You know how you can tell when you've made a good decision? If you feel like you waited too long to make it, then it's a good decision.

Checking the results of a decision against its expectations shows executives what their strengths are, where they need to improve, and where they lack knowledge or information.

Business proposals and decisions—big or small—have to provide satisfactory answers to this question: “Do we think this is true or do we know?”

There are three very distinct ways that organizations can fall into the analysis-paralysis trap. One is a managerial tendency to “over-fit the curve”—a statistical term that refers to the diminishing value of additional data once a pattern (or curve, in the graphic sense) has been found. Data collection has a price, inaction has a price and an analytically literate organization will clearly understand the cost of over-fitting.

The second cause of analysis-paralysis is waiting for data that simply does not exist, which reflects an inability to design experiments to generate the needed data. As mentioned above, experimentation has a price and inaction has a price, so an analytically literate organization will be characterized by a clear understanding of data gaps and the value of experimentation to break the logjam.

The third cause of analysis-paralysis is the fact that most companies do not know or articulate their risk tolerance clearly and are much more likely to penalize failed action than inaction. As a result, many managers do not act unless there is enough data to assure them of successful outcomes. An analytically literate organization will have a firm grasp of its risk tolerance. With guidelines and models for action under uncertainty, it will restore the symmetry between how it treats failed action and inaction.

Great empires are not built by people who see two sides to every question.

Lucky risk takers use hindsight to reinforce their feeling that their gut is very wise. Hindsight also reinforces others’ trust in that individual’s gut. That’s one of the real dangers of leader selection in many organizations: leaders are selected for overconfidence. We associate leadership with decisiveness. That perception of leadership pushes people to make decisions fairly quickly, lest they be seen as dithering and indecisive.

We work with many scientists, chemists, engineers, and accountants. By training, they are usually able to absorb, digest, and analyze large amounts of information. Their challenge is in making the leap from information to implication. Frequently, head-only leaders will struggle with the implications because wild swings in social, economic, and technological trends undermine logical, fact-based forecasts. Guts-only leaders will miss the boat because their instincts don’t function as well in an era when all the rules have changed and experience (which sharpens instinct) has become less relevant as a predictive tool. And heart-only leaders will have difficulty identifying future trends because they’re drowning in a sea of opinions and feelings—the more they listen, the more open they are to fresh perspectives, and the more confused they become.

We’re not dismissing the strengths of each type of leader. Far from it. What we are suggesting is that in an uncertain, interdependent world, leaders need to avail themselves of all three capabilities if they are to avoid the obvious pitfall of overdoing their strengths.

We naturally develop patterns of thought and behavior over time. These are a sort of survival technique, enabling us to deal with what life has taught us can be classified and dealt with by recourse to routine approaches. As a result, our reservoirs of intellectual energy are freed from being drained by repetitive solutions to the same problems, and are available to be alert to new developments and to devise stratagems to deal with them.

The problem is twofold. We sometimes erroneously classify the countless cues that assault us each day, and wind up well down dysfunctional paths to addressing some of them before we realize, if we ever do, what is happening. Second, we begin to accord undue status to the patterns we have created, evaluating data according to how they fit our theories, rather thanthe reverse. We need to periodically redirect our newly liberated capacity for espying new information back onto our old solutions in order to reassess them—and our use of them.

Some employees make products, some make sales; the CEO makes decisions. Therefore, a CEO can most accurately be measured by the speed and quality of those decisions. Great decisions come from CEOs who display an elite combination of intelligence, logic, and courage.

When management waits until the data is clear, the game is over. But that means management has to take action on a theory rather than evidence. Unfortunately, the word theory gets a bum rap at the Harvard Business School and in business in general because it’s associated with the term theoretical, which connotes impractical. But actually theory is very practical. It says this will happen and this is why; it’s a statement of cause and effect. In our teaching we have so exalted the virtues of data-driven decision making that in many ways we condemn managers only to be able to take action after the data is clear and the game is over. In many ways a good theory is more accurate than data. It allows you to see into the future more clearly.

If we consider what the work of attention is like, how continuously it goes on, and how imperceptibly it builds up structures of value round about us, we shall not be surprised that at crucial moments of choice most of the business of choosing is already past.

There is a tendency in our planning to confuse the unfamiliar with the improbable. The contingency we have not considered seriously looks strange; what looks strange is thought improbable; what is improbable need not be considered seriously.

You have a good conceptual tool if you can answer ‘yes’, to the following four questions:
1. Is it portable? Can you use it to generalize across situations?
2. Is it actionable? Does it give you information about what to do?
3. Is it trustworthy? Does it give you what you want more often than not?
4. Is it adaptable? Can you change it as you learn, and use it in original ways?

