D3 commits to act on a nonpartisan, independent analysis of those business war games.
• In return for the D3’s full and open participation in the business war games, Congress also commits to act on that analysis of the war games. Note that the action may be not to bail out the D3.

I hereby add another approach, not mutually exclusive with the re-recommendation. If we’re considering a change in management, how about awarding D3 management positions through a form of competitive examination? (Which is another Gilbert and Sullivan phrase/solution, from “Iolanthe.”) Let applicants — individual managers or, perhaps preferably, entire management teams — run their own business war games, strategy simulations, scenario plans, whatever. Let them present their plans to Congress and to independent, nonpartisan experts in competitive strategy. It’s not so unlike getting a drug approved by the FDA, a weapons system vetted by the DOD, or winning a portion of the broadcast spectrum by the FCC.

May the best plan win.

What to “do about” the Detroit Three is, deservedly, front-page news. You, I, and Congress hear about incompetent management, structural labor inefficiencies, uncompetitive products, and much, much more.

The past is past but the future is not, and there is not just one possible future for the D3. Now is the time to look forward and foresee what, in a few years, will make us glad for our foresight or wonder once again how we could have missed an opportunity.

Both sides of the bail/bailout debate have points, and both sides paint excessive pictures. For instance, if the D3 all go bankrupt, it is fantastically disruptive to hundreds of thousands, perhaps millions, of people. On the other hand, bankruptcy does not mean that they close their doors, never to return, any more than it meant that Delta and United were permanently grounded. For another instance, if we the taxpayers rescue bad decision-makers, we may be tossing away $25 billion to no avail. On the other hand, we the taxpayers will face a stiff bill either way: if not a loan or loan guarantee, then the loss of many jobs, corporate tax revenue, and so on.

The $25 billion question is too important to answer with an expression of (or an experiment in) ideological purity, a split-the-difference compromise, a roll of the dice, or a verdict assigning blame. Yes, the executives made bad decisions. Yes, union work rules got too restrictive. Yes, our nation’s unwillingness to support universal health care saddled the D3 with costs their competitors didn’t have to bear. Yes, we the consumers fed the D3’s addiction to gas guzzlers and then we deserted them. Yes, there’s more. So what? What do we do now?

We humans are simply not equipped to play chess games of this magnitude in our heads. Let’s get as creative about how we solve our problem — to bail or to bail out — as we want the D3 to be in solving theirs.

I recommend that Congress and the industry commission business war games on behalf of the industry, the workers, the government, and we the people. Multiple games, in parallel, to explore multiple futures. (Contrary to popular belief that “it’s obvious” what should be done, there are always stunningly different possibilities to discover and explore.) Games that include actual industry executives (including some to role-play the competition), actual union executives, actual government officials, actual advertisers, actual consumers, actual taxpayers. Let the games simulate different paths through the thicket: the government bails or bails out; competitors pounce on buyers and/or snap up bargains; unions make this concession or that investment; advertisers make the pitch to real people; real people react.

Give, guarantee, or loan the D3 enough cash to get through the next few months while the games get set up and run. Have Congress observe and analyze the games, full transparency. Although the cost of such games would be almost absurdly immaterial, a spark-plug’s worth of the $25 billion being bandied about, have the industry pay for the games in return for the few months of operating cash. More importantly, have the industry commit to act on an independent, nonpartisan analysis of the games. Have people in traditionally adversarial relationships participate too, in particular labor and dealers. War games’ ability to help people see through others’ eyes leads to consensus and shared commitment to act.

Such a process would demonstrate a commitment to the change in Washington for which we just voted. It’s a commitment to make good decisions, not partisan decisions. It’s a commitment to get real and to take action that might actually succeed.

Democrat, Republican, management, labor, breadwinner, taxpayer. What everyone needs regarding Detroit is better decisions in Detroit. There’s no better way to get those better decisions than to generate and stress-test ideas in a safe, realistic environment before we commit billions of dollars and risk millions of jobs.

