The Predictioneer’s Game: Using the Logic of Brazen Self-Interest to See and Shape the Future

5

NAPKINS FOR PEACE:

DEFINING THE QUESTION

EVERY AFTERNOON AT 3:30 the Hoover Institution’s senior fellows get together in the Commons Room for coffee or tea and some of the best cookies on the West Coast. In the summer of 1987, as on many other occasions, I took a break from my research to enjoy a cup of coffee, a large chocolate chip cookie, and a bit of collegial camaraderie. On this particular July day, a distinguished Israeli sociologist, Shmuel Eisenstadt, was visiting. He asked me what I was working on, the standard conversation opener in the Commons Room, and I explained that I was trying to improve my original forecasting model which was then several years old.

Shmuel asked, “So, you can predict how to make peace in the Middle East?” This being a tall order, I responded cautiously that perhaps I could predict what steps might be taken over the next few years to advance the prospects of peace. I emphasized that this required data, not a crystal ball. He asked what data I needed and then, pen in hand, wrote on my coffee-stained napkin and his napkin as well, listing stakeholders, their potential influence, salience, and positions on a scale measuring possible concessions in the context of a multination peace conference. (The Soviet Union was calling for such a conference in 1987.)

Eisenstadt’s question to me is pretty close in form to the questions I’m asked to examine—whether by companies pursuing mergers, the Defense Department trying to evaluate a terrorist threat, law firms trying to sort out suits, or the CIA trying to understand Iran’s nuclear ambitions—and almost always, the question asked is not actually the question for which an answer really is wanted or needed.

WHAT’S THE PROBLEM?

The big question—how to make peace in the Middle East—is best answered by working out what the many smaller questions are that, taken together, add up to a solution. Framing the problem is usually the hardest part of the prediction and engineering process. I never cease to be surprised that even when billions of dollars or thousands of lives are at stake, decision makers rarely work through what it is they actually need to know. They are usually astonished—fortunately, pleasantly so—to realize that they have not thought systematically enough about their own problem to know what it is they need to know or do.

For me, answering a question like “How can we make peace in the Middle East?” involves breaking this question down to specific issues, to specific choices that must be made. Therein lies the key. Questions need to be about actual choices confronting decision makers rather than about abstract ideas like winning or getting ahead. If you want to win, you need to know what it is that signifies you won, you lost, or you did acceptably well, and by how much. Only after we have drawn out exactly what subjects or questions need precise decisions can we start putting the analyses of all of the issues together so that we might successfully identify the underlying stumbling blocks and find ways around them.

For some problems, framing the problem is relatively easy. Much litigation, for instance, revolves around a settlement price. What a defendant really wants to know is “How much do I have to pay to resolve this lawsuit?” To answer that question, it’s just necessary to know what demands players currently say they support, how salient the price is for each stakeholder, and how much clout each could bring to bear.

Of course, even lawsuits are sometimes more complicated than just working out a settlement price. Discussing a problem with a client, I might learn that they also need to know whether all charges can be resolved with one global settlement price or whether the price paid for one part of a lawsuit will influence the price that has to be paid for another part. Once in a while, it gets still more complicated. There might be multiple defendants, so it’s important to work out not only how much has to be paid to get the plaintiffs to settle, but how the cost will be distributed across the defendants, their insurers, and other involved parties. This sometimes leads to questions about whether it makes sense to drag in other parties as co-defendants or not. Adding co-defendants involves trade-offs. On the one hand, there will be more potential payers with more defendants. That can be a good thing, of course. On the other hand, more defendants means having to coordinate a strategy with many more businesspeople, their lawyers, and their insurers. That can get confusing and expensive. As you can see, a simple question like “What will it take to settle this lawsuit?” can quickly grow into a complicated, interlaced set of issues in which resolving one question changes how others can be resolved. That means we not only have to figure out what the actual questions are that must be decided, but also the sequence of agreements that leads to the optimal outcome.

