We want to get good at selecting our passengers. A clear working memory helps us make better decisions.

Shane Parrish, an expert in decision-making, stresses the importance of avoiding decisions in a “suboptimal state”. He advises: don't decide when you're hungry, angry, lonely, or tired—summed up by the acronym "H.A.L.T." (This concept is borrowed from Alcoholics Anonymous.)

A taxed working memory deserves a spot on the list alongside hungry, angry, lonely, or tired.1

Ranking tasks, like with the Eisenhower Matrix, can be tricky because stuff that stays in our mind may not always seem significant to us, while what we consider trivial can linger, taking up space. Often, it's uncertainty—whether emotional or about how to tackle something—that keeps these tasks stuck in our minds.

Becoming skilled at handling uncertainty is the skill we need, as our brains tend to hold onto uncertain work. So, keeping our working memory free serves as a proxy for our ability to handle uncertainty quickly.

Two principles:

1. Uncertain things like to hang out in working memory. (Emotional and vague things do too.)

2. Things without a plan like to hang out in working memory.

The goal is to quickly determine what to do with something within a few minutes.

We need to place these items into an external system, such as a calendar, notes/tasks app, or a kanban board. David Allen, the author of "Getting Things Done," says it this way: "The brain is for having ideas, not for holding them."

Strategies like using pre-decisions2. flipping a coin, delegating, or heuristics like the "2-minute rule" (doing something immediately if it’s quick to do) can help.

Here are three simple but effective ways to quickly move things from your mind to your calendar.

1. Schedule a half-hour this week to think it over.

2. Work on it (even if naively) instead of overthinking. Set a fixed time to work, as this can help clear things up. Stop when the time is up.

3. Schedule time to discuss it with another person.

These strategies may seem basic, but they are often a solid first step for many problems. You are taking time to understand and define the problem.

A great example is in book I’m almost finished reading, In "Talking to Strangers," Malcolm Gladwell.

Gladwell discusses research showing that an algorithm outperforms judges in deciding who should be released on bail. The algorithm, analyzing historical data, more accurately predicted who would reoffend or fail to appear in court. Unlike human judges, the algorithm's decisions were consistent and free from biases and noise, significantly reducing crime rates if implemented.

Judges, like most of us, think that by seeing the person and getting a feel for them, is important to judging what should be done. But that inclination is what creates more mistakes.

But then, here is the question: if we should reach for an algorithm to make decisions, where do we get these algorithms?

Here is the insight.

Though an algorithm seems like it only meaningfully exists in the world of math and computers, we can think a little less strictly about them.

A step-by-step process reduces noise and can be considered an algorithm.2

As a result, this allows us to recognize numerous opportunities to find useful algorithms from insights and guidance in books ranging from self-help to strategy.

References

Gladwell, Malcolm. Talking to Strangers. Penguin Books, 2020.

We settled on one person, and we thought it might be him with 55% confidence. Then, in discussing the likelihood, we eventually bumped our confidence to 94%. For example, after realizing our friend was a workaholic and wouldn’t have time for dating new people outside of work, we concluded it had to be someone at the same company. That led us to increase our confidence from 55% to 80%. Then, a few other facts, like seeing them talking once in the hallway, nudged it up higher and higher.

Were we correct? YES. Eventually, over dinner, we told our friend that we had a guess about who it was.

"Okay? Who do you think?" our friend said.

"We think it is John [a pseudonym]," we said, giving our 94% prediction.

"You're right," our friend said.

What we did well was continuously update our estimates intuitively based on small pieces of information and insights.

The more mathematically sound approach is to use the Bayesian belief-updating equation. “In simple terms, the theorem says that your new beliefs should depend on two things: your prior belief (and all the knowledge that informed it) multiplied by the "diagnostic value" of the new information.”1

But starting with a simple approach can build confidence and habits.

Also, the simpler approach is not unreasonable. Individuals who consistently make highly accurate predictions about future events (e.g., superforecasters) rarely explicitly use Bayesian updating.

The superforecasters are a numerate bunch: many know about Bayes' theorem and could deploy it if they felt it was worth the trouble. But they rarely crunch the numbers so explicitly. What matters far more to the superforecasters than Bayes' theorem is Bayes' core insight of gradually getting closer to the truth by constantly updating in proportion to the weight of the evidence.2

References

Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The art and science of prediction. Broadway Books.

