Criteria for a Well-Structured Value Set
Criteria for a Well-Structured Value Set
Criteria for a Well-Structured Value Set
Not just any list of values will be appropriate for decision making. Value sets can be well structured or poorly structured. To be properly structured, a value set should satisfy the following criteria:
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Completeness•
Relevance•
Non-Redundancy•
Testability/Measurability•
Meaningfulness•
Value IndependenceIf you have created a value tree, it's important to be clear that the set of criteria we'll be considering here is not intended to apply to the entire tree. It's intended to apply only to the values (and sub-goals) that you've selected to include in your decision table and upon which your decision will be based.
Completeness Completeness Completeness Completeness
Completeness requires that all important values be included. A decision isn’t likely to “stick” if important values are overlooked. it’s likely to be a “pseudo-decision” (Tyler, 1969). The Bureau of Reclamation, in one of the most costly mistakes in its history, created plans for an Orme Dam that were never implemented (Brown, 1986). The reason was that such values as impacts on Native Americans and bald eagles weren’t fully taken into account. A later plan that was based on a more complete value set has now been successfully implemented.
As we've noted, the values most commonly overlooked are those that relate to consequences to others, those that relate to negative consequences, those that relate to long-term consequences, and those that relate to the ways we and others think about ourselves. Stimulus-variation techniques can be used to bring such values to mind and ensure completeness.
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It can help to apply the following test for completeness:
If the answer is, “I would care, and I would not want to toss a coin”, the follow-up question is, “Then what difference would the choice make?” This difference should call attention to additional values. If, instead, the answer to the first question is, “I wouldn’t care, and I'd just as soon toss a coin”, the value set passes this test for completeness. The test for defensive avoidance is also appropriate here: “Would I be willing to commit to and actually follow through on whichever of the alternatives the analysis indicates is the best?” Questioning any reluctance here can bring additional values to mind, including ones that are being avoided.
Consider a hypothetical example. You’re comparing cars in terms of Cost, Safety, Reliability, and Aesthetics, which you've determined are the important values. You then ask yourself, “If two cars were identical in cost, safety, reliability, and aesthetics, would I be willing to let someone else choose?” If your answer is, “No, I’d want to know how much their trunks hold”, you’ve discovered an additional value. In this case, you may decide that this value isn't important enough to add to your value set, but the technique worked just the same, and you can keep this value in mind in case you need it later to break a tie.
Amelia selected from her value tree the following set of values as the most relevant to her decision: Contribution to Society, Creativity/Intellectual Challenge, Cultural Diversity, Compensation, Time for Family, Emotional Involvement, and Work Culture. (Notice that Compensation isn't an ultimate value but a means, proxy value, or sub-goal.)
Amelia tested her value set for completeness by (a) considering the possibility that some variant of Enforcement, her intuitively lowest-ranked alternative, turned out to be superior to all the others in terms of the values she'd selected and (b) asking herself whether she’d then be willing to toss a coin. This question slowed her down; she felt resistance; she realized she wouldn’t. When she asked herself why,
To test for completeness,
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Pick the alternative you would intuitively rank highest;•
Pick the alternative you would intuitively rank lowest;•
Assume that these alternatives are identical withrespect to all the values you have identified so far; then
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her answer was, “If I worked in Enforcement, I wouldn’t be in control.” Thinking about it, however, she realized that this was Creativity again, and this had been taken into account. When thinking intuitively about the completeness question, she'd been unable to take into account the entire value set that she was easily able to take into account analytically. On reconsideration, understanding now that Creativity had been taken into account, she felt that she'd be willing to toss a coin, and so her value set really did appear to be complete, after all. She didn’t need to add any more values. However, in thinking about income, she realized that she'd left out benefits, so she changed Income to Compensation, to take account of both salary and benefits.
Relevance Relevance Relevance Relevance
Relevance requires that no unimportant values be included. An unimportant value is one that is impacted to about the same extent by all alternatives and, hence, of little relevance to a choice among them. Focusing first on only the most relevant aspects of a problem implements the breadth-first search strategy.
In applying breadth-first search to decision making, you first consider a broad range of alternatives taking into account only their most important, or decision-relevant, characteristics. This is what a good artist does in exploring a variety of possibilities with rough sketches, what a good writer does in exploring a variety of possibilities with rough outlines, and what a good chess player does in focusing first on the big pieces, the kings, queens, bishops, knights, and rooks. Later, the most promising alternatives are considered in depth. This is also what we do in creating models and maps, which simplify reality in terms of its most decision-relevant features.