Business mergers are still more complex most of the time. Unlike litigation, mergers rarely hinge just on the price to be paid for putting companies together. Lots of other issues enter into a successful merger. But mergers can also be simpler than litigation in that the range of likely merger issues is usually well defined and tends to be pretty much the same across all mergers and acquisitions. The price to be paid (or to be obtained) is always a question, of course, but merger discussions rarely get anywhere if there is a substantial disagreement about the worth of a property. That means that the price rarely is the reason an attempted merger or acquisition fails. Remember the example of a merger between a French and a German bank in an earlier chapter. That deal hinged on whether the German executives had to move; they were willing to take a lower price for their bank if they could stay put in Heidelberg.

Several years ago I worked with an international team of analysts on a major effort to create a unified defense industry across Europe, one that could compete with the American defense industry. We looked at virtually every possible combination of defense firms in Britain, France, and Germany, as well as several possible combinations that would have included defense firms in Italy, Spain, or Sweden. The question put to us was “Can we do these mergers?” To answer that question, my team needed to examine approximately seven different issues for each possible combination of firms for a total of more than seventy individual issues, each representing a decision that could make or break a multibillion-dollar deal. Some of the make-or-break questions, the issues on which agreement had to be reached, included (1) the price, (2) allocation of management control between merged units, (3) the scope of businesses to be included or excluded from the merged entity, (4) employment guarantees for workers in various units across national boundaries, (5) government’s role in regulating or sharing in ownership and management of the newly created firm, (6) the timing of the transition to combined working units and teams with shared technology, and (7) where the senior managers would be expected to live.

Merger efforts are more likely to fail because of the “lesser” issues than because of a price disagreement. Yet few executives seem to recognize this when they initiate the process. As a result, they spend millions of dollars on getting good financial advice to sort out the right price to offer, only to have many prospective deals fall through because too little effort has been invested in working out other issues. Sometimes these “other issues” can seem so absurdly small that they are ignored, only to end up being, in hindsight, the deal breakers.

A few years ago I worked on a failed merger in the pharmaceutical industry. The prospective deal was expected to produce great efficiency gains that could have dramatically benefited the pharmaceutical market and prescription drug consumers. Almost all of the executives in the two firms involved were most enthusiastic about the opportunity, with “almost all” being the key phrase. The killer issue that did the deal in involved allocating management control between the CEOs in the two firms. That, of course, is no small question. The absurdity came in how the deal was killed.

The two chief executives hated each other, and, as is the case with many big companies in Europe, there were bitter family issues lurking in the background as well. So deep was the animosity between the two CEOs that everyone agreed a way had to be found for them to work together before going forward. A dinner was arranged for the two of them, and senior aides, who should have smelled a rat, also attended. It took a huge effort just to get this pair to sit at the same table.

The host CEO finally agreed to have the dinner at his home and then, without my or any of his own aides’ realizing it, he planned a menu of course after course of the other chief executive’s most disliked foods. Amazing as it may seem, this prospective multibillion-dollar deal fell apart over a menu, or, more accurately, over a deeply personal conflict that made all other efforts and points moot.

Working out what the right issues are takes patience, good listening skills, and the ability to steer a conversation toward what will really drive results rather than the inchoate musings of the decision makers. Fortunately, the dinner menu rarely plays such a prominent part in negotiations. For me, an “issue” is any specific question for which different individuals, organized groups, or informal interested parties have different preferences regarding the outcome, and for which it is true that an overall agreement cannot be reached unless at least a key set of players come to agreement on the question. It helps to know whether there is a status quo, and if there is, to make sure that the issue is not constructed to be biased against the existing situation.

Recently I taught a seminar in which my students were asked to pick a world crisis that interested them and to model a way to solve the problem. One group of students decided to examine carbon dioxide emissions (a topic I return to in the last chapter). They defined an issue in which one end of the scale reflected current CO₂ emissions and the other end the stiffest reduction advocated by any environmental group. Everything in between represented possible agreements among the players. Do you see the problem here? I asked if they thought there were no energy companies or others who felt that CO₂ emissions should be less regulated than was the case at the time. Of course there were interested parties who wanted to have more freedom to produce CO₂! So the way the students had designed their issue was biased. The only answer that could come out of their exercise was to increase regulatory controls or, with low odds, keep them the same. Their scale allowed no possibility of anyone wanting to produce more carbon dioxide. They had unwittingly created a biased issue, one that made them feel good but would almost certainly lead to a wrong answer. Such fundamental flaws in the design of questions ensure that the answers to them will be useless.

