If you already knew the ultimate outcome of a decision, the choice would be much simpler. However, decision-making is often challenging due to imperfect information. For example, when considering a job change, there are many options to contemplate, such as:
- Finding a job similar to your current one.
- Seeking a job with opportunities for advancement.
- Staying in your current job and waiting for a promotion.
- Leaving your current job to retrain and completely change your career path.
As it's evident, when we deeply consider the future, we discover that the choices are endless. What can you do in such situations? You can try these four methods:
Pros and Cons List:
The preferred thinking framework is the "pros and cons list," where you list the potential "benefits and drawbacks" of making a decision and then balance them. This method works in some straightforward situations but has several drawbacks.
For instance, how do you know how many options should be on the list? What if the pros and cons don't carry equal weight? How do you handle the interplay between different factors?
Therefore, it can lead to the "grass-is-greener mentality," where we psychologically emphasize the positive factors (e.g., greener grass) and overlook the negative ones.
A finance student who graduates might embark on a career in venture capital. If you make a pros and cons list, you might find numerous advantages (opportunity to work with founders at various levels, change the world, get high investment returns, join innovative companies with high leverage).
However, assuming you have an introverted personality, many of these options may not be favorable. You may have to engage in various social activities, shoulder psychological burdens that others don't, and perhaps, most of your time might be spent on struggling companies once you enter a particular field.
While a career in venture capital can be excellent, it doesn't necessarily mean "it's the right fit for you." It's only with time and experience that your choices and career path become clearer.
You've probably heard the saying, "If all you have is a hammer, everything looks like a nail," which is what this means. The "pros and cons list" is like a hammer in your decision-making toolkit; it works in some situations, but for highly complex, high-stakes matters, you need other methods.
In the list you've outlined, assign some numerical values to the options to represent their value in your mind (positive values for benefits, negative values for drawbacks).
When changing jobs, it involves less work (+5 points), but it's far from home (-3 points), and it pays well (+10 points). By doing this, each option no longer carries equal weight, and if multiple options are related, you can combine them for a score.
Doesn't this seem more manageable?
Simply summing up the pros and cons of each option, based on the final score, can enhance decision efficiency; this is a simple cost-benefit analysis.
But what about when things get even more complicated?
You can convert the corresponding scores into monetary values (e.g., -100 dollars, +500 dollars). This way, by adding costs and benefits together, you can estimate the value of a particular option.
When considering buying a house, write down the money you have to pay (down payment, transfer fees), future payments (mortgage, property taxes, maintenance fees, property transfer taxes, etc.), and the returns you hope to get when selling it in the future. Then, by adding these together, you can estimate your long-term gains or losses.
However, creating a pros and cons list that includes intangible costs and benefits may seem a bit tricky, and it's not something you'd typically plan out in daily life. What can you do?
You can employ "endgame thinking" and use the present value (PV) method with a discount rate (PV = present value, C = future amount, r = discount rate, t = investment periods) to measure it.
The challenge of the pros and cons analysis is ultimately sensitive to the discount rate.
This is a practical approach; by putting the costs of your investments and the discount rate together, and then calculating over time, you can see if there's potential for appreciation.
In a sentimental analysis: Spending two years at a small company, how much money can you make in two years? Is there room for growth and promotion? After two years, do you have an opportunity to move to a larger platform, or are you facing the risk of being unemployed due to age?
By evaluating the overall picture, you can roughly estimate whether your value will increase or decrease after two years. However, your perception of the increase or decrease will change based on your "initial goal." Suppose you had already planned to start your own business after two years. In that case, perhaps working at a smaller company offers better opportunities for self-improvement.
In a rational analysis: Take a 50,000-dollar bond as an example. By changing the discount rate, you can calculate the net income. This analysis method helps us use endgame thinking to estimate where the net income falls within a reasonable depreciation rate.
Please note that the usefulness of cost-benefit analysis (pros and cons list) depends on the numbers you input. If you can't convert all the options into numbers or compare them with a discount rate, you can't make a probabilistic assessment.
In other words, you should identify the critical elements that affect the options in your internal struggle, convert them into scores, and if scores can't substitute for numbers, use cash values. Then, based on a goal set two or five years in the future, perform a discount rate analysis.
Cost-benefit analysis (pros and cons list) is a top-notch reference model to help you make decisions.
Decision Trees for Probability
Ideals are full, but reality is lean. In most cases, your options, along with related costs and benefits, are not very clear, and sometimes the potential outcomes are too uncertain for you to figure out the key elements from the start.
"I hope to have the pool facilities ready before the summer swimming season begins, and I have two contractors providing quotes. The first one is from the pool maintenance team I've used before, and their quote is as high as $30,000.
