Unpacking Effect

Decision under Uncertainty

Unpacking affects more than numeric judgments of probability. In decision theory, however, subjective probabilities are generally inferred from preferences between uncertain prospects rather than assessed directly. It is natural to inquire, then, whether unpacking affects people’s decisions as well as their numeric judgments. There is considerable evidence that it does. For example, Johnson et al. (1993) observed that subjects were willing to pay more for flight insurance that explicitly listed certain events covered by the policy (e.g., death resulting from an act of terrorism or mechanical failure) than for a more inclusive policy that did not list specific events (e.g., death from any cause).

Unpacking can affect decisions in two ways: First, as has been shown, unpacking tends to increase the judged probability of an uncertain event. Second, unpacking can increase an event’s impact on the decision, even when its probability is known. For example, Tversky and Kahneman (1986) asked subjects to choose between two lotteries that paid different amounts depending on the color of a marble drawn from a box. (As an inducement to consider the options with care, subjects were informed that one-tenth of the participants, selected at random, would actually play the gambles they chose.) Two different versions of the problem were used, which differed only in the description of the outcomes. The fully unpacked Version 1 was as follows:

The source’s complete payoff table for the red, green, blue, and yellow outcomes is not reproduced here.

The study reports that Box B dominates Box A and that all subjects chose Box B in this version. Version 2 combined the two outcomes resulting in a loss of $15 in Box A (i.e., Blue and Yellow) and the two outcomes resulting in a gain of $45 in Box B (i.e., Red and Green):

The partial description retained here gives Box A a loss of $15 for Blue or Yellow and Box B a gain of $45 for Red or Green.

In accord with subadditivity, the combination of events yielding the same outcome makes Box A more attractive because it packs two losses into one and makes Box B less attractive because it packs two gains into one. Indeed, 58% of subjects chose Box A in Version 2, even though it was dominated by Box B. Starmer and Sugden (1993) further investigated the effect of unpacking events with known probabilities (which they called an event-splitting effect) and found that a prospect generally becomes more attractive when an event that yields a positive outcome is unpacked into two components. Such results demonstrate that unpacking affects decisions even when the probabilities are explicitly stated.

The role of unpacking in choice was further illustrated by Redelmeier et al. (1995). Graduating medical students at the University of Toronto (N = 149) were presented with a medical scenario concerning a middle-age man suffering acute shortness of breath. Half of the respondents were given a packed description that noted that, “obviously, many diagnoses are possible . . . including pneumonia.” The other half were given an unpacked description that mentioned other potential diagnoses (pulmonary embolus, heart failure, asthma, and lung cancer) in addition to pneumonia. The respondents were asked whether they would prescribe antibiotics in such a case, a treatment that is effective against pneumonia but not against the other potential diagnoses mentioned in the unpacked version. The unpacking manipulation was expected to reduce the perceived probability of pneumonia and, hence, the respondents’ inclination to prescribe antibiotics. Indeed, a significant majority (64%) of respondents given the unpacked description chose not to prescribe antibiotics, whereas respondents given the packed description were almost evenly divided between prescribing (47%) and not prescribing them. Singling out pneumonia increased the tendency to select a treatment that is effective for pneumonia, even though the presenting symptoms were clearly consistent with a number of well-known alternative diagnoses. Evidently, unpacking can affect decisions, not only probability assessments.

Although unpacking plays an important role in probability judgment, the cognitive mechanism underlying this effect is considerably more general. Thus, one would expect unpacking effects even in tasks that do not involve uncertain events. For example, van der Pligt, Eiser, and Spears (1987, Experiment 1) asked subjects to assess the current and ideal distribution of five power sources (nuclear, coal, oil, hydro, solar/wind/wave) and found that a given power source was assigned a higher estimate when it was evaluated on its own than when its four alternatives were unpacked (see also Fiedler & Armbruster, 1994; Pelham, Sumarta, & Myaskovsky, 1994). Such results indicate that the effects of unpacking reflect a general characteristic of human judgment.