In this essay we will analyze a difficult problem that’s in the news. It’s about how our beliefs and assumptions guide our analysis of a knotty problem. It’s relevant to anyone who works with data, even if — or perhaps especially if — answers seem obvious.

My home town, New Haven, Connecticut, has been in the news. Not because it’s my home town. Rather, because of a reverse discrimination lawsuit (Ricci v. DeStefano) brought by firefighters against the city. The case is in the news also because Judge Sonia Sotomayor, who’s been nominated for the U.S. Supreme Court, joined her colleagues on the Second Circuit Court of Appeals in a unanimous decision backing the city.

Ricci v. DeStefano is now before the Supreme Court. (Update: for their decision, please see the end of this essay.) I thought it might be nice to bring some cold, rigorous thinking to the hot, emotional case.

The case concerns tests used to promote firefighters to lieutenant or captain. Although the legal arguments focus on which parties have which rights and obligations, the core question (which we might hope is relevant) is whether the tests actually were discriminatory.

Here are the numbers:

• 41 people passed the captain’s exam: 25 white, 8 black, and 8 Hispanic. The city would have to promote the 9 people with the top scores. 7 were white, 2 were Hispanic, none were black.
• 77 people passed the lieutenant’s exam: 43 white, 19 black, and 15 Hispanic. The city would have to promote the 10 people with the top scores. All were white.

The city contends that the apparently too-high percentage of whites being promoted by their test scores is evidence of racial discrimination, and so they threw out the test results. The firefighters say that throwing out the test results reverse-discriminates against those who scored well on a fair test.

I decided to calculate whether the high percentage of white promotions was statistically “too” high. If it is statistically unlikely that whites would do so well and non-whites not so well, we’d have evidence that the tests might have been discriminatory (the city’s position). If the odds are high that it the results could happen by chance or merit, we’d have evidence that the tests were not discriminatory (the firefighters’ position).

Note that analysis can say nothing about the intentions behind the tests. Note also that analysis cannot prove that the tests were discriminatory (or not) in some absolute-truth sense. Still, if the odds strongly favor one side or the other, that should count for something. A reasonable person would and should draw different conclusions about X if the odds of X are 1% and if the odds of X are 99%.
 
I wrote a computer program that looked at every possible way to distribute 41 people in 9 slots (the captain’s exam) and every possible way to distribute 77 people in 10 slots (the lieutenant’s exam). Then, it looked at how many of those possible ways matched the actual racial distribution of the results.
 
There are 350,343,565 possible combinations of 9 winners on the captain’s exam. Of them, 13,459,600, or 3.8%, had 7 whites, 0 blacks, and 2 Hispanics. Another way to look at the results is how many had 7 whites and 2 non-whites. Under that test, 57,684,000 combinations match 7 whites and 2 non-whites, or 16.5%.

In statistical analysis, 5% is a common threshold for “significance;” that is, 1-in-20 odds, a fairly reliable result. (More-stringent analysis uses 1%.) Thus, the 3.8% supports the city’s case, and the 16.5% does not.

Then there’s the lieutenant’s test. There are (this is not an exaggeration or a joke) 1,096,993,404,430 possible combinations of 10 winners out of 77 people. Of that trillion-plus, 1,917,334,783 fit the 10 whites, 0 non-whites outcome. The odds of that are far below 1%; to be exact, the odds are 0.17%. That suggests it was not an accident that 10 whites got the 10 top scores. Why it happened — the test itself, the scoring, self-selection among those who took the test, something else — is a different question, about which neither the analysis nor I make any statement. (I didn’t mention merit as a reason why it happened. We’ll come back to that.) All we can say is that there’s only 1 chance in almost 600 that such an outcome would occur by chance. That’s like guessing a coin toss correctly 9 times in a row: it can happen but you wouldn’t bet on it. Those results support the city’s case strongly.

That said, 1-in-600 odds don’t prove there is discrimination. Those test results could happen by chance, especially if multiple cities use the same test or the same city uses the test multiple times. It’s like winning the lottery if you play enough times or dying on an airplane if you fly enough times. And again, there’s the question of merit.

It gets more complicated. We know that New Haven took pains to create a test that wouldn’t racially discriminate. Assuming that they were sincere and at least partially effective in their efforts, that should raise our confidence that the test results happened by chance (the firefighter’s position), not by discrimination (the city’s). How much should we raise our confidence that the test was not discriminatory? I don’t know. A place to start might be to compare the results of the contested exam with the results of previous tests, or to look at other cities’ test results.

Here’s a different complication: how do we define or discern discrimination? Presumably it would show up as an unfair boost, not unlike steroids, rather than as a blatant gift. Let’s try an experiment. What if, for instance, whites were surreptitiously given slightly higher scores on the tests than blacks or Hispanics? I don’t know how that would be done, but let’s assume that there was a clever way. The average score on the lieutenant’s test for whites was 71.8, for blacks 63.8, for Hispanics 63.6. An unfair boost is one of several possible explanations for that difference. The existence of the difference does not prove the difference was unfair or even statistically reliable, though it begs to be studied more. Regardless, what if we split the difference on the averages and subtract 4 points from every white candidate’s score? How many of the top 10 scorers would be white?

Answer: still 10.

Subtracting 4 points from the whites’ scores is arbitrary, and it could be argued that it introduces clear bias in an attempt to eliminate assumed bias. Even so, 4 points doesn’t change the promotion list. That’s an argument in favor of the firefighters who brought the lawsuit, though it doesn’t prove much.

What if we completely erase the average differences among the groups by subtracting 8 points from the whites’ scores? We’d have 6 white winners and 4 black winners, an argument in favor of the city. And we still haven’t proved much.