I started doing this after reading in 2016 the incredible book "Superforecasting" by Phil E. Tetlock and Dan Gardner.

The many words and phrases we use around probabilities, such as doubtful, slim chance, rarely, or very likely, are interpreted differently by people. Drastically differently.

Sherman Kent, a legend in intelligence circles, figured this out when a team had agreed to use "serious possibility" when estimating if the Soviet Union would invade Yugoslavia in 1951. It turned out that one analyst thought this meant the odds were 80 to 20, while another thought the odds were 20 to 80—complete opposite interpretations.

"Serious possibility" wasn’t clear.

“A phrase that looked informative was so vague as to be almost useless. Or perhaps it was worse than useless, as it had created dangerous misunderstandings.” Page 55

Here is an example from a New York Times article (capitalization mine).

The origin of the Covid virus remains the pandemic's biggest mystery. Did the virus jump to human beings from animals being sold at a food market in Wuhan, China? Or did the virus leak from a laboratory in Wuhan?

U.S. officials remain divided. The F.B.I. and the Department of Energy each concluded that a lab leak was the MORE LIKELY cause. The National Intelligence Council and some other agencies believe that animal-to-human transmission is MORE LIKELY. The C.I.A. has not taken a position.

The question remains important partly because it can inform the strategies to reduce the chances of another horrific pandemic.

The agencies, hopefully, have specific estimates. However, how journalists communicate this can be interpreted very differently by the public. What does “more likely” mean? Is it 51 percent, 20 percent, or 90 percent? It can mean all those things to different people.

The takeaway is to get yourself in the habit of thinking and communicating to others in numbers—using estimates to escape the vagueness of words.

References

New York Times Editorial Board. (2024, June 3). Opinion: The Covid lab leak theory. The New York Times. https://www.nytimes.com/interactive/2024/06/03/opinion/covid-lab-leak.html

Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The art and science of prediction. Broadway Books.

By paying attention to Klein, I learned about the premortem method that he and his team developed, building on others' research on "prospective hindsight," which involves imagining that an event has already occurred.

In a premortem, you imagine that your goal or project has gone up in flames and work to identify potential causes.

Here is a long quote from the 2007 Harvard Business Review (HBR) article by Klein.

“A premortem is the hypothetical opposite of a postmortem. A postmortem in a medical setting allows health professionals and the family to learn what caused a patient’s death. Everyone benefits except, of course, the patient. A premortem in a business setting comes at the beginning of a project rather than the end, so that the project can be improved rather than autopsied. Unlike a typical critiquing session, in which project team members are asked what might  go wrong, the premortem operates on the assumption that the “patient” has died, and so asks what did go wrong. The team members’ task is to generate plausible reasons for the project’s failure.”

For a long time, I referred people to the 2007 HBR article. While researching this post, I found a couple of more recent articles written by Klein himself: one for McKinsey in 2019 and another for Psychology Today in 2021. Each is short and useful to read. (See references at the end.)

Premortems, in my experience, are extraordinarily fun and informative. They are one of those focused types of meetings that deliver results. It is particularly satisfying because, in critical ways, it helps to grease the wheels of the project.

1. Incentivizes the identification of problems and rewards foresight. "Team members compete to see who can raise the most worrisome issues."

2. Forges a culture of candor, helping cross-functional teams to communicate directly about problems.

3. Sensitizes the team to pick up early signs of trouble

4. Develops actions and strategies to anticipated challenges.

5. Helps develop leading indicators on if the project is on/off track and kill criteria for when the project should be abandoned.

Premortems capture knowledge beforehand, leading to less biased post-assessments.

In other words, premortems make it possible to pass on accurate lessons for future projects.

References

Klein, Gary. (2007, September). Performing a Project Premortem. Harvard Business Review. https://hbr.org/2007/09/performing-a-project-premortem

Klein, Gary. (2019, April 3). Bias Busters: Premortems: Being smart at the start. McKinsey Quarterly.https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/bias-busters-premortems-being-smart-at-the-start

Klein, Gary. (2021, January). The Pre-Mortem Method: A practical way to do risk assessment. Psychology Today. https://www.psychologytoday.com/us/blog/seeing-what-others-dont/202101/the-pre-mortem-method

“Decisions are different from choices. If you casually select an option from a range of alternatives, you’ve made a choice. If you react without thinking, you’ve made an unconscious choice. But neither of these is the same as a decision. A decision is a choice that involves conscious thought”.1

This distinction between the two words is important, a definitional difference worth remembering.