The “20-80 rule” of management science asserts, as a rule of thumb, that 20% of the facts account for 80% of what you care about. These are the “vital few” among the “trivial many” (MacKenzie, 1972, pp. 51ff). Focusing on the vital few is the most efficient way to approach a problem.
If we’re to adhere to the principle of breadth-first search in problem formulation, the number of values must be kept to a minimum. A rule of thumb is that usually no more than five to seven values are required for completeness; often, two or three are enough. If alternatives are tied or nearly tied on the basis of just the most important values, less important values can always be brought in later to break the tie. If you already have five to seven values, you should be applying the following relevance test:
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Where the completeness test encourages you to add values, the relevance test encourages you to remove them. You should retain a value only if its relevance or importance is substantial in comparison with that of all the other values in the set. A good rough definition for “substantial” is an importance weight (see later) of at least .10.
Sunk costs is a classic example of a value that’s entirely irrelevant. As noted in discussing irrationality, we’re inclined to stick with an alternative that we’ve already invested a great deal in, to “throw good money after bad”. We tend to think of the sunk cost as applying only to the alternative in which it has been invested. However, the amount invested in any alternative will be “just as gone”, no matter which alternative is selected. No matter what you do, you aren’t going to get it back.
Let’s say that you’re planning to buy a piece of land, Parcel A, for $120,000 and that you’ve given the seller a non-refundable deposit of $2000. You then learn about a comparable piece of land, Parcel B, which is selling for only $117,000. If it weren’t for the deposit, you’d purchase Parcel B; however, the possibility of losing the $2000 and getting absolutely nothing for it inclines you toward Parcel A.
However, the $2000 is gone, no matter which parcel you purchase. That's why it's entered in both rows of the table. Past costs are “sunk costs”, “water over the dam”, or “spilt milk” and shouldn’t influence your decision. Only future prospects are relevant to
How likely is it that information about additional values will change my mind about any decision based on the values already in my set?
Past
Payments PaymentsFuture PAYMENTSTOTAL
Parcel A $2000 $118,000 $120,000
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your decision. While the purchaser would have to pay out $118,000, over and above the $2000 that has already been paid, to secure Parcel A, she’d have to pay out only $117,000 to secure Parcel B. If she purchases Parcel B, she’ll be $1000 richer than if she'd purchased Parcel A—though she’ll have to admit that the initial $2000 was invested in the wrong property. (This admission should be easier—even a source of pride—if she focuses on process and reminds herself that she's managing to resist a seductive decision trap that has led many less able decision makers astray.)
Amelia considered each of her values and asked herself whether the differences from alternative to alternative were substantial or negligible. All seemed to be substantial, so she didn’t drop any She kept relevance in mind, however, in case any alternatives were eliminated along the way, since eliminating alternatives can render once-relevant values irrelevant for deciding among the alternatives that remain.
Testability/Measurability Testability/Measurability Testability/Measurability Testability/Measurability
Testability is the requirement that the valued outcome be describable in objective terms, so that the facts for your decision could be provided by someone else. Measurability sets as an ideal the higher standard that each value be not only testable but also quantifiable. The experience of science has shown that numbers provide the most powerful language for expressing facts, and the discussion of value judgments will show that numbers, when they're meaningful, can also provide the most powerful language for expressing values. Although the goal of measurability can’t always be achieved, it usually can, and, in any case, it’s always a goal worth striving for. Testability, the less stringent criterion, can always be achieved.
The following question is a good check for testability:
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A good test for measurability is to ask yourself what units you're using to measure each attribute: dollars, days, number of people, and so forth. A good practice is to specify as part of your labels for values the units of measurement employed.
There are three good reasons for insisting on testability. First, as we saw in the last chapter, it’s a safeguard against irrationality. Second, it’s a way of getting clear in your own mind just what you mean. (For example, does “income” mean gross income or profit? Before or after taxes?) Third, it makes it easier to get information from someone or someplace else to improve your decision.
Measurability can clarify our thoughts, even in cases where we never get around to doing any arithmetic with the numbers. Dale was deliberating about whether to accept an attractive job offer in California. After some problem structuring, he saw that the principal argument in favor of accepting the offer was the opportunity for personal growth and that the principal argument against accepting it was that he'd have to live farther from his son (who lived with his ex-wife) and would thus become less a part of his son’s life.