Neither the 4-point experiment nor the 8-point experiment proves anything about the presence or absence of discrimination. They merely show the sensitivity of the promotions to presumed systematic bias of a certain number of points. The experiment is about the size of the arbitrary subtraction, not about discrimination. The experiment comes down to whether the experimenter believes that 4 points, or 8 points, or 0 points, or 2.736 points, or 12.345 points, is the right adjustment for the differences in average scores. It’s an analysis based on an assumption. (If there are data that support a real adjustment, that’d be another matter entirely. I don’t know if any such data exist.)

Let’s try another approach. Forty-three whites passed the lieutenant’s exam, along with 19 blacks and 15 Hispanics. Given the much larger number of whites, we’d expect that there would be more variation among them than within either of the other two groups. That happened: there was a wider range of scores among whites. Some of those scores were at the top end. That supports the firefighters’ case.

At last, here’s the merit issue I’ve been promising you. Calculating the 1-in-600 odds started with the implicit and more-or-less invisible assumption that all the people who passed the test got the same score. It’s a direct consequence of treating each of the trillion-plus combinations of winners as equally probable. (Did you spot that assumption? I didn’t until I got pretty deep into my analysis.) In effect, my calculations answered the question “how probable is it that equally qualified people of different races would produce 10 white winners (the lieutenant’s test) or 7 white and 2 Hispanic winners (the captain’s test)?” But how do we know people’s qualifications? That’s what the tests are supposed to reveal. And if the tests reveal merit, then the test results would be right, by definition. But I don’t know if they do (perhaps someone else does); some tests work and some tests don’t. For now, we can only make assumptions.

Presumably the New Haven Fire Department believes the tests measure something of value. On the other hand, presumably no one believes the tests are perfect. So what should we conclude?

Alas, our analysis is not conclusive. If we assume that the candidates were equally qualified, more or less, the 1-in-600 calculation is pretty compelling in favor of the city. Ditto if we assume the tests and scoring were slanted, intentionally or not, toward the white candidates. On the other hand, the wider variation in the larger group, the less-than-overwhelming 1-in-26 odds on the captain’s test, and the city’s previous efforts to ensure fair tests argue in favor of the suing firefighters.

Most important, there’s the question of whether the tests measure merit. If they do, the firefighters’ case is strong. If they don’t, the city’s case is strong.

The bottom line: no definitive answer yet.

Let’s put New Haven aside and up-level the discussion.

When I ran my computer program, I was sure of my conclusion: New Haven is right, the firefighters are wrong. As I wrote this essay, though, I thought about questions my dear readers might ask, and those questions made me think. I questioned my methods, assumptions, and conclusions. I went back and forth as I crunched numbers every way I could imagine short of making this essay my career. So much for cold, rigorous thinking; instead, I got a cold, hard dose of unwanted humility as my conclusion morphed into I don’t know. More data might help, especially information about the validity of the tests. I don’t have those data, and even if I did, there’s only so much time for analysis before we must make decisions. That’s true in government and in business.

That I have not come to a clear conclusion about Ricci v. DeStefano doesn’t mean this exercise (and your faithful reading) has been useless. Quite the contrary; we can come to conclusions about how we come to conclusions. This exercise has:

• Made me less likely to jump to a tempting conclusion based on a short stack of factoids.
• Reminded me that numbers don’t speak for themselves (1-in-600 odds), that analysis reflects basic assumptions (equally probable combinations of winners), and that statistical processes are always at work (wider variations in larger groups).
• Taught me to actively, deliberately look for contrary data and ideas and to keep asking how my analysis could be wrong or incomplete.
• Helped me formulate different ways to solve analytic problems. For example, when there is no clear conclusion, ask a different question. “Which mistake would I rather make” is a good one. “What would a smart reader say” is another.
• Shown me that even though “proof” may be an unattainable standard, we can avoid simplistic answers.
• Proven to me that inconclusive data can lead to more-thorough thinking.

Both sides care about the truth, and we have learned that the data we’ve seen so far are not sufficient to tell us the truth. We have learned that we can debate the promotion patterns and test scores as long as our voices hold out but those data alone do not reveal the truth. We have learned that making a decision relying solely on the data we’ve seen so far will be at least partially the triumph of persuasion or ideology. Finally (and this is both important and exciting), we have learned what else we need — data on the validity of the tests themselves – to make a decision using reason and analysis.

Update. On June 29, 2009, the Supreme Court ruled in favor of the firefighters in a 5-4 decision written by Justice Anthony Kennedy. The Wall Street Journal reported that Justice Kennedy said employers “must show a ’strong basis in evidence’ before ignoring results of employment-related tests.” As we saw in this essay, the evidence was mixed, and whether discrimination had been present or absent, it would have been difficult to prove or disprove.

Further reading
Steven D. Levitt and Stephen J. Dubner, Freakonomics.
Leonard Mlodinow, The Drunkard’s Walk.
John Allen Paulos, Innumeracy.
Jay Russo and Paul Schoemaker, Decision Traps.
Nassim Nicholas Taleb, Fooled by Randomness.
See also Marvelous Techniques.

The auto teams were smart and they wanted to win. Yet collectively their decisions subtracted value from the industry: four out of five would have been better off if they’d done nothing at all and repeated last year’s moves for two more years. The fifth team had problems of its own, appearing to be successful but leaving many billions of dollars on the table.

The auto-makers’ decisions were individually sensible but harmful in combination. They worked by the book, but the book didn’t work. It appears that company-centric financial approaches (what are our costs, how much capacity should we mothball) instead of competitive analysis (what will our competitors do) led to those problems.

The good news: anyone who’d gone through the war game would be less likely to make those mistakes in real life.

EXECUTIVE NON-SUMMARY
On June 9, 2009, the front line in the automobile wars could be found at the stately Governor Hotel in Portland. That’s where a group of 30 strategists converged to war-game the industry.

The war game was conducted by Advanced Competitive Strategies and sponsored by the Oregon chapter of the Society of Competitive Intelligence Professionals. We used a simplified version of ACS’ award-winning ValueWar™ strategy simulator, customized for the auto industry. Mark Chussil, ACS’ founder and a veteran of hundreds of war games on six continents, facilitated the war game.

