gear: “Oh, that's simple. The chicken claw goes with the chicken, and you need a shovel to clean out the chicken shed.” Rather than saying, “I don't know,” the left hemisphere made up a response based on what it knew.6
Steven Pinker, a psychologist at Harvard, calls this part of the left hemisphere the baloney-generator. He wrote, “The spooky part is that we have no reason to believe that the baloney-generator in the patient's left hemisphere is behaving any different from ours as we make sense of the inclinations emanating from the rest of our brains. The conscious mind—the self or soul—is a spin doctor, not the commander in chief.”7 Gazzaniga's patient simply reveals what's going on in all of our heads.
To explain the past, we also naturally apply the essential elements of stories: a beginning, an end, and a cause.8 As events in our world unfold, we don't—really, can't—know what's happening. But once we know the ending, we stand ready to create a narrative to explain how and why events unfolded as they did.9 For our purpose, the two critical elements required for analyzing the past are that we already know the ending and that we want to understand the cause of what happened. Those two elements are what get us into trouble. Most of us will readily believe that this happens to others. But we are much more reluctant to admit that we can fall prey to the same bias.
We often assume that if event A preceded event B, then A caused B. Even Nassim Taleb, who has done a great deal to raise the awareness of the role of randomness and luck in our daily lives, points the finger at himself in this regard. He tells this story: Every day, he used to take a cab to the corner of Park Avenue and 53rd Street in New York City and take the 53rd Street entrance to go to work. One day, the driver let him out closer to the 52nd Street entrance and threw Taleb off his routine. But that day, he had great success at his job trading derivatives. So the next day, he had the cab driver drop him off on the corner of Park and 52nd Street so that he could extend his financial success. He also wore the same tie he had worn the day before. He obviously knew intellectually that where he got out of the cab and which tie he wore had nothing to do with trading derivatives, but he let his superstition get the best of him. He admitted that, deep down, he believed that where he entered the building and what tie he wore were causing him to succeed. “On the one hand, I talked like someone with strong scientific standards,” he continued. “On the other, I had closet superstitions just like one of these blue-collar pit traders.”10 Taleb entered the building from 52nd Street and then made money; therefore entering the building from 52nd Street caused him to make money. That faulty association is known as the post hoc fallacy. The name comes from the Latin, post hoc ergo propter hoc, “after this, therefore because of this.” A lot of the science done in the last two hundred years has been aimed at doing away with that mistaken way of thinking.
Knowing the end of the story also leads to another tendency, one that Baruch Fischhoff, a professor of psychology at Carnegie Mellon University, calls creeping determinism. This is the propensity of individuals to “perceive reported outcomes as having been relatively inevitable.”11 Even if a fog of uncertainty surrounded an event before it unfolded, once we know the answer, that fog not only melts away, but the path the world followed appears to be the only possible one.
Here is how all of this relates to skill and luck: even if we acknowledge ahead of time that an event will combine skill and luck in some measure, once we know how things turned out, we have a tendency to forget about luck. We string together the events into a satisfying narrative, including a clear sense of cause and effect, and we start to believe that what happened was preordained by the existence of our own skill. There may be an evolutionary reason for this. In prehistoric times, it was probably better for survival to take the view that we have some control over events than to attribute everything to luck and give up trying.
John Glavin is a professor of English at Georgetown University who teaches courses in writing for the stage and screen. Glavin spends a great deal of time understanding what makes for a great narrative and emphasizes that stories are vehicles for communicating how to act. We use stories, especially those about history, to learn what to do. “Narrative is deeply connected with ethics,” he notes, “and narratives tell us how we should and should not behave.” But when we try to learn from history, we naturally look for causes even when there may be none. Glavin adds, “For a story to work, someone has to be responsible.”12 History is a great teacher, but the lessons are often unreliable.
Undersampling and Sony's Miraculous Failure
The most common method for teaching a manager how to thrive in business is to find successful businesses, identify the common practices of those businesses, and recommend that the manager imitate them. Perhaps the best-known book about this method is Jim Collins's Good to Great. Collins and his team analyzed thousands of companies and isolated eleven whose performance went from good to great. They then identified the concepts that they believed had caused those companies to improve—these include leadership, people, a fact-based approach, focus, discipline, and the use of technology—and suggested that other companies adopt the same concepts to achieve the same sort of results. This formula is intuitive, includes some great narrative, and has sold millions of books for Collins.13
No one questions that Collins has good intentions. He really is trying to figure out how to help executives. And if causality were clear, this approach would work. The trouble is that the performance of a company always depends on both skill and luck, which means that a given strategy will succeed only part of the time. So attributing success to any strategy may be wrong simply because you're sampling only the winners. The more important question is: How many of the companies that tried that strategy actually succeeded?
Jerker Denrell, a professor of strategy at Oxford, calls this the undersampling of failure. He argues that one of the main ways that companies learn is by observing the performance and characteristics of successful organizations. The problem is that firms with poor performance are unlikely to survive, so they are inconspicuously absent from the group that any one person observes. Say two companies pursue the same strategy, and one succeeds because of luck while the other fails. Since we draw our sample from the outcome, not the strategy, we observe the successful company and assume that the strategy was good. In other words, we assume that the favorable outcome was the result of a skillful strategy and overlook the influence of luck. We connect cause and effect where there is no connection.14 We don't observe the unsuccessful company because it no longer exists. If we had observed it, we would have seen the same strategy failing rather than succeeding and realized that copying the strategy blindly might not work.
Denrell illustrates the idea by offering a scenario in which firms that pursue risky strategies achieve either high or low performance, whereas those that choose low-risk strategies achieve average performance. A high-risk strategy might put all of a company's resources into one technology, while a low-risk strategy would spread resources across various alternatives. The best performers are those that bet on one option and happen to succeed, and the worst performers are those that make a similar bet but fail. As time passes, the successful firms thrive and the failed firms go out of business or get acquired.
Someone attempting to draw lessons from this observation would therefore see only those companies that enjoyed good performance and would infer, incorrectly, that the risky strategies led to high performance. Denrell emphasizes that he is not judging the relative merits of a high- or low-risk strategy. He's saying that you need to consider a full sample of strategies and the results of those strategies in order to learn from the experiences of other organizations. When luck plays a part in determining the consequences of your actions, you don't want to study success to learn what strategy was used but rather study strategy to see whether it consistently led to success.
In chapter 1, we met Michael Raynor, a consultant at Deloitte. Raynor defines what he calls the strategy paradox—situations where “the same behaviors and characteristics that maximize a firm's probability of notable success also maximize its probability
