Decision-making under VUCA conditions


I was recently asked if there is a decision making approach that’s designed specifically for situations characterized by volatility, uncertainty, complexity, and ambiguity (VUCA). I don’t know of a one-size-fits-all solution, but I can speak to what’s needed to optimize decisions made in VUCA conditions. Here are the main ingredients:


  1. Acrobatic-catThe ability to adjust one’s decision-making approach to meet the demands of a particular problem: For example, some problems must be addressed immediately and autocratically, others are best addressed more collaboratively and with a greater focus on data collection and perspective seeking.
  2. The ability to make high-quality autocratic decisions: By setting up systems that keep stakeholders continuously appraised of one another’s perspectives and data, we can improve the quality of autocratic decisions by ensuring that there are fewer surprises and that rapid decisions are informed decisions.
  3. Dynamic steering: Every leader in an organization should be constantly cultivating this skill. It increases the agility of teams and organizations by building skill for efficient decision-making and timely adjustment.

The most complete information possible (under conditions in which complete information is impossible), which requires:

  1. Collaborative capacity: highly complex problems, by definition, are beyond the comprehension of even the most developed individuals. Collaborative skills ensure that leaders can effectively leverage key perspectives.
  2. Systems and structures that foster ongoing two-way communication up and down the organizational hierarchy, across departments, divisions, and teams, and between internal and external stakeholders.
  3. Systems and structures that cultivate excellent perspective-taking and -seeking skills. These include…
    • Building in opportunities for collaborative decision-making,  
    • “Double linking”—the formal inclusion, in high-stakes or policy decision-making, of representatives from lower and higher levels in the organizational hierarchy or from cross-disciplinary teams, and
    • Embedding virtuous cycles to ensure that all processes are continuously moving toward higher functioning states, and that employees are constantly building knowledge and skills.

Where appropriate, technologies for constructing models of highly complex problems:

  • For a comprehensive overview of options, see Decision Making Under Uncertainty: Theory and Application, by Mykel J. Kochenderfer.

Our flagship adult assessment, the Leadership Decision-Making Assessment (LDMA), was designed for the US government to document and assess the level of sophistication individuals and teams demonstrate on key skills for making optimal decisions in VUCA conditions.


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Virtuous cycles of learning and instruction

What is a virtuous cycle of learning?

Ideal learning occurs in virtuous cycles—repeating cycles of goal settingobservation (taking in new knowledge), testing (applying what has been learned and getting feedback on results), and reflection (figuring out which adjustments are needed to improve one’s performance on the next attempt). This process, which occurs unconsciously from birth, can be made conscious. One recent application of the virtuous cycle is in dynamic steering, in which decisions are developed, applied, and evaluated through intentionally iterating cycles. The idea is to stretch as far as possible within a given cycle, without setting immediate goals that are completely beyond one’s reach. Success emerges from the achievement of a series of incremental goals, each of which brings one closer to the final goal. Processes of this kind lay down foundational skills that support resilience and agility. For example, the infant who learns to walk also learns to fall more gracefully, which makes learning to run much less traumatic than it might have been. And decision makers who use dynamic steering learn a great deal about what makes decisions more likely to be successful, which leads to better, faster decisions in increasingly complex or thorny situations.


The figure on the right illustrates how educators can support virtuous learning cycles. There are 4 "steps" in this process (not necessarily in the following order):

  1. Find out what individual learners already know and how they work with their knowledge, then set provisional learning goals.
  2. Acquire and evaluate information
  3. Apply new knowledge or skills in hypothetical or real-life situations.
  4. Provide frequent opportunities for learners to reflect upon outcomes associated with the application of new knowledge in an environment in which ongoing learning, application, and reflection are consistently rewarded.

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Testing as part of learning 2

I can’t help it, I’m a developmental psychologist. I’ve been lurking about, watching my Granddaughter, Erwin, as she learns to master her environment. She’s about 8 months old now (real age, she was three months premature, so her birth age is 11 months)

Last week, Erwin figured out that complex actions can be used intentionally to make things happen in social situations. For example, she started reaching toward her Mom and Dad to indicate her intention to be picked up. At around same time, she began pointing to objects to indicate interest or draw them to the attention of her others. And she has begun to imitate actions like waving, clapping, and head shaking. Today, when we were Skyping, she clapped her hands to get me to play pat-a-cake, and she shakes her head to get her Mom to do the same—which she finds hilarious. To Mom’s dismay, Erwin is so excited by this new way of influencing her environment that she has stopped napping.

To see an example of Erwin’s attempts at verbal communication and her new reaching behavior, double-click on the picture below. Notice how emphatic her arm extension is, and how she makes eye contact as she reaches out.