The war game was more to demonstrate war-gaming than to solve the problems of the auto industry. After all, the industry’s problems took decades to build, and it would take our talented strategists more than four hours to fix them all. That said, it was fascinating to see many of the industry’s woes reenacted — and understood — in those four hours. Moreover, the strategists saw for themselves how war-gaming provides a new look at businesses people know well.

Why a Business War Game
Think for a moment about the automobile industry. How has such a studied and storied industry worked itself into such a predicament? What would a business war game add to our understanding of the industry, which is already the subject of endless scrutiny, rumor, and legend? And more generally, since this essay is more about war-gaming than the auto industry, how can business war games aid your firm in assessing and developing winning strategies?
 
Think for a moment about spreadsheets. As commonly used, spreadsheets apply accounting-based methodology to strategy problems. Unfortunately, those spreadsheets do not take competitors or customers into account, which are serious omissions when one needs to anticipate competitors and win customers. (See also With All This Intelligence, Why Don’t We Have Better Strategies?) Because such spreadsheets don’t include real-life industry variables, they effectively (albeit unintentionally) make us overconfident. They assume our strategies will work.
 
Think for a moment about forecasts. Forecasting tools are usually based on data and trends from the past. That’s fine if the future is going to look like the past. If, however, the future is going to look different from the past, forecasting can be materially or wildly off, due to its nature as an extrapolator of a past that’s irrelevant by definition. Would (or did) forecasts in 2007 say that 2009 would be so dismal? Apparently not, based on today’s huge inventories of cars waiting to be driven home.

Think for a moment about other tools used commonly to set performance targets and evaluate strategy options, such as benchmarking, gap analysis, and budgets. Each, like spreadsheets and forecasts, is useful. And each (necessarily) makes assumptions that we the strategists tend to forget when we are caught up in a delirious frenzy of statistics and decimal places.

Business war games provide, even force, a new look at businesses we know well. They do it by having us role-play competitors and customers in addition to ourselves, by having us compete as well as compute, by having us encounter action and reaction rather than assume bigger and better. (For more on this subject, see Learning Faster Than The Competition.) They let us explore and stress-test in a safe environment, where mistakes mean oops instead of ouch. They let us fully use what we know about our businesses, and in so doing, they lead to insights that surprise those of us who use other tools… which is to say all of us.

The Design of the Automobile Industry War Game

Scope: Teams, segments, judges, and challenges

Five auto makers in the war game: Ford, GM, Hyundai, Toyota, and Volkswagen. They competed in the USA in three segments: Big Tough (roughly SUVs), Slick Style (roughly upscale sedans), and Cool Green (roughly eco-friendly vehicles). Each auto maker team had three to six strategists. We had judge teams too, representing consumers and investors.

In addition to excluding some competitors and market segments, we excluded issues such as pensions, healthcare, debt service, dealers, government regulations, and suppliers.  Even though we focused on simple decisions for pricing, marketing, production, and capacity, that was more than enough to generate the dilemmas and debacles that the real auto makers face in real life.

Analysis: ACS’ ValueWar strategy simulator

We used a simplified version of the ValueWar simulator to handle the calculations, the same technology that ACS has used in numerous business war games around the world. We customized the simulator to reflect the automobile industry.

There is much to say about simulation design and other aspects of business war-gaming. Some of the links in this essay will have relevant information. In addition, if you’d like to explore those topics, please contact ACS at info@whatifyourstrategy.com.) What’s most important here is that it is possible to simulate and war-game virtually any industry. How do we know? Because we’ve done it in dozens of industries from airlines to vaccines, and we’ve done it on six continents.

Numbers: Publicly available information and realistic estimates

The war game used publicly available information and realistic estimates for strategizing and simulating. Nothing proprietary or mysterious, nothing contentious. As with pretty much all business war games we’ve seen, the action and the insights come from thinking, behavior, assumptions, and decisions, not from decimal points or obscure factoids. (Related: see The Seven Deadly Sins of Business War Games.) Do not use any information or analysis presented herein to make investment or other weighty decisions about the auto industry. Not even minor decisions.

Process: Two rounds (year 1 and year 2) and four hours

The auto teams made decisions about pricing, marketing, production, and capacity mothballing. They made those decisions for year 1; the decisions went into the simulator; everyone saw the results; they made decisions for year 2. All, including a debriefing, in four hours.

Along with each year’s decisions the auto-maker teams presented marketing pitches to the consumer judges and business pitches to the investor judges. The judges’ assessments joined the teams’ decisions in the eager simulator, which enforced common-sense rules — for instance, you cannot sell more than you produce — and did the arithmetic to estimate demand, sales, profits, and market share.

“Estimate” is an important word. We didn’t pretend that this simulator was “accurate” (no analysis of the future is). However, it was definitely realistic and directionally correct. For instance, all else being equal, if you raised your price you would drive away demand but you would make a higher margin on the cars you sold. All else being equal, if you spent more on marketing, more customers would buy from you. And so on. In sum, the simulator contained a large number of simple, non-controversial relationships, and it was quite capable of revealing consequences that other tools cannot.

Scoring: A level playing field

The five auto makers we chose for the war game didn’t start from equal positions and they didn’t have equal resources. We took that into account in the scoring process to ensure that every auto-maker team had an equal opportunity to win. By the way, it helps for teams to know there will be a winner: it taps the competitive emotions that affect real-life decision-making. We’ve run about a hundred business war games for real business situations, and we’ve seen a company’s own people cheer when they, role-playing the competition, beat their own company! That’s good for the same reason that a boxer wants to practice with a tough sparring partner.

The hard part: Strategic thinking

Competitive strategy is often likened to chess for its complexity and to poker for its competitive interplay. It’s tougher than chess and poker, though, because in competitive strategy the contestants make their moves simultaneously. You make your strategy decisions before you know your competitors’ strategy decisions. Even if you wait and see, you are making your strategy decisions before your competitors make their next strategy decisions.
 