Update. With financial support from the US government, GM and Chrysler entered and exited bankruptcy. GM has shed several of its brands as part of its plan to return to profitability. Sergio Marchionne, CEO of both Chrysler and Fiat (Chrysler’s new partner), acknowledged the need to sell vehicles now: “If there is a month where I have to sell 40 percent of my volume as fleet, I will.”  He also said, at the same auto-industry event, that Detroit has “almost a fanatical, maniacal interest in [market] share” and that “unprofitable volume is not volume I want.”

This essay starts with a shocking pricing tournament and proceeds to the challenges faced by President-elect Obama and the titans of industry.

You’d have every reason to expect me to do well in that pricing tournament. With over 30 years in competitive strategy, a global roster of brand-name clients, award-winning and patent-pending (update: patented) simulation designs, a slew of publications, and an MBA from a well-known Eastern business school, I look like a good bet. And that’s even before I reveal my unfair advantage, to wit, that I wrote the simulator for the tournament. (I planned not to include my results in the official tournament tally.)

Imagine my surprise when my performance in my own tournament ranged from sort-of-good to what we will generously call below average.

(Does that performance mean you should expect me to do poorly in the future? We’ll get to that. The short answer is no.)

One hundred and fifty able strategists participated in the tournament. (Update: as of early 2010, nearly 300 able strategists have participated. The lessons in this essay still hold.) My sobering experience was shared by most of them. Pricing specialists, high-end consultants, senior strategists, experienced managers: all, like me, tried to do well; all, being smart and experienced and credentialed, expected to do well; most, like me, didn’t do well. We were wrong, and we were surprised.

When I analyzed the results — in effect, it was a massive business war game with millions of pricing simulations played in a computer, which ACS calls a decision tournament — I figured out what I’d done wrong. It had nothing to do with decimal points, life experience, industry knowledge, or general smartness. It had everything to do assumptions I made. Those assumptions became clear because I got to see what other people actually did, as opposed to what I assumed they’d do. In the best tradition of the scientific method, the pricing tournament let me stress-test my strategy/hypothesis. It was a safe environment where I could learn.

About whether you should expect me to do poorly in the future. Yes, if I were to implement my tournament strategies in real life, I probably would perform badly. Except that I wouldn’t implement those strategies in real life. Why not? Because I had the benefit of the tournament. I learned that my strategies wouldn’t work and I learned what would work better.

Now let’s translate that pricing-tournament experience to challenges we face in government and industry.

We have great debates about the great issues of the day. To some extent those debates are about personal values; for instance, how we value personal responsibility versus safety nets. Perhaps to a greater extent, though, those debates are about what works. The financial crisis is leading many people to shift from trust-the-free-market toward we-need-more-regulation not because of ideological soul-searching but because of the painful evidence that deep deregulation didn’t work.

No one gets up in the morning intending to make bad decisions. Those who proposed deep deregulation did not expect their proposals to fail any more than I expected my pricing-tournament strategy to fail. Yet despite good intentions bad decisions get made, and they can be hard, costly, and time-consuming to reverse. In businesses, in a country, in a world with shrinking room for error, it is imperative that we waste less time and money being wrong.

Some problems are big without being tough. Although fixing Social Security involves massive numbers, it is not a tough problem; it is well understood that the solution contains some combination of reduced benefits, higher taxes, and delayed eligibility. The real issue is about collecting the political will to act. By contrast, challenges such as the financial crisis (relevant for industry and government), climate change (mostly for government), and desperate competition (mostly for industry) are tough because the problems and the proposed solutions involve sequences of actions and reactions among human beings. Statistics, trend lines, and historical analogies are the wrong tools for such problems. They do not solve such problems any more than they would solve a game of chess or the pricing tournament.

Sometimes it looks as though the wrong tools are working, in the sense that they make predictions that come true. When that happens, though, it’s more about lucky data than good predictions. Extrapolating the past into the future works just fine when the future behaves like the past. When the future is different — with, say, a financial crisis in a hyper-interconnected world, or climate change that can literally change the planet — there is no relevant past to extrapolate. That’s where simulation comes in.