YOU CAN’T ALWAYS GET WHAT YOU WANT, BUT IF YOU TRY …

Once an issue is properly framed, we have to think about how to capture the thought process that people go through in working out decisions. Without doing that, without climbing into the heads of your rivals, you’re not likely to get what you want. You’re not even likely to know how to try to get what you need.

The game structure I use looks at choices as sometimes involving cooperation, sometimes competition, and sometimes coercion. The most complicated part is to try to emulate how people think about changes in their situation as well as what others say and do. Players are always interested in altering the lay of the land in their favor. They want to surround their desired outcome with tall mountains of power that are hard to overcome. They want to tear down mountains of opposition, leveling the power terrain around positions they want to defeat. At every step along the way, everybody has to work out who will help them and who will get in the way. They have to calculate the risks and rewards, costs and benefits of actively trying to change other people’s choices or lying low, trying to stay out of the line of fire. The math can get complicated,¹ but let’s look at some examples of the process at work that we should be able to follow pretty easily.

The table below shows the small data set that resulted from the cookie-and-coffee conversation I had with Shmuel Eisenstadt in 1987, augmented in 1989 by a discussion with Harold Saunders, who by then was the former deputy assistant secretary of state for the Near East and North Africa. The continuum of possible outcomes on the settlement issue ranged from the establishment of a fully independent, secular Palestinian state at one extreme to the annexation by Israel of the West Bank and Gaza at the other extreme. Position 30 on the scale was defined to represent territorial concessions granted by Israel to the Palestinians without establishing an autonomous state but establishing instead a Palestinian political entity federated with Jordan. The 1987 status quo was located at the position equivalent to 85 on this scale (under the “Negotiated Settlement Options” column). There was no semi-autonomous Palestinian territory or government at that time.

It is not hard to see how territorial concessions can be organized on a continuum. Although the scale above is not based on percentages of land or land value, still there is a natural progression in choices ranging from Israel’s annexing contested territory on to no concessions by the Israelis and finishing at the other end of the scale with a fully independent Palestinian state. From these beginnings in 1987, I prepared a forecast that would closely predict the actual territorial concessions agreed to between the Israelis and the Palestinians in 1993 at Oslo. (We will look more closely at this forecast a little later in this chapter.) Let me reemphasize a central assumption behind the scale, an assumption I introduced casually in the discussion of North Korea. The scale tells us that someone advocating a weakly autonomous Palestinian territory (70 on the scale) strongly prefers the status quo (at 85 on the scale) to a territory closely in federation with Jordan (25 on the scale). We know the preference for 85 is stronger than for 25, even though 85 and 25 flank the advocated position at 70, because 70 is much closer numerically to 85 than to 25. That is how each scale works. Numerical values that are closer to the numerical value of the player’s advocated position are liked better by the player than positions reflected by numerical values farther from the advocated position. Knowing that, let’s have a look at a business issue that can help us understand more about how to turn problems into a well-defined numerical scale.

Here the focus is on what an issue’s scale might look like when the question involves something less obviously numerical in character than land for peace. To see an answer to this, consider the following range of choices from a litigation I worked on some time ago. Naturally I have masked the details to protect my client, but the ideas should be clear enough.

WHAT CHARGES WILL THE U.S. ATTORNEY BRING AGAINST THE DEFENDANT?

Scale Position

Meaning of the Numerical Value on the Scale

100

Multiple felony charges including several specific, severe felonies

90

Several specific, severe felony counts but no lesser felonies

80

One count of the severe felony plus several lesser felonies

75

One count of the severe felony but no other felonies

60

Multiple felony counts but none of the severe felonies

40

Multiple misdemeanor counts plus one lesser felony

25

Multiple misdemeanor counts and no felonies

0

One misdemeanor count

This scale makes clear how the client prioritized possible outcomes. The definition of 0 on the scale tells us that the client did not believe that any stakeholder would argue for the dismissal of all charges. That was not a choice included in the range of feasible outcomes. The upper bound on the scale indicates that the client believed at least some in the U.S. Attorney’s Office or others involved in the process would argue for severe criminal penalties. The real action was to unfold in between, and we’ll take a deeper look at this “case” in a later chapter.

