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February 25 - February 27, 2019
The AREA process gets its name from the perspectives that it addresses: Absolute, Relative, Exploration and Exploitation, and Analysis. The first “A” stands for “Absolute,” which refers to primary, uninfluenced information from the sources at the center of your research and decision-making process. The “R” stands for “Relative,” and refers to the perspectives of outsiders around your research subject. It is secondary information, or information that has been filtered through sources connected to your subject. The “E” stands for “Exploration” and “Exploitation,” and they are the twin engines of
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The AREA Method allows you to become your own expert in the ecosystem related to the decision you need to make. In that sense, it’s more than a process that you apply—it’s a muscle that you build and it can become second nature; it can be part of the frame you bring to the world.
Critical Concepts (CCs) are the one, two, or three things that really matter to you. They answer the question “What am I really solving for?” There is no single answer. Critical Concepts are going to vary from person to person. Different decision-makers will have different time horizons in which to make their decisions, different personalities, and different goals. Two people looking at the same data may have different CCs and may make entirely different decisions.
Projection Bias, where we assume that others believe as we do, and Authority Bias, where we defer to authority figures, and they overwhelmed the experts’ ability to see disconfirming data.
The planning fallacy is our tendency to underestimate the time, costs, and risks of completing a task, even though we’ve previously experienced similar tasks.
The confirmation bias refers to a form of selective thinking in which we seek out and overvalue information that confirms our existing beliefs, while neglecting or undervaluing information that is contradictory to our existing beliefs.
Optimism bias This is a bias in which someone’s subjective confidence in their judgments, or in the judgments of others, is reliably greater than their objective accuracy.
Without meaning to, we tend to project our thoughts and beliefs on to others and assume that they are wired the same way we are. This can lead to “false consensus bias,” which not only assumes that other people think like we do, but that they reach the same conclusions that we have reached.
Salience bias refers to the tendency to overestimate evidence that is recent or vivid.
We prefer stories—narratives—to data. Narratives are crucial to how we make sense of reality. They help us to explain, understand, and interpret the world around us. They also give us a frame of reference we can use to remember the concepts we take them to represent. However, our inherent preference of narrative over data often limits our understanding of complicated situations. For example, a good story can sell almost anything.
Loss aversion Empirical estimates find that losses are felt almost two-and-a-half times as strongly as gains.
The relativity bias inhibits our ability to objectively assess information based upon an over-dependence on comparisons. For example, when given a choice, we tend to pick the middle option: not too pricey, not too cheap.
Authority bias This bias refers to our natural inclination to follow and to believe in authority figures.
Liking bias If you like someone or something, you will interpret data in their favor.
We tend to covet things we believe are scarce, sometimes irrationally. For example, during the real estate bubble, investors became concerned that only a limited amount of land was available to be developed with no real evidence that this was the case.
If one bias is present, you might be able to recognize it, but if several biases are at work at the same time, it is much harder. Berkshire Hathaway’s Munger calls this the “Lollapalooza effect.”
Recognizing where things may be working for you (where you have an edge) and where you may be at a disadvantage (facing a potential pitfall) can make the process more effective and efficient for you, even before you start. That way, you may think about how to counter your weaknesses and capitalize on your strengths. To do that, consider the four ways you can frame your research. In so doing, you are, of course, making an assumption about how easy or difficult parts of the process may be. These four frames are Behavioral, Informational, Analytical, and/or Structural. Together they spell out
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I will emphasize again: Open-ended research is not terribly productive. It’s not only time consuming, but can feel overwhelming and ultimately counterproductive. The AREA process recognizes this and asks you to carefully determine your CCs. What matters to you? Use the answer to shape and frame your research process. It will make a difference between a frustrating, unhelpful process and a life changing affirmation that comes from a decision well made.
The first “A” in AREA stands for Absolute information, meaning information that represents the perspective closest to the entity or entities at the core of your decision. It is primary source information, unfiltered by outside, secondary sources.
Reading and analyzing the numbers first keeps you from assuming that whatever your Absolute target says is backed up by its data. I have often found that a careful reading of the data opens up interpretations not included in the target’s written analysis. You’ll be better able to think about what your target says if you have first come to some conviction yourself.