The second one, with a lower quote of only $20,000, is from a team consisting of just one person. I have no prior experience with this individual, and he seems a bit unscrupulous.
I believe there is only a 50% chance that he will complete the contract on time (within 1 week) as per the quote. If it's not completed, there's a 25% chance of a one-week delay with an additional $300 labor cost, a 20% chance of a two-week delay with an additional $600 labor cost, and a 5% chance of a delay of more than three weeks, requiring rework with a total additional labor cost of $1,000.
What should I do?
I can use a decision tree to assess this situation. It's a diagram that looks like an inverted tree and helps analyze uncertain decisions. Usually, the branches represent decision points, and the leaves represent different possible outcomes.
Now, by adding up the probabilities and monetary values for each potential outcome, I can calculate the expected value for each contractor. Based on the expected outcomes, I can determine the price I should pay to each contractor.
In any case, by starting with a decision tree and the final expected values, even if there are many potential issues, we can make a rational decision considering both time and money.
Decision trees incorporate these additional values effectively, "pricing in" extra costs because these values include not just the money you have to pay but also the psychological effects, so we call them "utility values."
Utility may diverge from the actual price.
Even if two things have the same price, you might still perceive one as more valuable than the other, like preferring to see a concert of your favorite band over another band at the same price.
In fact, utility reflects a utilitarian philosophical thought. Its viewpoint is that the decision that brings the greatest utility to all relevant parties is the most ethical.
However, as a philosophical thought, it has its drawbacks.
First, when it comes to multi-party decision-making, even though overall benefit is enhanced, the distribution of benefits among all parties may be unequal, making this decision seem unfair, just as an increase in living standards doesn't necessarily mean income equality.
Second, utility values are hard to estimate. If only the decision that maximizes overall benefit is considered, utilitarianism is a very useful philosophical model. Regardless, in situations with multiple possible probabilistic outcomes, a decision tree can help you figure out what to do.
Consider insurance, for example:
Should you choose insurance with low premiums and a high deductible or insurance with high premiums and a low deductible? It depends on the expected level of medical expenses and whether you can afford a high deductible in the unlikely event of a significant accident.
In other words, if you believe you'll stay healthy and unlikely to incur significant expenses in the future, lower premiums make more sense; if you anticipate potential major issues in the future, the latter option does.
Indeed, when considering unlikely but severe consequences, decision trees are especially useful. Therefore, the actual cost of such an event is much higher than the deductible cost. Additionally, in such analyses, be mindful of small probability "black swan events."
A black swan refers to extreme events with significant consequences (e.g., causing enormous economic losses), but the probability of its occurrence is much higher than initially expected.
Known vs. Unknown
For unknowns, we can start from a simple 2x2 matrix, envisioning these four categories of things known or unknown. This concept was introduced in 1955 by psychologists Joseph Luft and Harrington Ingham.
This model is particularly useful when you're thinking more systematically about risks (e.g., risks a project might face).
Something might be a risk to others, but not to you because you know how to deal with it based on past experience. For example, a project might require a technical solution, but you already know what the solution is and how to implement it; it's just a matter of execution.
The project has known risks, but due to uncertainty, it's not clear how to resolve them at the moment. Think of third-party risks, where you only find out the reactions once you're involved.
There are risks you haven't considered, but there are clear ways to address them. For example, your project might need to be delayed until April, but you didn't know that another company is having its annual conference abroad in April.
Some risks aren't obvious and require collective efforts to discover. Changes in the organization or industry (budget cuts, company acquisitions, launching new products) can significantly alter the project. Even if you recognize the "unknown unknown" (turn it into a "known unknown"), the likelihood of its occurrence and consequences remain uncertain.
As you can see, start by listing things in these four categories and then strive to make them "known knowns." This model focuses on gaining a comprehensive understanding of a situation, akin to systems thinking.
Take childbirth, for example:
By reading books, you know that the first few weeks after a child is born can be challenging. You need to take time off, buy a car seat, crib, diapers, and so on (these are known knowns).
You also know that a child's eating and sleeping (or not sleeping) might be an issue, but you can't predict their preferences until they arrive (known unknowns).
You might not know about "swaddling" the baby, but nurses or family members will quickly tell you, turning this "unknown known" into a "known known"; plus, there are things you never thought of, like whether your child will have a learning disability, and so on (unknown unknowns).
What to do about "unknown unknowns"?
There's a related model that offers multiple perspectives, known as scenario analysis or scenario planning, which is especially useful when you're thinking more systematically about the future.
Multiple Perspectives Scenario Analysis
It's named after analyzing different scenarios that could occur. It may sound simple, but it's actually quite complex because considering potential future scenarios is challenging, let alone comprehensively thinking about their likelihood and consequences.