Let’s take a quick look at this issue scale. It can shed a lot of light on how to think about issues. We can see that the two key “distance” differences here, laid out by the client, are the plea from 25 to 40 (no felony vs. one lesser) and from 40 to 75 (no severe vs. one severe). After that, life gets worse, but not nearly as dramatically worse as happens with the move from 40 to 75. This tells us, among other things, that avoiding a severe felony charge altogether was of greater value to the client than adjustments in the number of severe felonies that might have to be pleaded to.

In framing issues this carefully, the decision makers begin to learn things they had not recognized about their own problem. Instead of an amorphous question, they now have focused issues to address. They have confronted the problem and defined what the meaning is behind the choices they face. Once they go through the interview process to identify the remainder of the data—who the players are, where they currently stand on the issue, how salient it is to them, and how much clout they can each exert—they have, for the first time, a genuinely comprehensive view of how high a mountain they must climb. As I mentioned, we will revisit this case in a later chapter to see how they thought it would turn out and how it actually did turn out. For now, let’s return to the napkins covered with data on how to promote peace in the Middle East.

PEACE IN THE MIDDLE EAST?

The information scribbled on a couple of napkins turned out to provide a terrific grounding for figuring out what was going to happen in the Middle East over the several years following 1987. Eisenstadt knew his stuff, and so did Harold Saunders. What did we learn when the data were subjected to the model’s dynamics, allowing all of the players the opportunity to bargain with each other, trading territorial concessions for political credit? The predicted outcome was reported in a 1990 journal article I wrote based on the cookie-hour napkins.² The article was published three years before the Oslo Accords created the Palestinian Authority. It predicted resolution at position 60 on the issue scale out of a feasible range of 0 to 100. Sixty on the scale was defined at the time as being equivalent to the Palestinians’ gaining some weak localized territorial control over a semi-autonomous entity. Remember, the status quo was 85, and lower values reflected greater and greater concessions to the Palestinians. We now know that the territorial concessions that were actually worked out between the Israeli government and Yasser Arafat were equivalent to about 60 on the scale, as I predicted in print three years beforehand.

Of course, this prediction was made a long time ago and was intended to be good only once there was a change in Israel’s government. As I wrote at the time, “Given [then Israel’s prime minister Yitzhak] Shamir’s apparent perception of the situation, we must conclude that as long as he is Prime Minister of Israel, there is no reason to expect significant progress.” Shamir, who became Israel’s prime minister in October 1986, was replaced by Shimon Peres in July 1992, paving the way for real progress between the Israelis and the Palestinians.

The analysis, however, was more detailed and nuanced than just a prediction of territorial concessions. My 1990 study went on to suggest that the then large and influential Popular Front for the Liberation of Palestine (PFLP) would “become politically isolated and irrelevant in negotiations. We can anticipate that they would respond to such a situation by increasing terrorist acts, aimed not only at the Israelis but perhaps also at the PLO leadership. If the analysis is correct and if the PLO adopted a strategy of incremental moderation, the future of the PFLP would be to flicker out of the picture.” Remember, this was out there for anyone to see and read in 1990. That is what I meant earlier when I said we must be willing to risk embarrassment if we want people to have confidence in our predictions. Certainly few in 1990 would have argued that the PFLP was likely to “flicker out of the picture.”

