From Gabriel Weinberg’s book “Superthinking”
Decisions, Decision
“grass-is-greener mentality, causing you mentally to accentuate the positives (e.g., greener grass) and overlook the negatives.”
“One simple approach to improving the pro-con list is to add some numbers to it. Go through each of your pros and cons and put a score of −10 to 10 next to it, indicating how much that item is worth to you relative to the others (negatives for cons and positives for pros). When considering a new job, perhaps location is much more important to you than a salary adjustment? If so, location would get a higher score.”
“This method is a simple type of cost-benefit analysis, a natural extension of the pro-con list that works well as a drop-in replacement in many situations. This powerful mental model helps you more systematically and quantitatively analyze the benefits (pros) and costs (cons) across an array of options.”
“The first change when you get more sophisticated is that instead of putting relative scores next to each item (e.g., −10 to 10), you start by putting explicit dollar values next to them (e.g., −$100, +$5,000, etc.). Now when you add up the costs and benefits, you will end up with an estimate of that option’s worth to you in dollars.”
“One useful tactic is to talk to people who have made similar decisions and ask them to point out costs or benefits that you may have missed. For instance, by talking to other homeowners, you might learn about maintenance costs you didn’t fully consider (like how often things break, removing dead trees, etc.). Longtime homeowners can easily rattle off this hidden litany of costs (said with experience!). When writing down costs and benefits, you will find that some are intangible. Continuing the house example, when you buy a house, you might have some anxiety around keeping it up to date, and that anxiety can be an additional “cost.” Conversely, there may be intangible benefits to owning a home, such as not having to deal with a landlord. In a cost-benefit analysis, when faced with intangibles like these, you still want to assign dollar values to them, even if they are just rough estimates of how much they are worth to you. Doing so will help you create a fair quantitative comparison between the courses of action you are considering.”
“In other words, cost-benefit analysis is only as good as the numbers you put into it. In computer science, there is a model describing this phenomenon: garbage in, garbage out. If your estimates of costs and benefits are highly inaccurate, your timelines don’t line up, or your discount rate is poorly reasoned (garbage in), then your net result will be similarly flawed (garbage out).”
“decision tree. It’s a diagram that looks like a tree (drawn on its side), and helps you analyze decisions with uncertain outcomes.”
“You can now use your probability estimates to get an expected value for each contractor, by multiplying through each potential outcome’s probability with its cost, and then summing them all up. This resulting summed value is what you would expect to pay on average for each contractor, given all the potential outcomes.”
“To a wealthier person who associates a high opportunity cost with their time, all this extra anxiety and hassle may be valued at an extra $1,000 worth of cost, even if you aren’t paying that $1,000 directly to the contractor. Accounting for this possible extra burden would move up the two-week-late outcome from $2,500 (previously a $500 overrun) to $3,500 (now a $1,500 overrun).”
“Just as in cost-benefit analysis and scoring pro-con lists, we recommend using utility values whenever possible because they paint a fuller picture of your underlying preferences, and therefore should result in more satisfactory decisions. In fact, more broadly, there is a philosophy called utilitarianism that expresses the view that the most ethical decision is the one that creates the most utility for all involved.”
“One thing to watch out for in this type of analysis is the possibility of black swan events, which are extreme, consequential events (that end in things like financial ruin), but which have significantly higher probabilities than you might initially expect.”
“Black swan events, though, often come from fat-tailed distributions, which literally have fatter tails, meaning that events way out from the middle have a much higher probability when compared with a normal distribution.”
“A Monte Carlo simulation is actually many simulations run independently, with random initial conditions or other uses of random numbers within the simulation itself. By running a simulation of a system many times, you can begin to understand how probable different outcomes really are. Think of it as a dynamic sensitivity analysis.”
“Without such knowledge, you can get stuck chasing a local optimum solution, which is an admittedly good solution, but not the best one. If you can, you want to work toward that best solution, which would be the global optimum. Think of rolling hills: the top of a nice nearby hill would be a good success (local optimum), though in the distance there is a much bigger hill that would be a much better success (global optimum). You want to be on that bigger hill. But first you have to have a full view of the system to know the bigger hill exists.”
“Joseph Luft and Harrington Ingham originated the concept of unknown unknowns, which was made popular by former U.S. Secretary of Defense Donald Rumsfeld at a news briefing on February 12, 2002, with this exchange:
“scenario analysis (also known as scenario planning), which is a method for thinking about possible futures more deeply. It gets its name because it involves analyzing different scenarios that might unfold. That sounds simple enough, but it is deceptively complicated in practice. That’s because thinking up possible future scenarios is a really challenging exercise, and thinking through their likelihoods and consequences is even more so.”
