Ambiguity is common, especially in innovation, and it is the uncommon individual who has the vision to make good decisions when dealing with ambiguity. These individuals must be willing to take a stand in spite of lack of hard evidence, and they personally shoulder the risks of their choices. These are managers like Dee Kapur, Chuck Jones, Edith Harmon, and the others mentioned in other chapters in this book. Rather than relying solely on results of experiments, they arm themselves with vision. Rather than waiting for overwhelming evidence of a correct path before moving forward, they use existing knowledge, experience, and resources.
By necessity or by training, innovators are comfortable with uncertainty. But not all of us are. Think about yourself. How comfortable are you making decisions without hard evidence? Would you defend human judgment as scientifically valid? Suppose, for example, that you and a friend cannot remember who paid for lunch last time, so you decide to flip a coin to see who pays today. You pull a U. S. quarter from your pocket, and you notice that it is one of the new quarters, each of which features a different state; this one is from Texas. If you are typical, you would not think twice about using that coin to decide who pays for lunch, even though you have never flipped any of the new Texas quarters before. If someone asked you whether you knew for certain that the coin was “fair,” you would have to agree that you do not know the exact probabilities, that one outcome may be slightly more likely than the other. Should that coin be used without further investigation? Should you first test it, maybe flipping it 1,000 times to see whether you get around 500 “heads”?
Most of us would use the coin without ever thinking about testing it. Put another way, we would simply assume that coin has equal chances of heads and tails. Would you be able to justify such an assumption? If you strictly apply the teaching of the typical required college statistics class, you would argue that you have no idea of the probability of a heads on a Texas quarter until you see some evidence. Yet, even while reading this, you probably still think it is close to being fair. Why? Is it not equally plausible that heads will show 9 times out of 10? The quarter could be more heavily weighted on the side that shows the state—everyone know that Texas is a really big state!
Science does support your gut here—that you can reasonably believe the coin is “fair” even though you have never tested it. How so? A relatively new statistical system, Bayesian statistics, supports the use of past information (such as experience) to help current decisions. You have probably flipped many coins over your lifetime, even though you have never flipped a Texas quarter. Because coins in your lifetime have been fair, or close enough to fair, your natural reaction with the new coin is to believe it also is fair, at least until proven otherwise. The formal, simple framework of your college statistics class would say you know nothing about the Texas coin until testing it. The Bayesian statistics framework would say that your wealth of experience with coin flipping is exactly what you should believe about this coin until it proves to be any different. The Bayesian statistics framework has revolutionized modern research methods. It is used as the basis for Internet search engines, e-mail spam filters, artificial intelligence systems, pharmaceutical tests, and much more.
Certainly, the Bayesian framework does not give you license to unbridled opinion and postulating. It does not toss out scientific rigor. You need the rigor of the Bayesian, the recognition of valid data, the discipline to throw away the irrelevant, the willingness to dive into the uncertain, and the care not to become overconfident. But it also tells us that educated insight has merit, just like statistical validation.
This world is that of the innovator—the simple acceptance of uncertainty, a willingness to make decisions in spite of a lack of information that reveals the “right” decision. The innovators’ familiarity with ambiguity has shaped their mind-set, has led them to see ways to improve their world that may be missed by others.