To prepare him for thinking about the tradeoff between personal growth and involvement in his son’s life, I sought a measure for involvement in his son’s life. I suggested two: Number of Days with His Son Per Year, and Number of Days Between Visits. Dale saw Number of Days Between Visits as clearly the more meaningful measure, since it didn’t take long for him and his son to get out of touch with one another’s lives, but it also didn’t take long to get back in touch.
I then asked him what'd be the likely number of days between visits if he accepted the job offer, what was the longest time between visits he and his son had already experienced, and what that experience had been like. That did it! The longest number of days between visits that Dale and his son had experienced was only about half what the average number of days between visits would be if he took the job in California—and that had added a strain to their relationship that he wouldn't want to repeat. Without any further analysis, Dale decided to reject the job offer. The rest of the meeting was devoted to exploring ways he might enhance his personal growth without leaving the area.
Quantification, after some preliminary problem structuring, helped Dale see his problem so clearly that he was able to make a choice without performing any calculations. In the medical specialty example that opened the book, quantitative questions had the same effect.
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Dollars, hours, number of people, and years are useful measures for quantifying a wide range of values. In choosing among jobs, for example, salary is easy to quantify in dollars. Leisure time can be quantified in terms of hours per week. The responsibility associated with a job might be quantified in terms of number of people supervised. Interesting co- workers might be quantified in terms of hours spent in interactions with co-workers with certain interests or who have college degrees. Opportunity for advancement might be quantified in terms of the average number of years it takes people with your qualifications to reach some specified level. In addition to dollars, hours, number of people, and years, measures of distance and area are also frequently useful for quantifying values.
Certainly, there are cases where quantification makes little sense, for example, color and style of cars, designs of houses, and types of work performed in various occupations. In such cases, where the differences are stubbornly qualitative, we must settle for verbal descriptions of the situations that give rise to positive or negative feelings. However, these descriptions should satisfy testability, in that anyone, regardless of his or her values, could determine whether a particular description is or isn’t true of a particular alternative. “Fire engine red” and “cardinal” are testable descriptions, but “a bright red” is less so, and “an attractive red” even less so.
Often values such as job satisfaction or sense of self worth seem, at first glance, to be not only unmeasurable but altogether untestable. Let’s look at “self worth”, as a particularly difficult example. The key is to think of concrete situations in which the alternatives would result in different levels of a feeling of self worth. For example, what may give you a feeling of self worth on the job is people coming to you to seek your opinion on matters on which you feel you have expertise. Next, try to describe these situations objectively. Let’s say that you work in a large architectural firm and that your area of expertise is fire safety. You could then define an objective measure that would be related to your sense of self-worth on the job, such as “the average number of times a week that people would come to you to ask questions about fire safety” or “the percentage of major decisions regarding fire safety on which you would be consulted.” In principle, these are measures on which someone else could obtain data, and so they are testable. They happen also to be measurable. Surprisingly often, a value that initially seems inherently untestable will turn out, after some hard thought, to be not only testable but also measurable.
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The table below shows the objective descriptions and measures that Amelia came up with for her values.
Meaningfulness Meaningfulness Meaningfulness Meaningfulness
Meaningfulness requires that each value be stated in terms that are understandable to the decision maker or decision makers and truly expressive of the value in question. To be meaningful, terms should be in plain language and within the experience of the decision maker. In Dale’s decision, Number of Days Between Visits was more meaningful than Number of Visit Days Per Year, because it was more closely related to what Dale really cared about. Similarly, a color patch or a photograph provides a more meaningful description of a color than numerical specification in terms of the international CIE Color System.
As another example, the amount of money you earn on a job can be expressed in terms of the number of dollars you earn each hour, the number of dollars you earn each month, or the number of dollars you earn each year. Dollars/hour, dollars/month, and dollars/year are all equally relevant, and they're equally testable and measurable. However, dollars/ hour may be more meaningful to a laborer, dollars/year may be more meaningful to a professional, and dollars/month may be easier to relate to your expenses, many of which come due each month. If a professor at another university told me how many dollars an hour he made, I wouldn't likely know, without some mental calculation, whether I'd be pleased or displeased with that salary. While dollars/hour, dollars/ month, and dollars/year
Value Value Value
Value Description/Measure Description/Measure Description/Measure Description/Measure Objective Objective Objective Objective Contribution to
Society Number of people/year affectedDuration of impact Creativity/Intellectual
Challenge Percentage of time engaged in activities she has planned Cultural Diversity Percentage of time working with
people from other cultures Compensation
(Family) Dollar value of annual salary plus benefits Time for Family Days/week with family
Emotional
Involvement Days/week working one-on-one with children with emotional problems