A few months ago, most of Erwin’s actions were aimed toward physical mastery—learning to obtain objects and manipulate them in a variety of ways, learning to move herself toward things she wanted to manipulate, or playing with sound just to hear the results.

When she was learning to do physical things, the physical environment provided most of the feedback. Although her parents were there to give encouragement, we all had the sense that it was the physical feedback that she craved—getting an object to her mouth, inching toward a favorite toy, pulling herself to stand.

Now she craves feedback from her parents; she has shifted her focus from physical mastery to social mastery. She reaches for Mom and gets picked up. She shakes her head and Mom shakes her head back. She points to a banana, and Dad brings it to her. She claps her hands, and Grandma plays pat-a-cake. And every time she undertakes a new action, she is conducting a test.

Testing is part of learning.

Each time any infant tries out a new skill, she is conducting a test. Each attempt is part of an action-feedback loop. Repeated attempts to master a new skill form a series of these action-feedback loops. Each iteration is an exemplary test—in the sense that it is educative—that guides the infant incrementally toward a new level of mastery.

Interestingly, infants never tire of this kind of testing, even when the feedback is not instantly gratifying. In fact, much of the feedback is along the lines of, “almost, but not quite,” or “that didn’t work,” neither of which seem to get in the way of infant learning. For example, when Erwin first started reaching toward her parents to ask to be picked up, her action was not easy to read. It rarely got the desired response. She gradually learned that the reaching needed to be clearly directed toward the parent and accompanied by eye contact. Now the message is, “You’ve got it!” At this point, Erwin takes the skill for granted, and has shifted her attention to things she has not yet mastered, like figuring out how to get adults to do other interesting or gratifying things.

The natural action-feedback mechanism of infancy works perfectly, because the proverbial carrot is usually, due to the very nature of normal human environments, dangled at just the right distance. Good parents respond to early attempts at communication, rewarding them with interesting responses, but success isn’t the only reward; it’s always accompanied by a new “carrot”—another interesting possibility just beyond the infant’s reach. In this way, the action-feedback mechanism functions both as an aid to learning and as a motivator.

Aspects of this “carrot-and-stick” perspective on learning have been expanded and described in a variety of research traditions—e.g., as part of the notion of reinforcement feedback in social learning theory (Bandura, 1977), as zone of proximal development in Vygotsky’s (1986) work, and as part of a complex process of assimilation and accommodation in Piaget’s (1985) work. It is important, because it speaks both to how we learn and to our motivation for learning. Good feedback plays two essential roles. First, it helps the learner decide what to try next. Second, it motivates the learner to keep striving toward mastery. And, as the infant example suggests, feedback cannot be reduced to simple reward or punishment. Ideally, it is information that supports learning by being useful to the learner. Learners are not motivated by reward or punishment per se, but by an optimal combination of “not there yet” “almost” and “you’ve got it”.

DiscoTests are for learning

Most of today’s tests provide feedback in the form of rewards (good grades, advancement, or honors) or punishment (bad grades and failure). My colleagues and I don’t find this acceptable, so we’ve created a nonprofit called DiscoTest. The overarching objective of the DiscoTest Initiative is to contribute to the development of optimal learning environments by creating assessments that deliver the kind of educative feedback that learners need to learn optimally. DiscoTests determine where students are in their individual learning trajectories and provide feedback that points toward the next incremental step toward mastery.

I’ll be writing more about DiscoTest in future posts. For now, if you’d like to know more, please visit the DiscoTest web site.

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Tacit knowledge has problems

Tacit knowledge can help us make quick decisions in familiar situations. But it has its limits…

As you probably know if you are reading this post, my colleagues and I make developmental assessments, several of which are focused on adult skills like leaders’ decision making. I am often asked about the validity of these assessments when it comes to implicit or tacit knowledge. The short answer is that tacit knowledge is not captured by any assessment that asks people to show how they reason through a problem — because tacit knowledge is, well, tacit.

Some people think this is a problem with our assessments, but we have come to the conclusion that the real problem lies in the kind of learning — unreflective experiential learning — that produces tacit knowledge.

What is tacit knowledge?

Often, people know more about a particular subject than they can communicate verbally. This is because much of what we learn through experience does not automatically become part of our conscious (or explicit) knowledge. It’s in a form that’s difficult to share. It is tacit or implicit. Tacit knowledge is not always a bad thing. It helps us make quick choices in familiar situations; we’d be in big trouble if we had to think through every single situation in our lives before we made a decision! But tacit knowledge has its problems.

Problems with tacit knowledge

There are four serious problems with tacit knowledge. (1) We can’t share it, (2) it’s limited, (3) it can be unreliable, and (4) it can slow development.