Today, two decades later, we can look back to see how accurate that prediction was. For instance, what do independent sources now say about what happened to the PFLP following the creation of the Palestinian Authority in 1993? BBC News Online, reporting on January 16, 2002, quotes Abdel Bari Atwan, editor in chief of the London-based Arab newspaper Al-Quds, as saying “The movement [i.e., the Popular Front for the Liberation of Palestine] had become marginalized. … It had gone from being the second most important Palestinian group to forth [sic] or fifth.”³ Anthony Cordesman, a highly regarded Middle East scholar and expert commentator for ABC News, echoes the BBC view. Reflecting back on the PFLP, he writes, “The PFLP opposed the Oslo peace process. As the PA [Palestinian Authority] and Arafat’s Fatah gained strength, the PFLP became increasingly marginalized.”⁴ Similarly, GlobalSecurity.org, generally seen as a reliable source of information, notes that, “Once a key player in Palestinian politics, the PFLP lost influence in the 1990s and was sidelined as Yasir Arafat established the Palestinian Authority.” The PFLP’s decline is similarly reported by numerous other sources. Most of these sources attribute the PFLP’s decline to events that happened after my 1990 article was published and long after Shmuel Eisenstadt wrote numbers on a couple of napkins. That is, the modeling exercise undertaken before the 1991 Gulf War, before the first intifada, and before the Oslo Accords foresaw the territorial changes between the Israelis and Palestinians, the necessity of a Labor government coming to power in Israel, and the decline of the PFLP. It foresaw the fundamental developments far enough ahead that even today people have trouble recognizing that the seeds of the agreement in 1993 had already been planted and were growing, unseen by most onlookers, well before.

What was it that the model identified in the give-and-take between the Palestinian side and the Israeli side based on data collected in 1987 and 1989 that led to a successful prediction? This is really the crux of the matter. After all, working out that the predicted outcome could lead to a stable agreement involved more than just pondering Eisenstadt’s original, vague question: “So, you can predict how to make peace in the Middle East?” Since Arafat and the Israeli prime minister both had vetoes over any deal, it was essential to figure out whether they were likely to end up supporting the same agreement, and if so, why. Here’s what the model indicated:

Israel’s Labor Party leader, Shimon Peres, was not in power when the study was done, and it was evident in the model’s results that until Labor came to power no agreement could be reached. That meant that the focus of attention in analyzing the possibility of a peace deal—the problem put to me by Shmuel Eisenstadt—had to be on Peres and the Labor Party. The model’s logic produced output that indicated that Peres believed he would face a lot of political pressure at home over his stance regarding the Palestinians. To attain and retain power—the goal of every politician—he believed (according to the model’s logic) that he had to look as though he would be tougher in Palestinian negotiations than was implied by the quite moderate stance he took in the late 1980s. The model showed he needed to be only a bit more moderate than Shamir, leading him to agree to occupy a position in the mid- to upper 60s instead of Shamir’s drift between 70 and 85 on the scale. So Peres was predicted to be responsive to political expediency at home.

For his part, Arafat concluded, according to the model’s simulations, that he needed to moderate his own stance to keep Labor from taking too hard-nosed a view within Israel. The model suggested that he would choose a course of action based on his personal political welfare rather than the well-being of the Palestinian people per se. As I wrote in the 1990 article, “The model solution suggests that Arafat would stabilize his political position, leaving himself devoid of serious political opposition either among the Palestinians or within Israel. … If Arafat does choose to moderate his stance, this suggests that he is willing to sacrifice both the Palestinian cause and his opponents at the altar of his personal political welfare.” That seems to have been the case. Thus the analysis went from a vague question to precisely structured propositions and a detailed analysis of the negotiating dynamics that made it impossible for Shamir and Arafat to reach agreement and those that made it possible for Arafat and Peres to come to terms once Peres became prime minister.

Asking the right questions and isolating the key interests for a given problem is too often a step that’s not taken from the beginning. Instead, we settle for conventional wisdom about the reasons behind actions that seem to fit the puzzle. The costs of this laziness can be grave, particularly when the problems are the kind for which society seeks remedies in order to prevent their recurrence. With an initial misdiagnosis, the wrong treatment will most assuredly follow. Had policy makers paid attention sooner to the pulls and tugs likely to face Arafat as well as the Israelis subsequent to Oslo, perhaps they could have managed circumstances better and might have avoided many of the setbacks between the Israelis and Palestinians since 1993.

DON’T JUST LOOK WHERE THE LAMPPOST SHINES

The failure to zoom in on what the issues are, who the players are, what their incentives are, and how to fix those incentives is not limited to problems in foreign affairs. The business world suffers at least as much from the same difficulties. To take an example of this from recent years, let’s look at a model that colleagues and I developed to identify the causes of and solutions to corporate fraud.

