From Raiffa’s book “Smart Choices”
Identify the key uncertainties.
- “Virtually any decision involves uncertainties, but most uncertainties don’t influence consequences enough to matter. Selecting the uncertainties important enough to include in a risk profile requires just two steps:
- “List all the uncertainties that might significantly influence the consequences of any alternatives.”
- “Consider these uncertainties one at a time and determine whether and to what degree their various possible outcomes might influence the decision. When there are many possible uncertainties, winnow them down to the few that are likely to matter most.” (p. 115)
Define outcomes:
- “The possible outcomes of each uncertainty must now be specified. This requires answering two questions:”
“How many possible outcomes need to be defined to express the extent of each uncertainty?”
“How can each outcome best be defined?” (p. 116) - “When there are many possible outcomes, you should simplify your expression of them by organizing them into ranges, or categories. The categories can be either quantitative ($10,000 to $20,000, $20,000 to $30,000, and so on) or descriptive (high, medium, low; successful, unsuccessful, neutral). In some cases, it may be helpful to assign a representative value to a numerical range—for example, using $25,000 as a stand-in for the range $20,000 to $30,000—to make calculations and comparisons easier.” (p. 116)
Assign chances.
- “Clearly defining the possible outcomes or categories of outcomes will help you in judging the chance, or likelihood, that each outcome will occur. Still, though, assigning chances can be one of the toughest and most nerve-wracking tasks in decision making, especially when you don’t know very much about the subject or when you’re under time pressure. But you can help ensure that your assessments are both reasonable and useful by following these suggestions:” (p. 117)
- Use your judgment– “Often, you can make a reasonable assessment of the chances of a given outcome based on your own knowledge and experience. Oddsmakers do it all the time in sports betting. Friends do it when they arrange blind dates. We all do it almost unconsciously in daily life: What are the chances I’ll encounter delays on my homeward commute this Friday?” (p. 117)
- Consult existing information– “There will often be information available that will help you assign chances to outcomes. You should carefully consider all the potential sources of information—libraries, the Internet, documents in your organization, research data, professional publications—that might shed light on the potential outcomes. Janet, for example, might get climatological data from the weather bureau to help her assess whether it will rain on a summer afternoon or evening.” (p.117)
- Collect new data- “Sometimes the particular data you need may not be available off the shelf—you may need to collect them yourself. A food company might estimate the percentage of families who will buy a new brand of coffee by conducting a market trial or a telephone survey.” (p. 117)
- Ask experts- “For most uncertainties, there will probably be someone out there who knows more about it than you do. Seek out an expert—your doctor, lawyer, or accountant, an economist—and elicit his or her judgment. In Janet’s case, a local meteorologist would be an appropriate expert.” (p. 118)
- Break uncertainties into their components.– “Sometimes dividing an uncertainty into its components, thinking about the components, and then combining the results will help in establishing probabilities. An entrepreneur recognizes that the success of a new car wash in an area currently undergoing development will depend on the relative number of cars brought to the area by the different proposals for the adjoining site: a shopping mall or an office park. He can assign chances to various ranges of washes per day assuming the mall is built, and do likewise assuming the office park is constructed. He can then blend the results in proportion to the chances he assigns to the construction of a mall and of offices, to get an overall assessment of washes per day.” (p. 118)
People like to use terms, but if you use terms, people may missunderstand you.
- When expressing chances, qualitative terms may come first to mind. In casual conversation, people often describe chances using phrases such as ‘‘unlikely,’’ ‘‘toss-up,’’ ‘‘barely possible,’’ ‘‘fairly likely,’’ ‘‘pretty good chance,’’ ‘‘almost sure,’’ and so on. They do this not only because it’s easy, but also because they think they’re really communicating their judgments about likelihood. But one person’s ‘‘fairly likely’’ may or may not be the same as the next person’s. Such subjective phrases may be sufficient for personal decisions that will not need to be justified to others, but they’re not precise enough for most decisions. In most cases, therefore, you will want to express chances quantitatively, as actual probabilities, using either a decimal (0.2) or a percentage (20 percent). Using numbers reduces the likelihood of miscommunication and sharpens decisions.” (p. 119)
Pinpoint precision is not required for probabilities.
- “Pinpoint precision usually isn’t required in assigning chances. Frequently, knowing that a chance falls within a certain range is sufficient for guiding a decision. (See ‘‘Which Flight?’’ below.) If the estimated chance of some outcome falls between 30 percent and 50 percent, for example, compare the alternatives using 40 percent. Then reconsider them using 30 percent or 50 percent. More often than not, the change won’t matter; the decision will remain the same.” (p. 120)
How can you get other people to express probabilities?
