Structure Complex Choices

Structure Complex Choices

People adopt different strategies for making choices depending on the size and complexity of the available options. When we face a small number of well-understood alternatives, we tend to examine all the attributes of all the alternatives and then make trade-offs when necessary. But when the choice set gets large, we must use alternative strategies, and these can get us into trouble.

Sometimes the choice architect provides a function similar to that of a curator at a museum. To the two of us, the most enjoyable art exhibits are rich enough to offer a meaningful experience but also small enough to be enjoyed in less than two hours, about the length of most movies. The old expression that less is more rings true here. Good choice architects often winnow the choice set down to a manageable size.

Often, however, people are their own choice architects; they even nudge themselves. A self-nudge can be called a “snudge,” and for most of us, life can be improved via well-chosen snudges. People might limit the amount of food in their refrigerators; they put some money they don’t want to spend into a one-year certificate of deposit that has a penalty for early withdrawal; they might delete Facebook or Twitter from their smartphones; they might program their computer so that they cannot receive email during certain hours. People work to counteract their own self-control problems, often by redesigning the architecture within which they make choices—for example, by making certain options harder or less fun, or by eliminating them altogether.13

For a more elaborate example, consider Jane, who has just been offered a job at a company located in a large city far from where she is living now. Compare two choices she faces: which office to select at her workplace and which apartment to rent as her home. Suppose Jane is offered a choice of three available offices. A reasonable strategy for her to follow would be to look at all three offices, note the ways they differ, and then make some decisions about the importance of such attributes as size, view, neighbors, and distance to the nearest restroom. This is described in the choice literature as a compensatory strategy, since a high value for one attribute (big office) can compensate for a low value for another (loud neighbor).

Obviously, the same strategy cannot be used to pick an apartment. In a large city, thousands of apartments might be available. If Jane ever wants to start working, she will not be able to visit each apartment and evaluate them all. Instead, she is likely to simplify the task in some way. One strategy to use is what Amos Tversky called “elimination by aspects.” Someone using this strategy first decides what aspect is most important (say, commuting distance), establishes a cutoff level (say, no more than a thirty-minute commute), then eliminates all the alternatives that do not come up to this standard. The process is repeated, attribute by attribute (no more than $2,500 per month; a functional kitchen; dogs permitted), until either a choice is made or the set is narrowed down enough to switch over to a compensatory evaluation of the finalists.

When people are using a simplifying strategy of this kind, alternatives that do not meet the minimum cutoff levels may be eliminated even if they are fabulous on all other dimensions. So, for example, an apartment that is a thirty-five-minute commute will not be considered even if it has a dynamite view and costs $500 a month less than any of the alternatives.

Social science research reveals that as the choices become more numerous or vary on more dimensions, people are more likely to adopt simplifying strategies. The implications for choice architecture are related. As alternatives become more numerous and more complex, choice architects have more work to do, and are much more likely to influence choices (for better or for worse). For an ice cream shop with three flavors, any menu listing those flavors in any order will do just fine, and effects on choices (such as the order in which they are listed) are likely to be minor because people know what they like. As choices become more numerous, though, good choice architecture should provide structure, and structure will affect outcomes.

Consider the example of a paint store. Even ignoring the possibility of special orders, paint companies often sell thousands of colors that you can apply to the walls in your home. It is possible to think of many ways of structuring how those paint colors are offered to the customer. Imagine, for example, that the colors were listed alphabetically. Arctic White might be followed by Azure Blue, and so forth. While alphabetical order is a satisfactory way to organize a dictionary or a workplace directory, it is a horrible way to organize a paint store.

Instead, paint stores have long used something like a paint wheel, with color samples organized by similarity: all the blues are together, next to the greens, and the reds are located near the oranges, and so forth. The problem of selection is made considerably easier by the fact that people can see the actual colors, especially since the names of the paints are spectacularly uninformative. (On the Benjamin Moore Paints website, three similar shades of beige are called Roasted Sesame Seed, Oklahoma Wheat, and Kansas Grain.) Thanks to modern computer technology and online shopping, many problems of consumer choice have been made far simpler. A good paint website not only allows the consumer to browse through dozens of shades of beige but also permits the consumer to see (within the limitations of the computer monitor) how a particular shade will work on the walls with the ceiling painted in a complementary color.

Of course, the variety of paint colors is tiny compared to the number of books sold by Amazon (zillions) or websites covered by Google (many zillions). Successful online companies succeed in part because of immensely helpful choice architecture. Customers looking for a movie or television show to stream can easily search movies by actor, director, genre, and more, and they can also get recommendations based on the preferences of other movie lovers with similar tastes, the method called “collaborative filtering.” Algorithms use the judgments of other people who share your tastes to filter through the vast number of books or movies available in order to increase the likelihood of picking one you like. Collaborative filtering is an effort to solve a problem of choice architecture. If you know what people like you tend to like, you might well be comfortable in selecting products you don’t know, because people like you tend to share your tastes. For many of us, collaborative filtering is making difficult choices easier.

A cautionary note: Surprise and serendipity can be fun for people, and good for them too, and it may not be entirely wonderful if our primary source of information is about what people like us like. Sometimes it’s good to learn what people unlike us like—and to see whether we might even like that. If you like the mystery writer Harlan Coben (and we agree that he’s great), collaborative filtering will probably direct you to other mystery writers (we suggest trying Lee Child, by the way), but why not try a little Joyce Carol Oates or A. S. Byatt, or maybe even Henry James? If you think of yourself as a progressive, and you like books that fit your predilections, you might want to see what conservatives think; if nothing else, you will fare better when arguing with your relatives at family gatherings. Public-spirited choice architects—those who provide our various sources of news, for example—know it’s good to nudge people in directions that they might not have specifically chosen in advance. Structuring choice sometimes means helping people to learn, so they can later make better choices on their own.