So, how do you do it?
Typically, for better scenario analysis, we must imagine plausible but different futures and ultimately come up with several possible scenarios.
This process can be very challenging because you might be inclined to latch onto the first thought that pops into your head. But often, this is a direct inference based on the current situation and doesn't challenge your underlying assumptions.
To challenge your underlying assumptions, there's a trick: list major events that could happen and speculate on their potential impacts. Some events might have no impact, but some could form the basis for scenarios you should think deeply about.
The second technique for envisioning future scenarios is called a thought experiment, and it's an experiment conducted in your mind only; it doesn't happen in the real world.
The most famous thought experiment is "Schrödinger's cat."
In simple terms, imagine you have a box with a cat inside. If a radioactive atom decays within the past hour, the cat will be killed by a poison.
This thought experiment raises some seemingly unanswerable questions: Before you open the box to observe the cat, is it dead or alive, or is it, as some interpretations of quantum mechanics suggest, in a state between life and death?
How is the experiment conducted?
You can pose "what if" questions, such as, what if life expectancy increased by 40 years? What if a well-funded competitor plagiarized our product? What if I chose to change careers?
You can also apply "what if" questions to events in the past. This is known as counterfactual thinking, where you imagine the opposite of what actually happened.
What if I had accepted that job offer? What if I had attended a different school? What if I hadn't taken that part-time job?
However, the key is that when reconsidering your past decisions, you should not only consider the positive outcomes that might have resulted from making different choices but also consider the ripple effects. The butterfly effect reminds us that small changes can lead to chain reactions.
Posing "what if" questions can facilitate creative thinking. In a broader sense, this relates to lateral thinking, one of many techniques associated with thinking outside the box. Another practical lateral thinking technique involves injecting randomness into idea generation. This type of thinking helps you jump from one idea to another, in contrast to critical thinking, which primarily evaluates the ideas in front of you.
You can randomly select an object from your surroundings and try to connect it in some way to your current thoughts, which can stimulate new ideas in the process.
However, no matter which technique you use, conducting scenario analysis alone can be challenging. Seeking external input can lead to better results. However, group dynamics can introduce cognitive biases or the "herd effect." So, what should you do?
Strive to ensure that all viewpoints are "evidence-based."
Actively engage in divergent thinking to identify multiple solutions.
It's worth noting that this is the opposite of convergent thinking, which focuses on trying to bring thoughts together into a single solution.
Convene a meeting with your team, but instead of brainstorming, simply review the goals of scenario analysis and then disband.
Furthermore, the people around you may share similar traits. To obtain as much lateral and divergent thinking as possible, consider stepping outside your usual social circles.
One method is actively seeking individuals from diverse cultural backgrounds, a practice known as crowdsourcing, which involves seeking advice from anyone willing to participate. This can be easily accomplished through the internet.
Crowdsourcing is highly valuable in many situations, from gathering news leads to adding entries to Wikipedia, to addressing real-world problems faced by businesses and governments.
In 2009, during a competition hosted by the online streaming service Netflix, crowdsourced researchers defeated Netflix's own recommendation algorithm.
Certainly, utilizing crowd wisdom makes sense when the collective knowledge surpasses what you originally possess. It can help you make wiser decisions than relying on your own judgment alone. The "crowd" can assist you in systematic thinking and gaining new data and insights in various situations.
Another approach is seeking advice from those who are "superforecasters," although this might be challenging.
However, Philip E. Tetlock, the author of "Superforecasting," conducted research involving thousands of participants and found that individuals who can make accurate predictions typically possess these traits:
Firstly, intellectual prowess is crucial, with deep expertise in vertical domains and a willingness to continually adapt their abilities over time. They tend to challenge their own ideas while also being open to independent thinking.
Secondly, they can retrospectively examine the probability of similar events occurring in the past and assess the likelihood of current events, avoiding the base rate fallacy. Ultimately, they are willing to invest time in continuously adjusting their assessments based on new information, avoiding confirmation bias.
Finding such individuals might be challenging. Consider someone who can predict market trends with significant experience and agile thinking.
Evaluate the "critical factors" in decision options and rate them. For basic issues, use a pros and cons list. For complex issues, consider cost-benefit analysis and discount rates.
Decision trees can help you calculate probabilities for all options.
A 2x2 matrix can help you discover "unknown unknowns."
For unknown unknowns, conduct scenario analysis, beware of anchoring, use "what if" thinking, and then apply convergent thinking to gather opinions. Beware of surrounding yourself with people from the same cultural background. You can use crowdsourcing or seek advice from those who can make accurate predictions.
All decision problems are ultimately probabilistic. If you can break down a matter around your envisioned goal, analyze it step by step, I believe your luck in taking action won't be too bad.