A committee is a group of men who individually can do nothing, but as a group decide that nothing can be done.

Decisions are made well only if based on a clash of conflicting views. The first rule of decision-making is that one does not make a decision unless there is disagreement. It safeguards the decision-maker against becoming a prisoner of the organization (or culture).

In the Information Age, information was a relatively scarce resource that conferred competitive advantages on those who obtained it. In the Knowledge Era, by contrast, information is virtually free. We often feel we’re drowning in the stuff. In theory, the true competitive advantage stems from turning all this information into useful knowledge. It’s a nice theory, as far as it goes. The truth, however, is that even knowledge holds little value until we use it to make decisions. Decision making is the all-important intermediate step between knowledge and action, between strategy and execution.

While the mind looks for proof, the heart looks for engagement. While the mind looks for information, the heart looks for passion. While the mind looks for answers, the heart looks for experience. The mind makes a decision, and it's the heart that makes a commitment.

An expert's job is to be right — to solve the client's problems through the application of technical and professional skill. The advisor behaves differently. Rather than being in the right, the advisor's job is to be helpful, providing guidance, input, and counseling to the client's own thought and decision-making processes. The client retains control and responsibility at all times; the advisor's role is subordinate to this, not that of a prime mover.

Cost-benefit analysis can be an effective tool to analyze simple, one-dimensional problems, such as whether to install dividers on dangerous stretches of highway, where relatively unambiguous data is in abundant supply and there is little controversy. It also is a good way to elucidate the trade-offs for a given policy or regulation, or to produce a summary statistic about its economic efficiency.

But the cost-benefit method loses its authority when it’s used to assess more complex decisions. It is inadequate for evaluations of interventions that will affect many different dimensions, such as markets, economies, health, the environment, and endangered species. Cost-benefit analysis is also inappropriate for products or processes over which there are disagreements about benefits or about which outcomes are important. And it should never be used as the basis for regulation in the presence of scientific uncertainty or value conflicts, or in an area where there are no authorities one can trust to know all the answers. Decisions like these require a more expansive methodology — one that isn’t dependent on the affectation of translating all value into economic terms, that is more transparent and responsive to outside criticism, and that pragmatically represents the interests of everyone involved: industry, government, and the public.

Humans do not make rational, logical decisions based on information input, instead they pattern match with either their own experience, or collective experience expressed as stories. It isn’t even a best fit pattern match, but a first fit pattern match … The human brain is also subject to habituation, things that we do frequently create habitual patterns which both enable rapid decision making, but also entrain behavior in such a manner that we literally do not see things that fail to match the patterns of our expectations.

Organizations are out there every day, making tons of decisions, but they aren’t keeping track of them. There are many factors within organizations that make them reluctant to learn from experience, so it’s a forlorn hope, but the goal would be to have dispassionate evaluations of past decisions, and to spend some effort in figuring out why each decision did or did not pan out. Doing that systematically is key: really try to question the way you make decisions, and improve it. There is very little motivation within organizations to do this, because it threatens people. Executives don’t like to be second guessed, and procedures that are threatening to them are not likely to be adopted. But as a result, organizations are learning much less than they could from their experiences.

The most effective way people can change a story is to view it through any of three new lenses, which are all alternatives to seeing the world from the victim perspective. With the reverse lens, for example, people ask themselves, "What would the other person in this conflict say and in what ways might that be true?" With the long lens they ask, "How will I most likely view this situation in six months?" With the wide lens they ask themselves, "Regardless of the outcome of this issue, how can I grow and learn from it?" Each of these lenses can help people intentionally cultivate more positive emotions.

Lyle's Law of Certitude: The more certain you are that you are correct, the more imperative it is to consider that you might be wrong.

Quick decisions based on wrong assumptions lead to quick trouble. Quick decisions based on a faulty analogy do the same. "Ready, fire, aim" is a prescription for poor marksmanship.

Richard Neustadt asks, "are you facing a problem that can be solved or a condition that must be treated?" Mistaking one or the other can be painful.

Are you using a flawed analogy? Assumptions that are not true? Are you asking "what should I do about this?" before you ask "how should I think about this?" If you are, you get only the solution to the wrongly defined problem.

These are the essentials of critical thinking. And there are a couple more:
* Being extro-spective: seeing the bigger picture beyond the immediate situation
* Looking around the corner: having a sense of what could happen later
* Having the ability to view doing nothing as one of the possible choices

The problem -- and the essence of what makes forecasting hard -- is that human nature is hardwired to abhor uncertainty. We are fascinated by change, but in our effort to avoid uncertainty we either dismiss outliers entirely or attempt to turn them into certainties that they are not.