Since the Enron debacle, Congress has made a real effort to strengthen corporate incentives to report honestly by introducing a massive amount of new regulation through the Sarbanes-Oxley bill, passed in 2002. However, I’m afraid Sarbanes-Oxley touches on but does not nail the root causes of fraud, at least not as those causes are seen by the model my colleagues and I developed. As we can see in figure 5.1, fraud litigation is once again on the rise, despite the passage of Sarbanes-Oxley. Since the recession began in 2007, fraud has skyrocketed ahead of its pre-downturn 2006 level. This is just a bit of evidence—there is more to come—for my model-based belief that the premises that guided Congress in passing Sarbanes-Oxley were misguided. President Obama promises a new wave of regulatory controls to rein in the risk of business fraud and failure. Let’s hope that his administration and the Congress are more attentive to incentives so that they get the regulations right.

FIG. 5.1. Federal Securities Class Action Litigation, 2002-2008

To regulate the risk of fraud it is fundamental that we first understand the motives for committing fraud. How does the model that my colleagues and I worked on determine which companies have an incentive to commit fraud and which do not?

Our game-theory approach is a variant on the study we did to understand how nations are governed (recall dear old Leopold). But pay attention, as the context of the corporate setting provides a most interesting twist. As we saw in our study of Leopold and other heads of state, loyalty to leaders is much weaker in democracies because the competition is over policy ideas rather than the personal enrichment of a few supporters. Much the same might be said for corporations and the survival of corporate executives. From this starting point, we might assume that the outcome in the business world would be the same as that in the political: “autocratic” leadership leads to corruption (fraud), while more “democratic” leadership does not. This is not the case. Perversely, as we will see, the strong loyalty engendered by relatively autocratic corporate styles helps reduce the risk of fraud. To understand why, we have to leave the light of the lamppost and really look behind the scenes at the logic that governs corporate behavior.

Big firms have millions of shareholders. Yet few of them attend the annual shareholders meeting, and they have little idea of how the business is run or how it might have done if it had been run differently. They dutifully send in their proxy, voting the way the board suggests, or they toss their proxy statement in the recycling bin and do not vote at all. Big blocs of votes are controlled by a small number of institutional investors, senior executives, and directors. They, not the shareholders, decide how to run the company. The more institutional investors and powerful shareholders there are, the more parties to whom the management is accountable. Ring a bell? It’s the challenge presented to any leader: the more “democratic” the system (think of democracy not as an absolute concept but rather as a continuum), the more people to please.

When things are going well, the incentives for the executives running a company are not incompatible with the shareholders’ interests. Growth in profits is good for the executives and it is good for the shareholders. But sometimes things do not go well. Then the interests of management and shareholders might part ways. Let’s see why.

In developed equity markets, the fraud model indicates that accounting fraud typically results because management is trying to preserve shareholder value. Don’t get me wrong. The fraud model does not think of executives as altruists who lose sleep trying to think up ways to make shareholders better off. They commit fraud to protect their jobs in the face of poor performance rather than as a result of a desire to defraud investors per se. That means we can use public records to link the likelihood of fraud to any publicly traded corporation’s reported performance, ownership oversight, and governance-induced incentives to manage the firm truthfully.

Examining publicly available information taken from SEC filings, my colleagues and I found that the amount paid in dividends to shareholders and salaries to senior management during years of honest reporting and during years immediately preceding fraudulent reporting differ in significant ways. In the fraud years, senior management receive less compensation—you read that right, less compensation, not more—than expected given their corporate governance structure and reported corporate performance. Dividends typically also fall short of expectations. All the while, reported performance and therefore the firm’s growth in market capitalization look healthy, just as they do in honest years. It is important to note that the compensation for senior management may still have increased in such fraud years, and in an absolute sense may still be quite grand, but the critical point is that if things are really going so swimmingly for the company, then compensation should be even higher than it is.

The logic behind the model we designed pinpointed these as trends to look for. We didn’t know whether we would actually find these patterns in SEC filings, but we did know that they were the key to predicting fraud if our model was right.