For instance, if you know that your competitors will focus on the Slick Style segment and cut production in the Big Tough segment, it’d be reasonable for you to contemplate abandoning the former for the latter. Problem is, simultaneous moves means you don’t and won’t know that. You have to go all-in with your decisions. (Hence the benefits of competitive intelligence and what-if analysis.) It’s a classic and difficult problem, and it affected the auto makers in the war game. Here’s how.

Lessons From the War Game

Lesson 1. You are not fully in control

Four of the five auto teams decided that Big Tough was on its way out, and they built fewer vehicles for it. The fifth modestly upped its production, but nowhere near as much as the others cut. As a result, war-game customers wanted to buy half a million more Big Tough vehicles than were produced, meaning the teams left roughly $15 billion of revenue on the table. That number (or, rather, the foregone profit on $15 billion in revenue) represents how much it would be worth to forecast the market and the competition better, and to act on that knowledge. Compounding the problem, two offered substantial discounts, and found themselves unhappily selling out at a price lower than they might have gotten. Why would you offer a discount when you don’t have enough supply to meet demand? You wouldn’t. But you might offer a discount if you expected more competition in the segment.

Taken individually their decisions look perfectly rational and reasonable. It was the combination of those rational and reasonable decisions that cost them $15 billion in that one segment. (More money was left behind in other segments.) But if they made rational and reasonable decisions, which of them made a mistake? If anyone did, what was the mistake? We could argue they were too conservative, but when you’re losing money and the market is shifting you naturally want to cut costs and to position yourself in the segments of the future. Those are prudent, hard-to-fault, by-the-book decisions. Perhaps one team could have thought harder about the profit pressures faced by its competitors, inferred that they’d shy away from that segment, and therefore find opportunity in the vacuum left behind. That would take more than a little courage, especially in tough economic times. Moreover, if more than one team came to the same conclusion, they could touch off a bloody war instead of a ghost town.

(For a funny taste of the “if I do this and you do that” conundrum, recall the battle of wits in The Princess Bride when Vizzini figures out which goblet contains poison. If you don’t want to watch the movie again, you can read his dialog with the Man in Black.)

Lesson 2. Align marketing and production

Four of the five auto teams decided that Slick Style was on its way in, and they built more cars for it. However, their marketing pitches weren’t so credible; the consumer judges didn’t want to buy so much from them. They did want to buy more from the one auto team that had a great reputation, but that team didn’t increase its production at all. Result: those who increased production but didn’t market effectively ended up selling less than they’d expected, thereby losing money, and the team that marketed effectively didn’t make enough vehicles, thereby not making money. Again, one could argue that all five made “bad” decisions, but all of the decisions, taken alone, were sensible and defensible.

It’s common in business war games for increasingly desperate teams to try increasingly desperate means. (Not unlike real life.) One of the teams gave in to the temptation — they were warned not to — to pander to the consumer judges. They promised more, better, faster, and cheaper, and they didn’t back it up with marketing. The simulator had several mechanisms to penalize unrealistic behavior, and that team saw their market share slip in year 1 and plummet in year 2. It would have been even worse for them if another team had not produced much less than demand. (See Lesson 5.)

Lesson 3. Don’t tailgate last year’s customer

After seeing results from the first year, teams made another set of decisions for in the second year of the war game. Seeing unsatisfied demand in Big Tough, two teams dove back into that segment. That led to too much production chasing too little demand, which is not good for the bottom line. One diverted capacity away from Slick Style, leading to insufficient supply there. Another big revenue opportunity lost.

By the way, in both years the teams had precise forecasts for the size of each segment. In other words, they had better information than they would in real life. Their production problems had nothing to do with demand forecasts.

Lesson 4. Simulations let you “what if” better options

One team dominated the Cool Green segment: Toyota. Another (GM) abandoned the segment from the start, and the Volkswagen team had only a token presence. The Ford team made gradual changes in the segment, and stayed in the number-two position. The Hyundai team made an aggressive run at Toyota and Ford. From year 1 to 2, Hyundai went from a substantial price premium to a substantial discount, and it greatly increased its marketing spending. Hyundai almost doubled its share of vehicles sold in that segment. But for all its efforts, Hyundai revenue increased by only a few percentage points, and its profits fell. Perhaps not a good move. Then again, that’s a benefit of war-gaming: you try something risky in the safe environment of the game, not in real life.

We tried some what-if simulations. Hyundai would not have done appreciably better even if it had not offered a big discount and spent freely on marketing. A key problem is that their Cool Green entry had much lower margins than their competitors’ because it was a small economy car rather than a high-tech green car. Their issues were deeper and more complex. It’s better to learn that in the safety of a business war game, when you have time to ponder bigger moves, rather than when you’re playing with real money in real life. We’ve seen that happen in war games for real business situations, which showed executives that they needed to find new options rather than debate the current list.

Lesson 5. Define what winning looks like

The auto team that scored best on profits and market share was Toyota. (That’s a statement about the Toyota war-game team as well as the Toyota brand itself. By no means was Toyota predestined to be big and profitable.) It appears they had a winning strategy. Appears. However, they were the team that had by far the biggest capacity shortfall in Slick Style in both years. Over two years that team could have sold over 2,000,000 more vehicles in Slick Style, and nearly 500,000 more in Cool Green. They could have sold roughly $70 billion more. So, did they do well (highest profits and share) or did they do badly (biggest opportunity lost)? Hmmm. Hard to know. But we do know that it would be hard to see the situation with a conventional spreadsheet, and it was easy to see in a war game with a strategy simulator.

Conversely, the simulation had the Toyota team’s competitors pick up much of what the Toyota team left behind. In effect, the Toyota team’s mistake became a gift that inflated the other teams’ results and that made them look better than their decisions warranted. Reinforcing lesson 1: if the Toyota team had not left so much behind, the other four teams would have suffered more.