This essay is not a product review of the many simulation technologies that can help guide governments and industries through tough challenges. Google finds “about 14,600,000” pages on a search for “simulation technology,” which would make a review more than I can do today. Rather, it is about the general value of simulation as a guide and as a way to get value from being wrong.

When you began to read this essay, you might have focused on the “I was wrong” part of the title. Let’s attend to the “when” part for a moment. The issue is not whether I, or you, or President-elect Obama, or the CEO of Whatever Inc., will be wrong. All of us are human and all of us make mistakes. What’s important is when we make our mistakes.

It is far cheaper, in every sense of the word, to make our mistakes when we’re in safe environments than to make our mistakes when we’re playing for real. For instance, the person who won the pricing tournament (a safe environment) used a strategy that many people would regard as risky. If it failed, we’d press the reset button on the simulation; if it succeeded, we’d gain more confidence in the path previously considered risky. For another instance, simulations revealed design flaws in DC-10 aircraft. (Unfortunately, the simulations were run after the 1979 crash in Chicago, not before.) For yet another, I’ve seen thousands of experts use business war games and crisis simulations to test their ideas and improve their skills in ways they couldn’t and shouldn’t do with real money, careers, and lives at stake. And there are about 14,599,997 other stories to be told.

We face big problems and we need big, creative solutions. I hope our leaders in government and industry will stress-test their ideas with simulation, where it’s safe, so that when it counts we won’t be wrong.

You delicately balance dignity, defiance, and desperation with your storied brand in tatters. You address customer dissatisfaction by appealing to emotion and evoking hallowed names from the past. You offer cheap financing and gifts as you race to reduce your product’s growing cost of ownership. You retrench, relying more and more on your most-loyal customers. You reassure prospective buyers as they question whether you’ll be able to provide service in the future. You watch implacable competitors advancing into areas once considered your safe haven.

Am I talking about GM or the McCain/Palin campaign?

(One thing I am not talking about is product quality. This post is not about the virtues and vices of cars or candidates; it is about strategic thinking.)

Whether we’re talking about the troubles of the Detroit Three or the Republican Two, it’s easy to blame the perfect storm of energy prices, financial crisis, and tough competition. But although the perfect storm may have accelerated their decline, it didn’t cause their vulnerability. Their vulnerability is the culmination of many years of decision-making.

Note that the decision-makers were not stupid and their decisions were not intended to cause vulnerability. On the contrary, the decision-makers were intelligent and they intended to build success. Yet vulnerability was the result, and it was not inevitable.

It’s worth thinking about how intelligent people get into such messes because lessons from their decisions can help you make better decisions. Some relevant issues:

• Forgetting what it means to be an upstart. Summary: incumbents have (or can have) all the advantages; upstarts win by thinking differently.
• Shifting (probably unconsciously) from “investment” to “budget.” Summary: budgets encourage financial thinking, investment encourages strategic thinking.
• Basing a false sense of security on past success. Summary: we conclude that a strategy is working when we like the results, and we assume we’ll like future results if we continue the strategy.
• Believing that a solution (or even the identity of the problem) is obvious. Summary: the solution to global warming is not to turn up the air conditioning.
• Failing to distinguish trimming fat from cutting muscle. Summary: we can only buy success for a short time.

Note that all of those issues concern ways of thinking and none of those issues is limited to a specific factoid, event, or industry.

Most important, note that none of those issues can be solved by a quick fix. For instance, appeals to zero-percent financing and patriotism are ultimately hollow. They’re not hollow because people don’t care about cost or country. They’re hollow because they are designed to solve the seller’s problem, not the buyer’s. Discounts move surplus (the seller’s problem) when not enough people want to buy a company’s product. Waving the flag buffs up public image (the seller’s problem) when not enough people want to buy a candidate’s policies.

