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making “policy.” But policymakers don't always understand the problem well enough to get the solution right. A sizable body of research records a continuing saga of perverse ways in which the execution undermines even good solutions (Bardach, 1977; Elmore, 1978; Freudenberg and Gramling, 1994; Gottfried and Conchas, 2016; Grindle, 2017; Peters, 1999; Pressman and Wildavsky, 1973). Policymakers, for example, have been trying for decades to reform U.S. public schools. Billions of taxpayer dollars have been spent. The result? About as successful as America's switch to the metric system. In the 1950s, Congress passed legislation mandating the adoption of metric standards and measures. More than six decades later, if you know what a hectare is or can visualize the size of a 300‐gram package of crackers, you're ahead of most Americans. Legislators did not factor into their solution what it would take to get their decision carried out against longstanding custom and tradition.

In short, the difficulties surrounding improvement strategies are well documented. Exemplary intentions produce more costs than benefits. Problems outlast solutions. Still, there are reasons for optimism. Organizations have changed about as much in recent decades as in the preceding century. To survive, they had to. Revolutionary changes in technology, the rise of the global economy, and shortened product life cycles have spawned a flurry of efforts to design faster, more flexible organizational forms. New models flourish in companies, such as Valve (the nonhierarchical video game powerhouse that shuns job titles and organization charts), Wegman's (the mission‐driven supermarket chain that consistently ranks among America's best places to work), Google (the global search giant), Airbnb (a new concept of lodging), and Novo‐Nordisk (a Danish pharmaceutical company that includes environmental and social metrics in its bottom line). The dispersed collection of enthusiasts and volunteers who provide content for Wikipedia and the far‐flung network of software engineers who have developed the Linux operating system provide dramatic examples of possibilities in the digital world. But despite such successes, failures are still too common. The nagging question: How can leaders and managers improve the odds for themselves as well for their organizations?

FRAMING

      Goran Carstedt, the talented executive who led the turnaround of Volvo's French division in the 1980s, got to the heart of a challenge managers face every day:

      The world simply can't be made sense of, facts can't be organized, unless you have a mental model to begin with. That theory does not have to be the right one, because you can alter it along the way as information comes in. But you can't begin to learn without some concept that gives you expectations or hypotheses. (Hampden‐Turner, 1992, p. 167)

Such mental models have many labels—maps, mind‐sets, schema, paradigms, heuristics, and cognitive lenses, to name but a few.¹ Following the work of Goffman, Dewey, and others, we have chosen the label frames, a term that has received increasing attention in organizational research as scholars give greater attention to how managers make sense of a complicated and turbulent world (see, e.g., Cornelissen and Werner, 2014; Foss and Weber, 2015; Gray, Purdy, and Ansari, 2015; Hahn, Preuss, Pinkse, and Figge, 2014; Maitlis and Christianson, 2014; Seidel, Hannigan, and Phillips, 2020). In describing frames, we deliberately mix metaphors, referring to them as windows, maps, tools, lenses, orientations, prisms, and perspectives, because all these images capture part of the idea we want to convey.

      A frame is a mental model—a set of ideas and assumptions—that you carry in your head to help you understand and negotiate a particular “territory.” A good lens makes it easier to know what you are up against and, ultimately, what you can do about it. Mental maps are vital because organizations don't come with computerized navigation systems to guide you turn‐by‐turn to your destination. Instead, managers need to develop and carry accurate charts in their heads.

Such maps make it possible to register and assemble key bits of perceptual data into a coherent pattern—an image of what's happening. When framing works fluidly, the process takes the form of “rapid cognition,” the process that Gladwell (2005) examines in his best seller Blink. He describes it as a gift that makes it possible to read “deeply into the narrowest slivers of experience. In basketball, the player who can take in and comprehend all that is happening in the moment is said to have ‘court sense’ ” (p. 44). The military stresses situational awareness to describe the same capacity.

      Dane and Pratt (2007) describe four key characteristics of this intuitive “blink” process:

       It is nonconscious—you can do it without thinking about it and without knowing how you did it.

       It is very fast—the process often occurs almost instantly.

       It is holistic—you see a coherent, meaningful pattern.

       It results in “affective judgments”—thought and feeling work together so you feel confident that you know what is going on and what needs to be done.

      The essence of this process is matching situational cues with a well‐learned mental framework—a “deeply held, nonconscious category or pattern” (Dane and Pratt, 2007, p. 37). This is the key skill that Simon and Chase (1973) found in chess masters—they could instantly recognize more than 50,000 configurations of a chessboard. This ability enables grand masters to play 25 lesser opponents simultaneously, beating all of them while spending only seconds on each move.

      The blink process is key to expertise and skill. Kahneman and Klein (2009) argue that it works best for individuals who have developed a deep understanding of a particular domain through experience and deliberate practice with feedback. Skill and expertise come to those who are willing to invest time and effort and learning (Ericsson, 2005). But for nonexperts, fast, intuitive thinking often leads to very bad judgments. Experts typically know when they don't know, but nonexperts think they know when they don't (Kahneman and Klein, 2009). “Subjective confidence is therefore an unreliable indication of the validity of intuitive judgments” (p. 524).

Research on human thinking has led to the identification of two distinct modes of cognition that operate in parallel: Type I (intuitive and automatic) and Type II (deliberate and analytic), summarized in Exhibit 1.1. Intuition is faster, requires less cognitive effort, and produces holistic judgments. It works best in the hands of experts dealing with fluid, messy problems, particularly if time is short. Analytic thinking is slower and requires more effort and conscious attention but can lead to superior judgment and decision in situations with well‐structured problems and high‐quality evidence (Evans and Stanovich, 2013; Hodgkinson and Sadler‐Smith, 2018; Kahneman, 2011; Kahneman and Klein, 2009; Luan, Reb, and Gigerenzer, 2019). Many businesses analyze big data to discover insights and patterns culled from mountains of data far beyond the capacity of any human mind.

Exhibit 1.1. Characteristics of Two Types of Human Thinking.

Type I (Intuitive)	Type II (Deliberate)
Fast Nonconscious Automatic Does not rely on working memory Requires less mental energy Relies on tacit, implicit knowledge	Slow Conscious Intentional Requires use of working memory Requires more mental energy Relies on explicit knowledge

      In medicine, there is a growing emphasis on “evidence‐based medicine”—basing diagnosis and treatment on rules derived from research. Emergency room physicians who treat stroke victims, for example, have a detailed set of guidelines for diagnosis and treatment that are periodically updated as new research comes in. Some scholars have argued that the same idea can also work for managers (Barends and Rousseau, 2018; Martelli and Hayirli, 2018; Pfeffer and Sutton, 2006, 2011), though evidence for the benefits of evidence‐based management is still sketchy (Reay, Berta, and Kohn, 2017). Pfeffer and Sutton (2011) cite research showing that incentive

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