Lesson 6. You’re not in this industry alone

Taken together, the teams subtracted value from the industry. If they had preserved exactly where they’d begun, they would have made far more money (or, more precisely, they would have lost far less money) than they did with their strategies.

Of course the teams might have come up with better strategies if they’d had more time. Much more importantly, the teams would probably have come up with much better strategies if we had the time to turn back the clock and try a second round of strategizing. In the hundred or so real business war games we’ve conducted, the disappointments from the first round are essential to getting people’s attention and stimulating people’s creativity. The second set of simulations are where the best strategy ideas come up. Just think: if you’d participated in this business war game, and you knew Lessons 1-6 (and more), wouldn’t that help you develop a much better strategy?

Lesson 7. Lessons look obvious, after the war game

As obvious as lessons 1-6 may appear now, they were not obvious before the war game. Corollary: what appears obvious before a war game often turns out to be a really bad idea. That lesson and its corollary are the rule, not the exception, in real business war-gaming. Here are some war stories.

What You Should Do (WYSD)

There’s much more to say about the war-game results. There’s also much more to say about war-gaming: ACS has run purely qualitative games, purely quantitative games, and everything in-between, on dozens of industries. If you’d like to talk, contact us at info@whatifyourstrategy.com. For now, we’ll refocus on war gaming instead of the auto industry. Let’s do some meta-thinking; that is, thinking about thinking.

WYSD #1: Think “systems”

To the extent that the teams made bad decisions, the decisions weren’t bad because they were based on faulty logical thinking or faulty financial thinking. They weren’t. The bad decisions were bad because they were based on faulty systems thinking. They didn’t take into account the interactive, interconnected system of my business, my competitors, my customers (and theirs), and my investors (and theirs). Conventional tools and human frailties get in the way, so it’s hard to get that kind of systems thinking outside simulations and business war games. It’s easy, productive, natural, and even enjoyable to get systems thinking inside simulations and business war games.

(For more on systems thinking and simulations, see The Logic of Failure by Dietrich Dörner.)

WYSD #2: Define “success” carefully

We need to reassess how we determine if a move “worked.” That’s critical, because it affects what behavior and decisions we reinforce and which we dispense with (e.g., by disincentives and dismissals). The four teams who moved partly out of Big Tough made reasonable decisions as individuals; it was in combination that they were hurt.

(For more on success, learn about ACS decision tournaments and see essays on ACS’ blog, especially Marvelous Techniques, When I Was Wrong, and It’s Working!)

WYSD #3: Look for disconfirming evidence

We humans like to be right and to find reasons why we’re right.  Strategy development is often an exercise in advocacy, and as such it can fall into traps such as groupthink. Business war-gaming is an exercise in challenging and stress-testing, which looks for reasons why not as well as reasons why. As such, war-gaming inherently resists groupthink.

WYSD #4: Keep asking why

No, we’re not advising that you indulge your inner little kid who keeps demanding WHY?! in that piercing, petulant voice. Rather, in our calm grownup voices we note that strategy simulation resists groupthink and unwarranted optimism by insisting that there be a clear path from action A to profit B. Simulation models can make those paths explicit, and they don’t accept “because I said so.” That’s insightful and even liberating, because it gets everyone on the same page and it clarifies what has to happen for your strategy to succeed.

WYSD #5: Notice and challenge assumptions

Do it mercilessly, merrily, meekly, or melodiously, it doesn’t matter. One such assumption, which we see often in business war games, is that your strategy must be hidden like a double extra top secret. Notice, though, how much of the distress in the automobile business war game was due to bad assumptions about competitors’ strategies, even disinterest in them. What if you were open and clear about your strategy? We’re not saying you should do that. All we’re saying is that not doing that is usually an unchallenged assumption.

Similarly, people often believe that improving the precision of an analysis will improve the decisions that result. In competitive strategy, that’s not often true. (See Precision In, Garbage Out.) Let us be precise: we are not against precision. But think about how different the auto war game results would have been if teams had added a decimal point, and contrast that with how different the auto war game results would have been if teams had successfully predicted competitors’ actions.

And In Conclusion
Our thanks to SCIP Oregon for inviting us to present the automobile industry business war game. Specifically, our thanks to the extraordinary team of Sean Campbell and Scott Swigart of Cascade Insights.

Our thanks to the intrepid strategists who gave their all to the automobile industry for four hours. It was a huge challenge, and you rose to the occasion with creativity, critical thinking, and good cheer. Well done.

And our congratulations to the Top Strategists on the top-scoring team and to the Top Strategist judges who asked great questions as consumers and investors.

If you would like to experience the automobile business war game or any of ACS’ other programs, please contact us at info@whatifyourstrategy.com.

Patent
Designed by Mark Chussil and implemented by Noam Ben-Ami, Crisis Simulations International’s DXMA™ was recently awarded United States patent number 7,536,287. Mark is a Founder of CSI as well as being Founder and CEO of ACS.

Conference
Mark will speak at the Pricing $trategies at Retail conference, sponsored by the Institute for International Research, on Tuesday, July 14, in Chicago. He will present ACS’ popular Top Pricer Tournament™, in which the conference attendees will compete to see who can develop the most-effective pricing strategy in three different industries. If you would like to join hundreds of executives from around the world by entering the tournament — it’s free, easy, and completely confidential — please write to info@whatifyourstrategy.com. You won’t have to wait for the conference to get your results.

For a 20% discount on the conference, mention code SPKRM2106MC when you contact IIR to sign up.

Here’s what the conference brochure says about the Tournament:

“All bets are off and muted panic rules the land during the current crisis. Hoping to maintain sales volume, companies race to the bottom on prices, not knowing where the bottom is. Hoping to maintain margins, other companies take a deep breath and hold their prices, not knowing what their competitors will do. How can you raise the odds of making good pricing decisions in bad economic times?

