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How to Make Rational Decisions in the Face of Uncertainty

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David M. Brenner, ChFC®, CLU®

D. M. Brenner, Inc.
Phone : (858) 345-1001
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As we’re battling a virus that scientists still don’t fully understand, watching the stock market sink, then soar, then sink again, and facing a contentious election, the future seems completely unpredictable. When we feel heightened uncertainty, our decision-making processes can break down. We may become paralyzed and afraid to act, or act on the basis of bias, emotion and intuition instead of logic and facts.


Being aware of our uncertainty is a necessary precursor to managing it. Effective awareness means pausing, taking a strategic pause and assessing the situation and the unknowns. We’re currently being confronted with data that appears to be actionable — even though logically, we know it’s incomplete and probably volatile. But even when our knowledge is limited, we have tools to help us make decisions systematically and analytically. We can use a simple four-step process to work with and through ambiguity to make careful, reasoned decisions.


There are three main kinds of data we often confront and feel compelled to act on:

SALIENT DATA, which captures our attention because it is noteworthy or surprising;

CONTEXTUAL DATA, which arrives within a framework that may affect how we interpret it;

and PATTERNED DATA, which appears to have a regular, intelligible and meaningful form.


Different kinds of data trigger different biases, so identifying the data type and its related bias makes it easier to escape mental mistakes.

Salient data can lead to salience bias, in which we put too much weight on new or noteworthy information, resulting in suboptimal decision-making, planning errors and more. For example, airline passenger demand in April 2020 plunged 94.3% compared with April 2019, because of COVID-19-related travel restrictions. That shocking statistic might make us think that travel as we have come to know it is finished — but in reality, this one salient piece of data tells us almost nothing about future travel.

Contextual data can constrict our thinking and lead to a framing bias: The context in which we receive the data affects the way we think about it. For example, “80% lean ground beef” sounds more healthful than “beef with 20% fat.” But it’s the same beef, framed differently.

Patterned data often prompts the clustering illusion — also known in sports and gambling as the “hot hand fallacy” — whereby we assume that random events are information that will help us predict a future event. The human brain is wired to look for patterns, sometimes when they don’t exist. Equally important, when patterns do exist, they often don’t have predictive value.

But how do we move forward once we’ve accepted that we need additional information or insight to confidently make decisions about the future?


The third step in our process is realizing that you don’t need to know everything, but you do need to identify what matters most to your decision-making. To do that, invert your problem solving. Begin at the end, asking: What do I really need to know to understand the situation? What difference would this information make? And how do I expect to use it? The universe of “known unknowns” — those pieces of data that exist but are not in your possession — is endless. But you don’t need to explore them all; inversion can help you home in on those you deem to be critical to solving your specific problem with confidence.

For example, the salient data about diminishing airline demand triggers a visceral response, which can make it easier to conclude that the industry is permanently in dire straits. However, if we step back, we can recognize that there will continue to be an airline industry; that in the long term, people will want mobility, and the world’s economy will require it. This is a “known known.”

To solve a specific problem, you don’t need to probe all the unknowns. To stay with our air travel example, this is true whether you are deciding whether to get on an airplane or to invest in an airline. A traveler’s concerns would be whether and when there is a flight to the desired destination and whether it feels safe to take it, whereas an investor might focus on which airline is best positioned to survive the downturn.


Many of us have trouble crafting the questions that could help us make a decision. One useful and practical way to move forward is to organize your questions into four main categories: behavior, opinion, feeling and knowledge. This ensures that you’ll bring both distance and a variety of perspectives to the way you probe your data, helping you counter preconceived assumptions and judgments.

BEHAVIOR questions address what someone does or has done and will yield descriptions of actual experiences, activities and actions. If you’re assessing the state of the airline industry, you might ask: Who is still traveling? Does that extrapolate to a larger cohort?

OPINION questions encompass what someone thinks about a topic, action or event. They can address goals, intentions, desires and values. In the airline example, you might ask: Is it currently safe to travel? Are the airlines taking proper precautions?

FEELING questions ask how someone responds emotionally to a topic. They can help you get beyond factual information to learn what people may be inclined to do regardless of the data. Here, you might ask: How safe do travelers feel? How safe do airline employees feel?

KNOWLEDGE questions explore what factual information the respondent has about your topic. While some may argue that all knowledge is a set of beliefs, knowledge questions assess what the person being questioned considers to be factual. You might ask: What routes have been paused or cut? How many more will be cut? Have there been COVID-19 transmission cases linked to flying?

Uncertainty is a mixture of actions and reactions, knowledge and emotion. Classifying and addressing the ingredients in the uncertainty mixture won’t gain us certainty, but we can be sure that our questions address all areas of uncertainty. The four-step process helps us better address our emotional responses, name and confront them and move forward with a rational decision.

Voltaire once famously recommended that we judge a man by his questions rather than his answers. We’ll never know the future, but by examining our data and our thinking we can develop and ask great questions that will allow us to more confidently make decisions amid uncertainty.

c.2022 Harvard Business Review. Distributed by The New York Times Licensing Group.

This HBR article was legally licensed through AdvisorStream.

David M. Brenner profile photo

David M. Brenner, ChFC®, CLU®

D. M. Brenner, Inc.
Phone : (858) 345-1001
Schedule a Meeting