Why these patterns? CEOs always have incentives to take actions that protect their jobs. If they see that their company is not performing up to market expectation, they are at risk and will take action to salvage their situation. Now, they can argue that the firm is the victim of unforeseeable shocks for which they should not be held accountable (this was the argument made by the CEOs of GM and Chrysler in seeking a government bailout—they pointed to the economic downturn, not management’s decisions, as the cause of the auto industry’s woes), but such arguments are risky to say the least, and may not be adequate to protect a CEO’s job.

If blaming the economy or some outside force doesn’t salvage senior management’s situation, then the top executives might misrepresent the corporation’s true performance. If they can sell the belief that the company is performing just fine, then they won’t be at risk of being fired. It is difficult for outsiders to know the corporation’s true volume of sales, revenue, costs, and profits. Market capitalization reflects these factors, and indeed these are the factors that when falsely reported and subsequently detected result in accusations of accounting fraud.

If revenues are exaggerated or costs are understated, then senior executives can temporarily lead the marketplace to misjudge the true worth of a company, making the company appear (falsely) to have met or exceeded expectations. This, the model suggests, is the essential motivation behind corporate fraud.

This wedge between lower-than-expected stock dividends and compensation for executives and seemingly normal or good growth in market capitalization is therefore an early-warning indicator of an elevated risk of fraud. Neither the SEC nor many corporations, however, seem to realize the importance of this information in detecting early signs of trouble. Sarbanes-Oxley certainly does not draw attention to analyzing the size of this benefits wedge.

The game’s logic and the evidence culled from more than a decade of corporate filings across hundreds of firms also raise questions about journalistic accounts and popular perceptions. A pretty standard journalistic view of fraud is that greedy executives act to enrich themselves at the expense of shareholders and employees, that they are little more than looters, and that the problem boils down to outrageous character flaws. This kind of thinking gets us nowhere.

Too often we look at what happened most recently and assume that earlier actions were motivated by those ends. It is easy to believe that greedy executives cook the books to enrich themselves, with self-interest tied merely to short-term gain (of course I would never argue that self-interest isn’t the key motivation, but we must examine exactly what the nature of that self-interest is). But in doing so we forget, for example, that Enron’s fraud started around 1997 and yet the senior managers did not sell off their shares until around 2000 and 2001. Why would they have waited so long, risking discovery for years, before cashing out? True, stock prices were going up, but equally true, the risk of being uncovered grew greater and greater with each passing month and year. Did they really only seek the gains to be made in the period from 1997 to 2001, or were they hoping for much greater gains five, ten, or fifteen years on?

We won’t analyze the problem properly if we look for the causes of fraud in its end result. Remember, correlation is not causation—the beginning, not the end, is where the explanation lies.

In the Enron case, it seems clear that by 2000 or 2001 the most senior leadership in the company realized that they could not fix its problems. Having reached the end of the corporate game, they cashed out. It is despicable that they did so while covering up the true state of affairs, thereby leaving their pensioners on the hook. But it seems equally evident that Enron’s senior management did not hang on for four years before cashing out just to enrich themselves and walk away. All that time, according to the game’s logic, they were trying to save the company because their longer-term interests would be rewarded if they were successful in doing so. If the numbers had to be fudged while they corrected the company’s course, so be it. In their view, the end justified the means. None of that was going to happen if the shareholders, and especially if key board members, found out what trouble Enron was in.

Give executives the wrong incentives and you can count on their taking actions with bad social consequences. Give them the right incentives and they will do what is right, not because they are filled with civic virtue but because it will serve their own interests. Remember Leopold? He had pretty good incentives in Belgium and he did good things there. He had horrible incentives in the Congo and he did horrible things there.

What, then, are the right and wrong incentives? Why do some companies commit fraud while others—the vast majority of firms—even in dire circumstances do not? In answering these questions we can gain insight into how to alter incentives appropriately and how to anticipate who has the wrong incentives and is at serious risk of committing fraud.

