CHAPTER 11

Even Swaps: A Rational Method for Making Trade-Offs

by John S. Hammond, Ralph L. Keeney, and Howard Raiffa

Some decisions are easy. If you want to fly from New York to San Francisco as cheaply as possible, you simply find the airline offering the lowest fare and buy a ticket. You have only a single objective, so you need to make only a single set of comparisons. But having only one objective, as any decision maker knows, is a rare luxury. Usually, you’re pursuing many different objectives simultaneously. Yes, you want a low fare, but you also want a convenient departure time, a direct flight, an aisle seat, and an airline with an outstanding safety record. And you’d like to earn frequent flyer miles in one of your existing accounts. Now the decision is considerably more complicated. You have to make trade-offs.

Making wise trade-offs is one of the most important and difficult challenges in decision making. The more alternatives you’re considering and the more objectives you’re pursuing, the more trade-offs you’ll need to make. The sheer volume of trade-offs, though, is not what makes decision making so hard. It’s the fact that each objective has its own basis of comparison. For one objective, you may compare the alternatives using precise numbers or percentages: 34%, 38%, 53%. For another objective, you may need to make broad relational judgments: high, low, medium. For another, you may use purely descriptive terms: yellow, orange, blue. You’re not just trading off apples and oranges; you’re trading off apples and oranges and elephants.

How do you make trade-offs when comparing such widely disparate things? In the past, decision makers have relied mostly on instinct, common sense, and guesswork. They’ve lacked a clear, rational, and easy-to-use trade-off methodology. To help fill that gap, we have developed a system—which we call even swaps—that provides a practical way of making trade-offs among any set of objectives across a range of alternatives. In essence, the even-swap method is a form of bartering—it forces you to think about the value of one objective in terms of another. How many frequent flyer miles, for example, would you sacrifice for a $50 reduction in airfare? How long would you delay your departure time to be assured an aisle seat? Once you have made such value judgments, you can make sense of the variety of different measurement systems. You have a solid, consistent basis for making sensible trade-offs.

The even-swap method will not make complex decisions easy; you’ll still have to make hard choices about the values you set and the trades you make. What it does provide is a reliable mechanism for making trades and a coherent framework in which to make them. By simplifying and codifying the mechanical elements of trade-offs, the even-swap method lets you focus all your mental energy on the most important work of decision making: deciding the real value to you and your organization of different courses of action.

Creating a Consequences Table

Before you can begin making trade-offs, you need to have a clear picture of all your alternatives and their consequences for each of your objectives. A good way to create that picture is to draw up a consequences table. Using pencil and paper or a computer spreadsheet, list your objectives down the left side of a page and your alternatives along the top. This will give you an empty matrix. In each box of the matrix, write a concise description of the consequence that the given alternative (indicated by the column) will have for the given objective (indicated by the row). You’ll likely describe some consequences in quantitative terms, using numbers, and others in qualitative terms, using words. The important thing is to use consistent terminology in describing all the consequences for a given objective; in other words, use consistent terms across each row. If you don’t, you won’t be able to make rational swaps between the objectives.

To illustrate what a consequences table actually looks like, let’s examine one created by a young man we’ll call Vincent Sahid. The only child of a widower, Vincent plans to take time off from college, where he’s majoring in business, to help his father recover from a serious illness. To make ends meet while away from school, he will need to take a job. He wants a position that pays adequately, has good benefits and vacation allowances, and involves enjoyable work, but he’d also like to gain some experience that will be useful when he returns to school. And, given his dad’s frail condition, it is very important that the job give him the flexibility to deal with emergencies. After a lot of hard work, Vincent identifies five possible jobs. Each has very different consequences for his objectives, and he charts those consequences in a consequences table. (See table 11-1.)

As we see, a consequences table puts a lot of information into a concise and orderly format that allows you to compare your alternatives easily, objective by objective. It gives you a clear framework for making trade-offs. Moreover, it imposes an important discipline, forcing you to define all alternatives, all objectives, and all relevant consequences at the outset of the decision process. Although a consequences table is not too hard to create, we’re always surprised at how rarely decision makers take the time to put down on paper all the elements of a complex decision. Without a consequences table, important information can be overlooked and trade-offs can be made haphazardly, leading to wrongheaded decisions.

Eliminating “Dominated” Alternatives

Once you’ve defined and mapped the consequences of each alternative, you should always look for opportunities to eliminate one or more of the alternatives. The fewer the alternatives, the fewer trade-offs you’ll ultimately need to make. To identify alternatives that can be eliminated, follow this simple rule: if alternative A is better than alternative B on some objectives and no worse than B on all other objectives, B can be eliminated from consideration. In such cases, B is said to be dominated by A—it has disadvantages without any advantages.

Say you want to take a relaxing weekend getaway. You have five places in mind, and you have three objectives: low cost, good weather, and short travel time. In looking at your options, you notice that alternative C costs more, has worse weather, and requires the same travel time as alternative D. Alternative C is dominated by D and therefore can be eliminated.

You need not be rigid in thinking about dominance. In making further comparisons among your options, you may find, for example, that alternative E also has higher costs and worse weather than alternative D but has a slight advantage in travel time—it would take half an hour less to get to E. You may easily conclude that the relatively small time advantage doesn’t outweigh the weather and cost disadvantages. For practical purposes, alternative E is dominated—we call this practical dominance—and you can eliminate it as well. By looking for dominance, you have just made your decision much simpler—you only have to choose among three alternatives, not five.