To recap, the purpose of limiting your initial focus to the numbers is fourfold. First, it enables you to see your Absolute target as objectively and as uninfluenced as possible. Second, it recognizes that organizations, or entities, present data on what they consider to be their main issues. Reading the numbers first quickly highlights what the entity considers most critical beyond what it is obligated to disclose. Third, it enables you to consider whether the data is relevant and clear. For example, if a charity says it provides 100 meals, that doesn’t tell you how many people queued up for
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Might your target have an incentive to show a result that might make its information unreliable?
The goal here in Relative is to widen your lens as you pull back from your Absolute research target. You’re setting your target into a broader context of its industry’s or field’s dynamics. The Relative research map feeds the questions you ask of your literature review, which will, in turn, feed the sources for your Exploration work.
In addition to considering your questions, I also recommend that you consider the framework for your interviews. There are several kinds of successful interview approaches, but each person ultimately must find a style that feels comfortable to him or her.
The Scientist. Here you are value-free, focused singlemindedly on collecting technically sound data. You are an objective scientist on a truth-seeking mission. I usually use this approach with early-stage low-level interviews in which I ask the interviewees to explain how and/or why something is done.
The Consultant. Here you are consensus-building and openly clarifying information. In this approach, you have already collected a lot of data and are partnering with the interviewee in the search for useful information. I’ve used this approach with mid-level interviews as a way to share and to check the quality of the information amassed.
The Interrogator. This is a probing, and at times aggressive, style. While thankfully many high-stakes decisions are not adversarial ones, there are times when you are trying to ferret out information by asking some tough questions. If you are the principal at a middle school and there’s been a food fight in the cafeteria, this may be the style you need. But for most of...
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People like to be noticed. If you can notice your interviewee’s accomplishments, or show him you’ve taken the time to learn something about him before calling, he will likely be flattered.
Dating back to the 17th century, the scientific method teaches scientists to prove that they can’t reject their hypotheses rather than trying to prove that they can confirm them. It has four simple steps: 1. Define a problem. 2. Formulate an educated guess or hypothesis. 3. Design an experiment to test the hypothesis. 4. Analyze the results.
Approximately three-fourths of our brain’s sensory resources are dedicated to vision. That leaves only one-fourth for all the other senses combined. According to Dual Coding Theory, a memory theory that explains the impact of imagery on the brain, we process verbal and visual information with different parts of the brain. In other words, when we work only with words, we’re not even using a quarter of our brain.
Scenario Analysis is another decision-making exercise to consider possible outcomes and their implications after a given period of time. It is a risk-assessment technique that does not try to show one exact picture of the future, but instead consciously presents several alternative future developments or key factors that would affect a decision, such as the previous estimate for patient visits per year. It is not predictive, but meant to manage uncertainty.
This exercise asks you to create several scenarios to show possible future outcomes, in particular a combination of an optimistic, a pessimistic, and a most-likely scenario. More scenarios are not necessarily better, as they can make the analysis unclear. The goal is to visualize and explore different ways forward.
The Rule of Three: Does your data come from at least three different sources? By collecting data from three unrelated sources, you can be more certain that your information is valid.
Base Rates: We all tend to ignore base rates, which are the underlying percentages or the actual likelihood of an event occurring. This tendency can lead to poor decision-making.
Data Fishing: The findings from one data set don’t automatically apply to another data set. Although it’s useful to identify patterns and relationships between data, be careful to avoid data fishing, or taking more information from a data set than it actually contains.
Comparing Apples to Apples: Review how the data is selected. Make sure you understand how a study was designed and conducted.
A pre-mortem, by contrast, is implemented before a decision rather than after it, so that the decision-making can be improved rather than autopsied. Unlike a typical critiquing session, in which project team members are asked what might go wrong, the pre-mortem operates on the assumption that the “patient” has died, and so asks what did go wrong. The task is to generate plausible reasons for your research target’s failure. According to researchers from the Wharton School, Cornell University, and the University of Colorado, prospective hindsight—imagining that an event has already occurred—is
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