“[In the Top Pricer Tournament] You will make pricing decisions that we will simulate against your competitors – the other attendees at the conference – to see how well your strategies perform. You will see a powerful new technology to help pricing strategists develop excellent pricing strategies. You will get a startling new perspective on successful pricing and learn how better to achieve pricing excellence.”

You can read more about the tournament in When I Was Wrong and Millions of Pricing Simulations.

Public Business War Game
ACS will facilitate a public business war game centered on the automobile industry on June 9, 2009, in Portland, Oregon. It’s sponsored by the Oregon chapter of SCIP (the Society of Competitive Intelligence Professionals), and you are invited. Here is program and registration information.

Recommended Book
How do smart strategists choose bad strategies? That’s ACS’ business. How do good people do bad things? That’s Stanford Professor Emeritus Philip Zimbardo’s business. Prof. Zimbardo has written a stunning, powerful, and intense book, The Lucifer Effect, based on his famous Stanford Prison Experiment. You can read about it at http://lucifereffect.org/. We’re citing it here not because it’s linked to ACS’ usual material. We’re citing it here because it’s fascinating and important.

The US government’s stress tests of banks found some wanting. That’s excellent! Early detection and preventive treatment are far better than early death and expensive clean-ups. Of course the stress tests aren’t perfect, just as medical screening isn’t perfect. Still, don’t we all wish those tests had been administered, oh, a year and a half ago?

The financial services industry may have precipitated the financial crisis but they are not the only ones who were unprepared for the shock. It’s not only AIG and Lehman Brothers. Chrysler, Circuit City, Filene’s Basement, and General Growth failed their real-life stress tests too. So have numerous companies even without the financial crisis.

Given the nature of the financial crisis it’s prudent to subject major banks to stress tests. It’s an equally terrific idea for companies in other industries to do the same. Not government-designed or -mandated tests. Rather, rigorous internal strategy tests designed to reveal the opportunities and threats that otherwise become apparent only in retrospect, and sometimes not even then.

I know most companies believe they do such reviews. Strategies pass budget reviews to the penny, market reviews to the last decimal place, and forecast reviews as far as the planning horizon can see. But, for the most part, that process is designed to advocate a strategy. It rarely stress-tests a strategy or contrasts the strategy with materially different alternatives. That’s why the stress tests my colleagues and I have conducted — we call them business war games and strategy simulations —lead to big, profitable surprises.

Stress tests for banks focus on capital requirements under a variety of economic scenarios. That’s necessary for banks, though it’s unclear whether it’s sufficient.

Companies in other industries need to be adequately capitalized too. But their capital needs don’t depend only on defaults, unemployment, and growth in the economy, and maybe not even primarily on those conditions. Perhaps (and perhaps not) unlike banks, companies in other industries are vulnerable to each other; that is, to the competition.

That’s obvious and noncontroversial. No strategist says competitors are irrelevant. What’s less obvious is that the traditional tools for stress-testing a business’ strategy do not adequately take competitors into account. Companies’ spreadsheets don’t have lines for “price cut required to match competitor’s panicky move.” Companies’ audits don’t include the quality of their products and services, relative to competitors’. Companies may assert that they will regain their historical market shares as they introduce new products but they don’t note the implicit assumption that those new products are being gauged against competitors’ old products.  Companies may have to disclose that moves by major competitors are projected to have a material impact but not that tiny upstarts are moving into the market with odd new technology.

Those shortcomings happen even though we strategists know better, want to do better, and try hard to do better. They happen because the shortcomings are hardwired into the usual strategy-development tools. Remember that strategy development went on, in good faith, at those companies that failed.

Effective business war games and strategy simulations — that is, stress tests — ask the relevant questions. They ask how we’re going to respond to a panicky competitor. They judge whether our products and service are at a competitive disadvantage. They explore what new initiatives the competition might launch. They imagine how a tiny upstart might change everything. In a spreadsheet, there’s no entry for “my insight about the market.” There is, in a business war game.

There are three major benefits unique to stress-testing with business war games and strategy simulations.

First, they let strategists rehearse before they go on stage. The rehearsal surfaces assumptions and prevents oops-I-forgot-about-that problems.

Second, they let strategists experiment. Try Plan X35a. A flop? No problem. Press the literal or figurative reset button and try Plan B88q. Ditto with competitive, economic, social, and political scenarios.

Third, they let strategists challenge. Not in the sense of “how could you possibly believe that, you stupid pejorative.” Rather, in the sense of “If I were the competition, this is what I would do to derail our company.” Those challenges are intense, illuminating, and tremendously useful.

The point of stress tests isn’t to anticipate every scenario, let alone to prepare for them. That’s neither possible nor a good use of resources. The point of stress tests also isn’t to place blame for mistakes.

Rather, the point of stress tests is to prevent mistakes, including missing opportunities. Superior preparation is a competitive advantage, and it is possible to anticipate and prepare better than we have.

In my experience conducting business war games, the first outcome is to discover unforeseen problems, and the second — usually just a couple of hours later — is to find unrealized potential. The result is action toward improved performance.

You can conduct your own stress tests. For some advice based on ACS’ experience, see The Seven Deadly Sins of Business War Games and Learning Faster Than The Competition. For case studies, a.k.a. war stories, see You’ve Got The Data. Now What? For examples of testing scenarios, see When I Was Wrong and Millions of Pricing Simulations.

The risk many businesses run is not that they’re performing without a net. We all do that, every day. The risk is that they’re performing without rehearsing.

Stress tests aren’t just for banks. Come to think of it, they’re not even so stressful.

ACS is proud to facilitate a joint meeting of the Oregon and Seattle/Puget Sound chapters of SCIP (the Society of Competitive Intelligence Professionals). Open to anyone (see registration information below), this is an intensive half-day program where you participate in a business war game on the automobile industry.

Of course we don’t expect to solve all the industry’s problems in half a day. We do, however, expect to give you a hands-on taste of business war gaming and show how you can generate competition-beating surprises for your business.