A consequences table can be a great aid in identifying dominated alternatives. But if there are many alternatives and objectives, there can be so much information in the table that it gets hard to spot dominance. Glance back at Vincent Sahid’s consequences table and you’ll see what we mean. To make it easier to uncover dominance, you should create a second table in which the descriptions of consequences are replaced with simple rankings. Working row by row—that is, objective by objective—determine the consequence that best fulfills the objective and replace it with the number 1; then find the second-best consequence and replace it with the number 2; and continue in this way until you’ve ranked the consequences of all the alternatives. When Vincent looks at the vacation objective in his table, for example, he sees that 15 days ranks first, 14 days ranks second, the two 12 days tie for third, and 10 days ranks fifth. When he moves from the quantitatively measured objectives to the qualitatively measured ones, he finds that more thought is required because the rankings need to be based on subjective judgments rather than objective comparisons. In assessing the benefits packages, for example, he decides that dental coverage is more important to him than a retirement plan, and he makes his rankings on that basis. (See table 11-2.)

Dominance is much easier to see when you’re looking at simple rankings. Vincent sees that Job E is clearly dominated by Job B: it’s worse on four objectives and equivalent on two. Comparing Job A and Job D, he sees that Job A is better on three objectives, tied on two, and worse on one (vacation). When an alternative has only one advantage over another, as with Job D, it is a candidate for elimination due to practical dominance. In this case, Vincent easily concludes that the one-day vacation advantage of Job D is far outweighed by its disadvantages in salary, business-skills development, and benefits. Hence, Job D is practically dominated by Job A and can also be eliminated.

Using a ranking table to eliminate dominated alternatives can save you a lot of effort. Sometimes, in fact, it can lead directly to the final decision. If all your alternatives but one are dominated, the remaining alternative is your best choice.

Making Even Swaps

Although it’s possible that you’ll be down to a single alternative at this point, it’s far more likely that you’ll still have a number of alternatives to choose from. Because none of the remaining alternatives are dominated, each will have some advantages and some disadvantages relative to each of the others. The challenge now is to make the right trade-offs between them. The even-swap method offers a way to even out the advantages and disadvantages systematically until you are left with a clear choice.

What do we mean by even swaps? To explain the concept, we need to state an obvious but fundamental tenet of decision making: If every alternative for a given objective is rated equally—for example, if they all cost the same—you can ignore that objective in making your decision. If all airlines charge the same fare for the New York to San Francisco flight, then cost doesn’t matter. Your decision will hinge on only the remaining objectives.

The even-swap method provides a way to adjust the values of different alternatives’ consequences in order to render them equivalent and thus irrelevant. As its name implies, an even swap increases the value of an alternative in terms of one objective while decreasing its value by an equivalent amount in terms of another objective. If, for example, American Airlines charged $100 more for a New York to San Francisco flight than did Continental, you might swap a $100 reduction in the American fare for 2,000 fewer American frequent-flyer miles. In other words, you’d “pay” 2,000 frequent flyer miles for the fare cut. Now American would score the same as Continental on the cost objective, so cost would have no bearing in deciding between them. Whereas the assessment of dominance enables you to eliminate alternatives, the even-swap method allows you to eliminate objectives. As more objectives are eliminated, fewer comparisons need to be made, and the decision becomes easier.

The even-swap method can be a powerful tool in business decision making. Imagine you’re running a Brazilian cola company and several other companies have expressed interest in buying franchises to bottle and sell your product. Your company currently has a 20% share of its market, and it will earn $20 million in the fiscal year just ending. You have two key objectives for the coming year: increasing profits and expanding market share. You estimate that franchising would reduce your profits to $10 million due to start-up costs, but it would increase your share to 26%. If you don’t franchise, your profits would rise to $25 million, but your share would increase only to 21%. You put this all down in a consequences table.

Which is the smart choice? As the table indicates, the decision boils down to whether the additional $15 million profit from not franchising is worth more or less than the additional 5% market share you would gain from franchising. To resolve that question, you can apply the even-swap method following a straightforward process.

First, determine the change necessary to cancel out an objective

If you could cancel out the $15 million profit advantage gained by not franchising, the decision would depend only on market share.

Second, assess what change in another objective would compensate for the needed change

You must determine what increase in market share would compensate for the profit decrease of $15 million. After a careful analysis of the long-term benefits of increased share, you determine that a 3% increase would make up for the lost $15 million.

Third, make the even swap

In the consequences table, you reduce the profit of the not-franchising alternative by $15 million while increasing its market share by 3%. The restated consequences (a $10 million profit and a 24% market share) are equivalent in value to the original consequences (a $25 million profit and a 21% market share). (See tables 11-3 and 11-4.)

Fourth, cancel out the now-irrelevant objective

Now that the profits for the two alternatives are equivalent, profit can be eliminated as a consideration in the decision. It all boils down to market share.

Finally, select the dominant alternative

The new decision is easy. The franchising alternative, better on market share than not franchising, is the obvious choice.

For the cola company, only one even swap revealed the superior alternative. Usually, it takes more—often many more. The beauty of the even-swap approach is that no matter how many alternatives and objectives you’re weighing, you can methodically reduce the number of objectives you need to consider until a clear choice emerges. The method, in other words, is iterative. You keep eliminating objectives by making additional even swaps until one alternative dominates all the others or until only one objective—one basis of comparison—remains.

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John S. Hammond is a consultant on decision making and a former professor at Harvard Business School in Boston. Ralph L. Keeney is a professor at Duke University’s Fuqua School of Business in Durham, North Carolina. Howard Raiffa is the Frank Plumpton Ramsey Professor of Managerial Economics (Emeritus) at Harvard Business School. They are the coauthors of Smart Choices: A Practical Guide to Making Better Decisions (Harvard Business Review Press, 2015).

Excerpted from Harvard Business Review, March–April 1998 (product #98206).