This article introduces strategists to the Mindspace framework and explores its applications in strategic contexts. This framework consists of nine effective behavioral interventions that are grounded in public policy applications, and it focuses on how changing the context can be more effective than attempts to de-bias decision makers. Behavioral changes are likely when we follow rather than fight human nature. Better decisions can be achieved by engineering choice contexts to "engage a bias" to overcome a more damaging bias. This article illustrates how to engineer strategic contexts through two case studies and outlines directions and challenges when applying Mindspace to strategic decisions.

Are the experts who successfully predict unusual events geniuses, or did they just get lucky? A new study indicates that their correct guesses were most likely flukes. It looked at the track records of forecasters who had one or two big wins and found that in the long run they were wrong more often than not.

To find the secrets of business success, what could be more natural than studying successful businesses? In fact, nothing could be more dangerous, warns this Stanford professor. To generalize from the examples of successful companies is to reach conclusions from an unrepresentative data sample, falling into the classic statistical trap of selection bias. Drawing on a wealth of case studies, for instance, one researcher concluded that great leaders share two key traits: They persist, often despite initial failures, and they are able to persuade others to join them. But those traits are also the hallmarks of spectacularly unsuccessful entrepreneurs. To discover what makes a business successful, then, managers should look at both successes and failures. Otherwise, they will overvalue risky business practices, seeing only those companies that won big and not the ones that lost dismally. They will not be able to tell whether their current good fortune stems from smart business practices or from coasting on past accomplishments or good luck. Fortunately, economists have developed relatively simple tools that can correct for selection bias even when data about failed companies are hard to come by.