It’s also worth thinking about how to get out of the mess. As I’ve seen while conducting numerous business war games in numerous industries on six continents, strategists almost always have more options, and better options, than they think. The trick is to discover the options and to act on them.

• First, acknowledge that we have a problem. That can be harder than it sounds, especially if you believe that evidence from previous strategies predicts all will be well. There is no substitute for healthy skepticism, basic numeracy, and what-if thinking.
• Second, consider whether the core problem is really what you initially or traditionally think. Look at new metrics: for instance, if you rely on financial spreadsheets, look at competitive intelligence or customer interviews. Ask deeper questions: for instance, if you solve the problem as you plan, would that really make things better? Beware of hidden bias: asking your customers whether they’re satisfied with you may tell you little about whether competitors’ customers will switch to you.
• Third, put on someone else’s hat, wear someone else’s shoes, look through someone else’s glasses. (Not literally, unless they agree.) The magic of business war games is that people see what the world look like from different perspectives, and especially from the perspective of competitors who want to win. Assume that the someone-else is intelligent, and ask why they think it smart to do something that looks not-smart when you’re wearing your hat, shoes, and glasses.
• Fourth, stress-test your ideas in a safe environment before you risk your business or campaign in real life. Focus groups. Mock debates. Business war games. Strategy and crisis simulations. Test markets. Ask what could go wrong and answer it rigorously. Think through what has to happen for your strategy to succeed, and whether you really believe those things will happen.

Those recommendations are not rocket science. Nonetheless, the analyses I hear and the articles I read suggest that they’re not accepted science. There’s one action you can take that will 1) demonstrate that it’s not accepted science and 2) help immunize you against the traps: Listen critically.

Let’s remember that people don’t only fail; they succeed, too. Asking how-not-to-fail questions yields one view of your options, one perhaps focused on denial and desperation. Asking how-to-succeed questions yields a different view of your options, one probably focused on opportunity and exploiting competitors’ denial and desperation. It is easier to stand out when others are pulling back.

The three words that begin the next paragraph are three words that I never thought I’d use to begin any paragraph, let alone one offering strategy advice for businesses feeling their way through the financial crisis of our times. Mind you, the three words merely begin the paragraph. They are not advice in themselves, which we’ll get to later.

Governor Sarah Palin was asked in the vice presidential debate who was to blame for the subprime lending crisis. She answered, “Darn right it was the predator [sic] lenders.”

There’s a lesson there for business strategists, and for politicians. A lesson we can use to help us move forward. However, the lesson has nothing to do with predators, lenders, lending, crises, or governors. It has to do with solving problems effectively.

Say we blame the predatory lenders. If you’re a predatory lender, consider yourself blamed. Problem solved? Not exactly, although we did get to enjoy some righteous indignation.

Say we blame the predatory lenders and throw them in jail and/or confiscate their ill-gotten gains. Problem solved? Not yet, although we might add a few pennies (relative to the size of the crisis) to our righteous indignation.

Say we blame the predatory lenders and ban predatory lending. Problem solved? It depends. It depends on whether predatory lenders and lending were the problem, or at least enough of the problem that a ban would solve the problem. It depends also on whether “solved” means undoing pain already suffered or preventing future pain.

As easy and satisfying as it is to explore blame and ban, that isn’t the point. The point is about identifying the problem and its potential solutions. If we see predatory lending as the problem, we will contemplate action, and perhaps take action, about predatory lending.

Tangent: Note that the ease and satisfaction of doing something quick and clear — blame, ban, etc. — is part of the problem. (I haven’t read this book yet but I like the title: Don’t Jump to Solutions, by William B. Rouse.) End of tangent.

But blaming predatory lending, on-target as it may be, begs another question. Where does predatory lending come from? What if it’s due to human nature? What if it’s implicitly encouraged by lax rules? What if it’s the result of greed on the part of lenders, bosses, or investors? What if it comes from more than one of those sources? What’s the problem we want to solve? Different options.