- “If you are having trouble expressing your judgment quantitatively, or getting someone else to do so, zero in from the extremes. If you ask the hostess at a busy, no-reservations restaurant the chances of getting seated at 5:30 P.M. on Thursday, she might respond, ‘‘I haven’t a clue; either you will or you won’t.’’ (Ah, frustration!) Countering with the question, ‘‘Is the chance better than 25 percent?’’ will very often elicit something more useful: ‘‘Oh, much more than that.’’ ‘‘More than 50 percent?’’ ‘‘Yes.’’ ‘‘As much as 90 percent?’’ ‘‘Too high.’’ The range has been narrowed to between 50 percent and 90 percent;” (p. 119)
- “Karen and Jane now call on Sam’s expertise to quantify the likelihood that Karen will win the trial. Sam has told Karen that she has a ‘‘pretty good’’ chance of winning, based on the outcomes of similar cases, the record of the judge, and his assessment of his own litigation skills. Jane probes the meaning of ‘‘pretty good,’’ trying to arrive at a hard number, which would sharpen the analysis. She asks Sam, ‘‘How would you translate ‘pretty good’ into a probability?’’ ‘‘I just don’t think that way,’’ Sam answers. ‘‘I don’t see how you can put a number on everything, especially things as subjective as winning a trial.’’ Jane turns to Karen. ‘‘How do you interpret that, Karen? Give me some number.’’ ‘‘Oh, I’d say that Sam thinks our chance of winning is around 20 or 30 percent.’’ Sam protests. ‘‘That’s not what I said! When I say a pretty good chance, I mean something more than that!’’ ‘‘How much more? More than 50-50?’’ ‘‘Certainly. More than 50 percent.’’ ‘‘How much more?’’ ‘‘Oh, I don’t know that you can put a precise number on it. It certainly isn’t as high as 90 percent. In jury trials you can never be that sure. It’s maybe somewhere between 60 and 80 percent.’’ ‘‘Would you say that 70 percent is reasonable, or high, or low?’’ ‘‘It’s a good estimate, as close as we can get.’’ (p. 128)
Use decision trees.
- “Some decisions, particularly highly complex ones, will require further analysis. That’s when a decision tree can be extremely useful. A decision tree provides a graphical representation—a picture—of the essence of a decision, displaying all the interrelationships among choices and uncertainties. In one sense, a decision tree is like a blueprint—it lays out, methodically and objectively, the architecture of a decision. And just as a builder would not set out to construct a house without a blueprint, a decision maker will often require a decision tree to resolve a tough choice under uncertain conditions.” (p. 123)
- “Decision trees are especially useful for explaining decision processes to others. (Hence the careful numbering of the branching points and the labeling of the branches.) Getting into the habit of sketching decision trees, even for relatively simple decisions involving uncertainty, can enhance your decision-making skills in two ways. First, decision trees encourage thorough, logical thinking about a problem—a useful habit to cultivate. Second, mastering the mechanical skill of tree construction on simple problems will make it easier to use the technique for more complex ones,” (p. 125)
Example 1:
“This simple decision tree, with its four possible paths, shows how pictures can clarify the relationships among alternatives, uncertainties, and consequences. It brings risk profiles to life. Seeing her decision presented this way immediately sharpens Janet’s thinking. She concludes that a successful picnic would meet her objectives so much better than would the dinner dance that it is worth taking a 30 percent chance on rain. She opts for the picnic.” (p. 125)
Example 2:
Jane summarizes. ‘‘If you go to trial and lose—not the most likely outcome, but it has a chance of 30 percent—your life would be pretty bad. You’d be in debt; you couldn’t afford some of the things that would make you happier; you’d have to remain in your present job and keep your present apartment.’’ Karen interrupts. ‘‘Not to mention the humiliation of losing and my regret over not accepting the sure thing of $210,000. I’m in pretty bad shape now, but I’d be far worse off if I lost the trial.’’ ‘‘But who says you’re going to lose the trial?’’ Sam barks. Jane continues: ‘‘If you netted a lot more money from the trial, what would you do with it? How would it change your life? How much happier would you be?’’ ‘‘If I had a lot more money, I’d still do all the things I said I’d do if I had the $210,000. But I’d get a condo rather than go on renting, I’d buy a new car instead of a used one, and maybe I’d buy some clothes and take a few trips to Europe and other places. And I’d definitely go to graduate school. But those things aren’t nearly as important to me as what that initial $210,000 would bring.’’ ‘‘How much more money above the first $210,000 would give you roughly the same satisfaction it would?’’ ‘‘Close to a million! At least $800,000.’’ Sam couldn’t contain himself. ‘‘You can’t be serious, Karen! You can’t equate having only about $200,000 with having another $800,000!’’ ‘‘Yes, I am serious. Without the $210,000, I’m ruined. More would make me richer, but that’s not as important to me as getting an even start.” (p. 160)
Risk tolerance
- “A person’s attitude towards risk is as individual as his or her personality. Some people avoid risk at all costs—they put all their retirement savings into certificates of deposit insured by the federal government. Others embrace risk—they invest all their money in options, in penny stocks, in junk bonds. Most of us fall somewhere in between. We take on some degree of risk, knowing that it goes hand in hand with reward, but not so much that we can’t sleep at night.” (p. 135)
Example 1:
- “The essence of Rob’s decision is that the surgery alternative offers him a 90 percent chance of restoring his vision, but a 10 percent chance of permanently worsening it. Clear enough? Yes. And yet so difficult. Should he take his chances on the surgery or play it safe with the status quo? What would you decide? The smart choice for one person may not be the smart choice for another. You might decide not to have the surgery, but your next-door neighbor might opt for it. It all depends on one’s attitude toward risk.” (p. 137)
- “Rob believes that restoring 20/30 vision without fuzziness would make a huge difference to him. He could resume driving at night, and tennis and traveling—two of his favorite pastimes—would become much easier. And although dropping to 20/100 would be bad—no question about it—he feels he has already made so many adjustments to weakened vision that it wouldn’t be the end of the world. He therefore decides that, in terms of desirability, the negative consequence of deteriorated vision only slightly outweighs the positive consequence of improved vision.” (p. 139)
- “Your risk tolerance expresses your willingness to take risk in your quest for better consequences—in Rob’s case, better vision. It depends primarily on how significant you consider the downside— the poorer consequences of any decision—compared to the upside. If, like most people, you are risk averse, the poorer consequences will weigh more heavily in your mind than the better ones. The more heavily they weigh, the more risk averse you are. Thus, to reflect your risk tolerance in a decision you need to think carefully about how desirable you consider the possible consequences relative to one another.” (p. 137)