We can’t share tacit knowledge. Tacit knowledge is created through unreflective direct experience. It doesn’t reside in consciousness, so we can’t readily share it with others. One context in which this is particularly problematic is in “intuitive” leadership. An intuitive leader, because his or her leadership skills are tacit, can’t teach others how to lead. When organizations are led by intuitive leaders, they often have trouble developing the leaders of tomorrow.

Tacit knowledge is limited to what we can learn from direct experience: Because tacit knowledge is created through unreflective direct experience, it isn’t terribly useful for dealing with novelty or abstraction. It’s most useful in familiar situations. Unfortunately, the world we live in constantly presents new challenges. To meet these challenges, we must continually evaluate the quality of new and existing knowledge.

Tacit knowledge can be unreliable. Often, tacit knowledge is sub-optimal. When we don’t consciously curate our knowledge, we’re leaving the job of networking that knowledge to our unconscious brain. The unconscious brain — sometimes called System 1 — networks knowledge purely by making associations. And though System 1 is fast and efficient, its default settings often make associations that lead us astray. We stand a better chance of networking knowledge effectively if we pay attention to what we’re learning and do some conscious steering.

Tacit knowledge can slow development. When we habitually learn primarily through unreflective experience or action, learning becomes increasingly slow and inefficient. People learn faster and better when they habitually reflect consciously upon experience.

Implications for assessment

The implications for assessment are clear. Tacit knowledge isn’t visible in a developmental assessment of reasoning skills. Although good developmental assessments can provide accurate evaluations of the level of complexity people can explicitly work with in specific skill areas, they can’t tell us about tacit capabilities.

Fortunately, my colleagues and I have learned to detect situations in which tacit knowledge is preventing someone from sharing their knowledge effectively. Once we’ve identified the issue, the next step is to help affected individuals bring their tacit knowledge into awareness. After that, we teach them the skills they need to become habitually reflective every-moment learners.

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Falling in love with complexity

As the idea of vertical development in the workplace takes hold, it’s important to be aware of a potential problem—falling in love with complexity.

In college it’s a common thing. Students, exposed for the first time to the complex ideas of philosophers, psychologists, and scientists, fall in love with complexity. They want, more than anything, to be able to think like their heroes, and many assume that the best way to do this is to emulate their language. Big words, complex sentences, and abstract ideas—the more abstract the better—abound. Once-competent writers temporarily lose their own voice. Students who have not already developed solid writing skills produce gobbledygook (complexity without integration). Good professors provide constructive feedback and support learning that will eventually tame the gobbledygook and unleash more mature voices.

Falling in love with complexity is now common in adults. Vertical development is a popular idea these days, and many adults are jumping on the complexity bandwagon, which is fueled by an abundance of popular books that tout the superiority of systems thinking, dialectical reasoning, etc. What the authors of these books often don’t tell their readers is that learning to think in a more complex way usually requires years of study in a particular discipline*. Readers are led to believe that all they need to do is read a book, take a course, or learn to meditate and they will emerge as more complex, integrated and capable persons. Unfortunately, they often learn the language of complexity, but don’t do the nuts and bolts learning, doing, and reflecting that’s required for robust learning.

In our assessments, I frequently see evidence of this problem. Check out this example: A successful manager should be able to apply transformational leadership and various frameworks to gather, interpret, weigh, balance, and align different individual and institutional perspectives and interests, as well as psychological and sociological aspects to allow for the emergence of the successful determination of an ideal resolution process for this cultural context. Such an approach would require emotional intelligence and dialectics to determine a set of guiding principles that will lead to an introspective and interactive process of finding a solution that is appreciative of the interdependence and significance of all perspectives.

True love, in this context, breeds chaos.

When college students fall in love with complexity and rapidly learn the vocabulary of complexity without developing a deep understanding of its meaning, they generally have a few years to recover before they enter the workplace. When working adults fall in love with complexity, it’s another story. Chaotic thinking interferes with the quality of workplace decisions.

If you are considering implementing or participating in a vertical development program, beware of programs that promise immediate transformative change*—these programs are more likely to provide the vocabulary of complexity than they are to support deep understanding. This kind of learning is superficial and potentially harmful—especially when it comes to decision-making.

My colleagues and I deal with the falling in love with complexity issue in two ways. First, we teach adults how to learn in a way that supports embodied, enduring, and useable knowledge rather than superficial knowledge. Second, when we measure the complexity of an individual’s thinking, we also independently evaluate quality of argumentation. This allows us to differentiate highly skilled complex thinkers from individuals who have learned the vocabulary of complexity without deep understanding. Then, we steer the latter group toward activities designed to build understanding by networking their knowledge more robustly.

*In adulthood, one full level of development takes about 8–10 years of dedicated reflective study and practice.

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