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Present times are uncertain times. COVID is still a mystery in the sense that the spread, treatment and prevention has not been fully comprehended. People are becoming jittery after long lockdown and economic meltdown. In several major cities of the world, people have taken to streets demanding end to lockdown. They carry placards saying, ‘Stop Corona Hoax’. It means the governments have lost confidence of people on this count. Governments are also tired of doling out relief packages and are mired in financial difficulties. On the business side, pricing and supply chain is not yet fully stable. To top it all, the weather is playing havoc in many countries of the world. China, US, India, Japan are seeing unprecedented weather conditions. Pakistan has also experienced record rains and is grappling with the aftereffects.
All this and more is making present times highly uncertain, fluid and fast shifting. We are used to making decisions on rational and logical grounds. We have lost those grounds and we are at a loss as to how to make decisions. We cannot avoid decision making however, not for today, and not for tomorrow.
Harvard Business Review published an article by Cheryl Strauss Einhom on 28 August titled, ‘How to make decisions in the face of uncertainty’. The link appears at the end of this article.
Cheryl recommends a four-step approach through the ambiguity to make careful, reasoned decisions. [Quote]
- Identify the category of historical date you are working with
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 has a frame that may impact how we interpret it; and patterned data, which appears to have a regular, intelligible, and meaningful form.
2. Recognize which cognitive biases are triggered by each category.
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 activate salience bias, in which we overweight 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 impacts how 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.
Recognizing how each of these categories triggers our biases can prevent us from falling prey to those biases.
3. Invert the problem to identify what you really need to know.
The third step in our process is to realize 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: So what? 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.
4. Formulate the right questions to get the answers you need.
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, which will help you counter preconceived assumptions and judgments. It will also give you a better context for interpreting the answers, because you’ll know the lens through which they are being filtered.
- 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 tackle what someone thinks about a topic, action, or event. They can get at people’s 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?
The four-step process helps us better address our emotional responses, name and confront them, and move forward with a rational decision. We’ll have a more complete picture, reducing the likelihood that we’ll rely upon well-worn thinking pathways and cognitive biases.
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. [Unquote]
Cheryl Strauss Einhorn is the creator of the AREA Method, a decision-making system for individuals, companies and nonprofits to solve complex problems, author of two books on complex problem-solving Problem Solved and Investing In Financial Research and an adjunct professor at Cornell Tech.