Our quick, intuitive views hinder clearer thinking because they provide ready-made answers to problems, as pointed out by Daniel Kahneman, the Nobel Prize-winning economist and psychologist.2

So, ready-made answers are ready-made CHOICES. And we don’t want choices. What we want are ready-made DECISIONS. This is where the use of pre-decisions and rules comes in.

Pre-decisions are proactive choices made to guide future behavior in moments where we might make a poor choice. Rules are the same, but have the benefit that “people typically don’t argue with your personal rules. They just accept them as features of who you are. People question decisions, but they respect rules'".3 So, a rule is a like how to communicate your decision to another person.

So, the skill, then, is to anticipate future events that could benefit from some pre-decisions or rules. When those future events come, we’ll be able to decide quickly and well.

Try it 8 times by a deadline. This strategy might not seem fast, but the motivation and information that come from quick action are often faster than pondering the ins and outs for a couple of weeks.

Eight is a good number because it naturally breaks down into a manageable schedule: 1x per week for two months, 2x per week for a month, or every day for a week with a bonus day to pull it all together.

A deadline is important to maintain continuity. Without it, knowledge will fade, and each attempt will feel like the first time.

For example, this is my 2nd post on Substack. I’m still unsure if writing a weekly blog is a good idea, but I don’t want to think about it too much. So, I’ve committed to doing at least eight posts (1x per week for two months) to decide if I want to keep with it.

Also, I used the 8x approach when seeing if I wanted to swim four years ago. As a result, swimming became a central part of my week and life.

Learning by doing is an effective method for gathering information and understanding emotions related to the decision. Because we’ll more naturally talk about what we are up to, it encourages gathering info as we talk to people. (“Oh, my sister did something like that a few years ago. She said what was important…”.) After several iterations, unknowns and issues often resolve, and repeated actions reduce our biases, concerns, fears, and hesitations.

Basically, this approach helps us decide by taking action and learning our preferences by experience instead of thinking.

1. Understanding expertise is important, as experts serve as guides in our interconnected and complex world

2. Recognize the value of actively open-minded thinking in fostering genuine expertise

In our complex world, we depend heavily on experts. A useful way to classify experts is by determining whether their judgments are verifiable or not.

For instance, consider a weather forecaster who predicts that Sunday will be sunny, but the day ends up being cloudy and rainy. This forecaster's predictions are verifiable because we can check if the forecast was correct. In contrast, many important areas involve judgments that are not easily verifiable and are often a matter of interpretation. These experts have been termed respect-experts (Kahneman, Simony, & Sunstein, 2021, Chapter 18).

A financial analyst, for example, isn't a 'respect expert' because their predictions about market trends can be measured against actual market performance; their judgments are verifiable. Fields like diplomacy and philosophy, however, involve respect-experts since their guidance often relies on subjective interpretation. This also applies to underwriting, criminal sentencing, wine tasting, essay grading, and book and movie reviewing

Understanding how to evaluate experts and their knowledge claims is crucial. Respect-experts typically possess traits like raw intelligence and the ability to explain their judgments with clarity and confidence. My favorite trait is being actively open-minded, which means seeking out and being open to new information that could change their minds.

Being actively open-minded isn't just about being right; it's about working towards and improving the likelihood of being right. This mindset is why experts generally show less bias and less noise in their judgments.

Actively open-mindedness is my favorite trait because it's accessible to everyone. It's a foundational characteristic that is easier to develop if you want to establish expertise.

We should be wary of so-called experts who can't change their views or acknowledge others' opinions. Knowledge can become outdated or overly simplistic, and inflexible experts may fail to adapt their thinking to new contexts, reducing their usefulness. Therefore, it's important to seek out experts who continually gain insight and refine their understanding by embracing new information and learning from mistakes.

Reference

Kahneman, D., Simony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgement. Chapter 18.