“Another technique for thinking more broadly about possible future scenarios is the thought experiment, literally an experiment that occurs just in your thoughts, i.e., not in the physical world.”
“Thought experiments are particularly useful in scenario analysis. Posing questions that start with “What would happen if . . .” is a good practice in this way: What would happen if life expectancy jumped forty years? What would happen if a well-funded competitor copied our product? What would happen if I switched careers? These types of what-if questions can also be applied to the past, in what is called counterfactual thinking, which means thinking about the past by imagining that the past was different, counter to the facts of what actually occurred. You’ve probably seen this model in books and movies about scenarios such as what would have happened if Germany had won World War II”
“it is extremely difficult to perform scenario analysis alone. Seeking outside input produces better results, as different people with different perspectives bring new ideas to the table. It is therefore tempting to involve multiple people in brainstorming sessions from the get-go. However, studies show this is not the right approach because of groupthink, a bias that emerges because groups tend to think in harmony. Within group settings, members often strive for consensus, avoiding conflict, controversial issues, or even alternative solutions once it seems a solution is already favored by the group. The bandwagon effect describes the phenomenon whereby consensus can take hold quickly, as other group members “hop on the bandwagon” as an idea gains popularity. More generally, it describes people’s tendency to take social cues and follow the decisions of others. In this way, the probability of a person adopting an idea increases the more other people have already done so.”
There are many ways to manage groupthink, though, including setting a culture of questioning assumptions, making sure to evaluate all ideas critically, establishing a Devil’s advocate position (see Chapter 1), actively recruiting people with differing opinions, reducing leadership’s influence on group recommendations, and splitting the group into independent subgroups. It is this last recommendation that is particularly relevant for scenario analysis, as it forms the basis for divergent thinking, where you actively try to get thinking to diverge in order to discover multiple possible solutions, as opposed to convergent thinking, where you actively try to get thinking to converge on one solution.
“It is additionally likely that people close to you, such as those within your organization, share similar cultural traits, and therefore you should look beyond your normal contacts and venture outside your organization to get as much lateral and divergent thinking as you can. One way to do so is actively to seek out people from different backgrounds to participate. Another way, easily enabled by the internet, is to crowdsource ideas, where you seek (source) ideas quite literally from anyone who would like to participate (the crowd).”
“Surowiecki explains the key conditions in which you can expect good results from crowdsourcing: Diversity of opinion: Crowdsourcing works well when it draws on different people’s private information based on their individual knowledge and experiences. Independence: People need to be able to express their opinions without influence from others, avoiding groupthink. Aggregation: The entity doing the crowdsourcing needs to be able to combine the diverse opinions in such a way as to arrive at a collective decision.”
“One direct application of crowdsourcing to scenario analysis is the use of a prediction market, which is like a stock market for predictions. In a simple formulation of this concept, the price of each stock can range between $0 and $1 and represents the market’s current probability of an event taking place, such as whether a certain candidate will be elected. For example, a price of $0.59 would represent a 59 percent probability that the candidate would be elected.”
“In a book entitled Superforecasting, Tetlock examines characteristics that lead superforecasters to make such accurate predictions. As it happens, these are good characteristics to cultivate in general: Intelligence: Brainpower is crucial, especially the ability to enter a new domain and get up to speed quickly. Domain expertise: While you can learn about a particular domain on the fly, the more you learn about it, the more it helps. Practice: Good forecasting is apparently a skill you can hone and get better at over time. Working in teams: Groups of people can outperform individuals as long as they avoid groupthink. Open-mindedness: People who are willing to challenge their beliefs tend to make better predictions. Training in past probabilities: People who looked at probabilities of similar situations in the past were better able to assess the current probability, avoiding the base rate fallacy (see Chapter 5). Taking time: The more time people took to make the prediction, the better they did. Revising predictions: Forecasters who continually revised their predictions based on new information successfully avoided confirmation bias (see Chapter 1).”
“When you’ve arrived at a decision using one or more of these mental models, a good final step is to produce a business case, a document that outlines the reasoning behind your decision. This process is a form of arguing from first principles (see Chapter 1). You are laying out your premises (principles) and explaining how they add up to your conclusion (decision). You are making your case. Taking this explicit step will help you identify holes in your decision-making process. In addition, a business case provides a jumping-off point to discuss the decision with your colleagues.”