One clear implication of the fraud model my colleagues and I developed is that the broader the group of people CEOs rely on to keep their jobs, the more likely it is that the shareholders who put them in power will throw them out. That’s what happens to leaders of democracies, and that is what is more likely to befall underperforming CEOs in relatively democratic companies. To save themselves, they are perversely incentivized to misrepresent the corporation’s true performance so that they don’t have to explain underperformance in the first place.

This is not to say that more “autocratic” companies (fewer people to please in the power structure) are incapable of fraud. It’s just that things have to be considerably worse for those companies before management sees sufficient risk to their jobs that they are tempted to engage in fraud. Our sliding scale extends across the public/private company divide as we consider partnerships (think of them as oligarchies) and family companies (monarchies).

Government regulators and boards of directors could do a better job of protecting shareholders and employees from the risk of fraud. To do so, the focus needs to be more squarely placed on the incentives executives have to monitor themselves and their colleagues in the face of declining business performance. Knowing how to adjust governance structures to induce the right incentives is the way to regulate firms successfully. Balancing incentives in good times and bad is a major challenge for running a business in a way that attracts and retains top-quality executives and satisfies shareholder expectations. Optimal corporate governance design needs to be done on a case-by-case basis, taking the nature of the firm’s market into account. A sweeping regulation cannot facilitate the fine-tuning that is needed to get incentives right. Confidence in business requires that we move in these directions rather than putting our energies into finding greedy individuals to blame or one-size-fits-all fixes for what are manifestly corporate governance problems. Looking for greed is just like the drunkard looking for his keys under a lamppost. More often than not, what’s lost is not under the bright lights.

In a broader sense, if we truly want to make it easier for corporate executives to come clean about problems they discover as soon as they discover them, then we also ought to change the law so that they are not punished for spilling the beans on themselves.

Lots of companies discover problems with their products or their performance long before these problems become public. Indeed, it is a good bet that some serious problems never become public at all. A few years ago, for instance, the 3M Corporation pulled Scotchgard off the market. Scotchgard was one of its biggest earners, and yet one day it was in the stores and the next it was gone. A few years later, 3M introduced what it called a new, improved Scotchgard. The EPA and other firms in the chemical industry wondered whether 3M had discovered a health or safety risk associated with the main chemical in Scotchgard, a chemical not found in its “new and improved” product.

I don’t know whether 3M discovered a problem or just decided one day to change a successful product. Imagine that they did find a problem. What would they—no, better yet, what could they responsibly do? Company leaders in such situations may be eager to reveal whatever it is they’ve discovered, but they also realize that doing so would violate their fiduciary duty. They are damned if they do and damned if they don’t. A public announcement leaves them open to lawsuits by people who used the product before anyone—inside or outside the company—knew there was a problem. These suits can be devastating to shareholder value, and it is shareholder value that corporate directors are legally obliged to protect.

Probably many companies would reveal what they know when they discover trouble if the government would immunize them against prosecution for any problems in their products that were previously unknown to them. The government won’t. Litigation is the favored solution, as opposed to rewarding responsible, public-spirited actions by corporate executives in difficult straits. The result is that corporate leaders are given the wrong incentives. Remember all the litigation surrounding the problems caused by DDT? Do you also remember that the Royal Caroline Institute won the Nobel Prize for Physiology or Medicine in 1948 for developing DDT? With litigation run rampant, we fail to provide corporations and their leaders with protection for reasonable expectations and decisions that, not by any misdeeds on their parts, may simply turn out to be wrong.

In this chapter, we’ve explored how to frame questions. The main idea is to isolate the individual components of a problem that shape its resolution. Then it’s a straightforward matter of turning those isolated individual components into issues that, depending on the circumstances, may be decided separately or that may be linked to each other. Once an issue is well defined, experts have an easier time talking about who really will try to influence the decision on each item. Then we can have the computer play the forecasting and engineering game to simulate what proposals each player is expected to make to each other player on a round-by-round basis, and we can bring into relief the incentives that players have to accept or reject proposed solutions.

With the computer program at the ready, we can sort through the problem and not only predict results, as I did with the napkins, but begin to engineer results to change outcomes, as I hinted could be done to prevent corporate fraud. Engineering outcomes is the subject of the next two chapters.