And those explanations beg other questions. Where did lax rules come from? A sincere belief that deregulation works best, a surrender to lobbyists and campaign donors, a decision (perhaps purposeful, perhaps not) to ignore danger signs, an inability of human beings to anticipate every way that deregulation can be exploited? Different options again.

Tangent: in “Name That Economy” Jacob Weisberg identifies no fewer than 14 variations on capitalism and other economic models, each designed (intentionally or not) to solve particular problems. End of tangent.

In business and in government we make decisions about the decisions we’ll make. In other words, we decide first on what the problem is, and then we decide what to do about the problem. We humans like to solve problems, and we like action: we reward action without thought more than we reward thought without action. And so we are prone to speed through the first decision, especially because the problem may seem so darn-right obvious, and jump to the second.

My point is not that there is a single right choice for that first decision and that our job is to find it. After all, options for the what’s-the-problem decision for a bank on the brink of insolvency are probably narrower than those for a technology company, a toy company, or a shipping company wondering about market demand.

My point is that we must make our first decision — the one where we choose the problem we want to solve — well, especially because the issues we face today are so complex and consequential for the world banking system, corporations’ ability to compete, and families’ financial futures. It’s essential that we think rigorously, guarding against deceptively easy and satisfying assumptions. Watch out for “either-or” thinking, unsupported or ideological “it’s obvious” statements, and blaming a single cause for complex problems.

It’s not all bad, far from it. As I said in my essay “A Dark and Stormy Night,” you have an opportunity to make great strategy decisions. Turmoil can give you a chance to distinguish your business (and yourself) from your competitors. The good news about a crisis is that leaders find people receptive to change. What if you think not only about how to “get through” the current crisis? What if you think about how to build customer loyalty, strike up new partnerships, revisit your business model, or redefine your business? Darn right there are major benefits to probing, to creative debate, to looking through different glasses, and to asking what if.

I wish to acknowledge Jay Russo and Paul Schoemaker, whose excellent books Decision Traps and Winning Decisions taught me about key points about “framing” decisions that I made here. If you like what I wrote, please give them credit; if you don’t like what I wrote, please blame me.

Look at how quickly demand for light trucks and SUVs has crashed. It’s so bad, the iconic companies who depended on them as their lifeblood are actually in danger of insolvency.

No one could predict cheap fuel would end this Thursday or two Wednesdays ago or a week from Friday. Almost anyone could predict that the ride would end eventually. We metaphorically remember Humphrey Bogart, in the role of our conscience, warning “You’ll regret it. Maybe not today, maybe not tomorrow, but soon and for the rest of your life.”

The problem wasn’t that we couldn’t predict a day of reckoning would come. For years, for decades, there’s been more than enough public discussion about demand for fuel growing faster than supply. Reasonably informed laypeople knew that, and experts in industries and governments dependent on fuel costs absolutely would know that.

Apparently the problem was that we didn’t believe the predictions. If we did, we would have acted on them. Well, some companies did. They invested in developed high-mileage cars, they hedged their energy costs, maybe they even bought energy futures. Some governments did, and managed to push through mass-transit and green-building programs.

It’s tempting to blame [fill in your favorite pejorative] management for being [fill in your favorite shortcoming]. For the most part, though, smart, responsible, dedicated, ambitious people run companies and governments. People pretty much like you and me. So, why would people like you and me not believe predictions?

We usually point to culprits such as denial, overoptimism, wishful thinking, and short-term thinking. Short-term thinking does have some basis in rationality: I’d be crazy to sacrifice my bonus to make the business better after I’ve gone next year, and besides, you’re squeezing me for every nickel so we can keep the stock up and the boss happy (or the equivalent for elected officials). The others, though, just beg the question: why do smart, responsible, etc. people (like you and me) go into denial, get overoptimistic, and think wishfully?

I’m interested in your answers to that begged question. I’d also like to hear your stories about predictions believed and not believed.