Program Description
What would you do if you were the GM CEO? Or Alan Mulally of Ford, Akio Toyoda of Toyota, Tom Purves of BMW, Robert Cosmei of Hyundai, or another industry leader? And not only what would you do, but how well would your strategies work?  Here’s your chance to find out. What’s more, here’s your chance to learn how to stress-test your strategies and anticipate your competitors with business war gaming. You might even have some fun while you’re at it. Not to mention the cool Top Strategist bragging rights, complete with official certificate and web-wide recognition, you and your team will secure if you win the war.

War games and simulations almost always lead to surprises. Why? Not because they have more decimal points or curvier demand curves. Rather, because they help us think better. They help us take competitors into account realistically, not optimistically. They help us focus on cause and effect. They help us think things through and see the big picture. They help us learn to think strategically.

And while there are no sure things in life, business war games tilt the odds in your favor. That’s what happened for companies like British Airways, GlaxoSmithKline, Shell, Weyerhaeuser, and a couple dozen more around the world, when they worked with Advanced Competitive Strategies. You’ll get a taste of what they discovered in this special, hands-on, half-day session facilitated by Mark Chussil, ACS’ Founder and CEO, a veteran of 100 business war games and a frequent writer and speaker about business war-gaming.

Speaker: Mark Chussil
A highly rated, thought-provoking, and entertaining speaker, Mark lectures and consults around the world about strategic thinking, advanced business war games, and computer simulation. Mark has worked with ACS clients around the world, including Astra Merck, AT&T, British Airways, DuPont, GlaxoSmithKline, Intel, Kodak, Methanex, Nortel, Novartis, Petronas, Sanofi Pasteur, Shell, Sprint, Weyerhaeuser, and others.  Mark earned his MBA from Harvard University and his BA from Yale University.

Location, date, and time. Tuesday, June 9th, 2009 at the Governor Hotel in Portland, Oregon. Noon – 4:00 pm, followed by a no-host networking event 4:00 pm to 6:00 pm at Jake’s Grill. 611 SW 10th Avenue, Portland, OR 97205. Phone: 503.220.1850.

Registration. SCIP Members - $35.00. Non-Members - $45.00. Lunch is included. Groups of 3-7 people can participate in the business war game as a team.

You can register on-site or by clicking this link: http://www.scip.org/Training/EventsDetail.cfm?itemnumber=7156.

Please contact us (see below) if you plan to register on-site so we can make sure we have a large-enough room.

Registration via the web will close Monday, June 8, 2009.  Cancellations not received in writing by Tuesday, June 2, 2009, will not be refunded.

Contact information
Sean Campbell, Oregon Chapter Chair:sean@cascadeinsights.com, 503.631.7552.
Deborah Trainor, Puget Sound, Washington Chapter Chair: Deborah.trainor@gmail.com, 425-830-0565.
Robyn Reals, SCIP Education Manager: rreals@scip.org, 703.739.0696 x107.

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.

It’s a wide-ranging discussion. You’ll find questions and answers about:

• The difference between business war games and strategy simulations.
• How business war games are the strategy equivalent of clinical trials, flight simulators, and sparring partners.
• The role of business war games and strategy simulations in higher-level strategy.
• How decision tournaments™ can address problems too massive to tackle conventionally.
• How the willingness to be wrong leads to deeper learning about strategy (and strategists).
• Why companies need business war games and why their answers aren’t obvious.
• What strategists and educators should look for as they explore business-simulation options.

Denis writes frequently and insightfully on his blog Experiential eLearning.

I commend to you Joel Lovell’s courageous and perspicacious essay “What Do They Know?” Subtitled “True Confessions of a Conflicted Money Guru,” Mr. Lovell muses about what to tell people about finances during the crisis and wonders why some public advisors seem confident despite being wrong. His essay is a fine companion to Sharon Begley’s “Why Pundits Get Things Wrong“.

As my readers know, I’ve wondered for a long time why smart people make bad decisions. Yes, there is the media-mogul preference for sound bites and the public’s clamor for tell-me-what-to-do-oh-oracle advice, but such responses don’t address the question. Public prognosticators don’t give bad recommendations because people want sound bites or oracular advice; the prognosticators would rather be right than wrong. The question remains, why do they get it wrong?

I think there are a few reasons why they (and the rest of us) get it wrong.

• They and we, being human, are prone to all the distortions and biases that we read about in books about social psych. (In addition to books I’ve previously cited, check out Sway: The Irresistible Lure of Irrational Behavior and The Drunkard’s Walk: How Randomness Rules Our Lives.)
• The tools we use to exercise due diligence and thorough analysis can systematically mislead us, giving us positive reinforcement for bad decisions. For example, forecasts that extrapolate the past into the future won’t work if the future is qualitatively different from the past. (See It’s Working! and With All This Intelligence, Why Don’t We Have Better Strategies?)
• The problems we face are more complex than we can handle in our heads. (See When I Was Wrong.)
• Perhaps more subtle: we believe our situations or businesses are different. In other words, we believe in the exceptionalism of our times and/or businesses. (See The Good, the Bad, and the Lucky.)

Note that those reasons suggest in what directions we should seek long-term improvement.

Accountability and oversight, not so hopeful for more than short-term symptomatic relief. The problem is, no matter how sternly we apply oversight and hold people accountable, we’ll continue to make bad decisions if we continue to rely on misleading decision-making techniques.

Regulatory change, more hopeful to the extent that it curbs decision-making that is known or can reasonably be expected to produce negative outcomes. (That’s as opposed to regulatory changes that prescribe specific behavior or technologies, about which I make no comment here.) Profits for today often override prudence for tomorrow (hence mortgages get written that people cannot pay back), so it can be beneficial to ensure that prudence is part of the equation. It’s worth remembering that we already accept such regulations in numerous areas of our lives, including licensing healthcare professionals, FAA safety standards, and insurance on bank deposits.

Just as we’d be better off preventing cancer than treating it, we’d be better off making good decisions than fixing bad ones. We need better-quality decisions, and better-quality decisions come from better-quality decision-making. As we assess proposed solutions to crises big and small, let’s start by asking how each proposal will help us make better decisions.

“The more feted by the media, the worse a pundit’s accuracy.” So reports Sharon Begley in Newsweek. Her article “Why Pundits Get Things Wrong” is a must-read for anyone placing big bets or assessing predictions. (Update: see also Joel Lovell on “What Do They Know?“, from the Washington Post.) She covers confidence, dismissing opposing views, knowing “one big thing,” sound bites and swagger, and the cognitive styles of foxes and hedgehogs.

Why, then, does our world make punditry a viable occupation? We might infer from Ms. Begley’s article that we humans (or at least media moguls) are apparently more attracted to punditry’s sound bites and swagger than we are repulsed by its negative track records. I’m not saying that pundits try to be wrong and I’m not saying that pundits don’t care if they are wrong; on the contrary, I believe that they believe what they say and I believe that they want to be right. Nonetheless, I find the data Ms. Begley cited about fame versus accuracy and hedgehogs versus foxes more compelling than pundits’ confidence.

(Tangential questions: Are pundits causes or effects? Does anyone consider himself or herself a pundit? Am I following a self-defeating strategy by seeking fame through the world’s foxiest blog about business strategy and strategic thinking?)

So if we shouldn’t trust those who have (merely) achieved fame, how should we decide whom to trust? Fame may not be a prerequisite, but it doesn’t follow that obscurity is. Let us note that we don’t even trust positive track records. For example, I might tell you that I’ve perfectly predicted the movements of the Dow, awards from the Academy of Motion Picture Arts and Sciences, and the career of Rafael Nadal. I won’t tell you that, partly because it’s not true and partly because I hadn’t heard of Rafael Nadal before I researched this blog post. (Do not consult me for sports predictions.) But just imagine, for blog post’s sake.

Most of us care about the why behind the track records before most of us will trust future predictions. If I emphasize my sterling credentials and innovative technologies, you might be inclined to believe my astounding track record. If I reveal that I base my predictions on the number of nuggets in each family-sized box of Post Grape-Nuts, you will walk away. Most of you, that is. Some will race out and buy Grape-Nuts and try to break my code, while pundits at Post scramble to explain why they knew all along that sales would rise.

So, deciding where to place our trust is not only a matter of the person making the predictions. It is also a matter of where those predictions come from. And as I’ve mentioned numerous times on these e-pages, track records alone aren’t enough. See, for instance, It’s Working! and The Good, the Bad, and the Lucky.

Speaking of track records, the Wall Street Journal reports that the Obama administration proposes to stress-test banks to ensure they can withstand a “dark economic scenario.” As veterans of business war games and decision tournaments, both of which stress-test competitive strategies, we at ACS have seen the positive effects of such efforts in numerous industries, and I believe the Administration’s move will help the banks help themselves. Moreover, requiring that most large banks can withstand such a stress-test will make it harder for banks to outperform competitors by taking on excessive risk, thereby leveling the playing field and avoiding future replays of the risk-race that contributed to the present crisis.

Executive compensation is top-headline news these days. It commands the attention of the President and Congress. It’s an attractive problem, full of righteous indignation, handy villains (“them”), and clear action plans. And although I’m not against “solving” it, I suggest that it’s not the important problem.

Let’s imagine that the executive-compensation problem has been solved to your satisfaction. Legislation has been enacted, paychecks have shriveled, remorseful CEOs have issued their mea culpas, defiant CEOs have left the building, resilient CEOs have gotten back to work. Or CEOs remain free to negotiate whatever deals they want with their boards of directors. Or something else. Whatever you believe is the perfect solution, so it shall be done.

Now that we’ve handled executive compensation, is the economy back in order? Are we all back to work? Are our retirement funds healthy again? I don’t think so.

Whatever we do with executive compensation, it is the equivalent of sticking a finger into a leaking dike. Giving executive compensation the finger may stop a disturbing (cash) flow, but the dike is still structurally unsound.

(We may want to cut compensation and super-sized bonuses on other grounds, such as the impropriety of using taxpayer money to make bailout beneficiaries rich. That’s a different issue. Let’s not think, though, that cutting compensation makes executives more competent, or less.)

The economy is not in trouble because executives got paid too much. The economy is in trouble because executives (and others in their companies, and people in government, and even we the consumers) made bad decisions. Not just a few executives and not just a few times.

Why did so many people make so many mistakes?

I have written eloquently about problems in corporate decision-making. (See, for example, “With All This Intelligence, Why Don’t We Have Better Strategies?,” “What You Pay For,” “It’s Working!,” and “Suffering was Optional.”) What those essays have in common is that they describe ways in which sober, dedicated business professionals are systematically misled by the tools of the trade. Spreadsheets, for instance, say nothing about competition. Trend lines extrapolated from the past are irrelevant if the future is unlike the past. Doing whatever it takes to make the numbers makes us look good and the performance targets look reasonable even if we hit them by eviscerating the company. (Update to this post: see “When Goal Setting Goes Bad,” an interview with Professor Max Bazerman of the Harvard Business School.)

As I suggested in “To Bail or to Bail Out,” it would be useful — more useful than capping executive compensation, though they are not mutually exclusive — to require the auto industry to use techniques such as business war-gaming as a condition of receiving public funds. War-gaming and other strategy simulations help companies create better strategies, and they would raise the odds that taxpayers will be repaid. I would advocate the same for other industries wanting public assistance. I believe in business war-gaming because I’ve seen it work in the 100 or so that I’ve conducted for Fortune 500 companies.

The point isn’t business war games, though, and if you prefer a different technique that’s fine with me. The point is that we need to get better at decision-making, not dike-plugging. More of the same, only better, won’t do. (See “Precision In, Garbage Out.”) Sticking a finger in the executive-compensation dike merely guarantees we’ll be the first to go underwater when the dike breaks.