236 Understanding Intuitive Decision Making
been obtained by simply averaging their individual judgments together. Moreover, group weighting
of information was a linear function of its degree of sharedness or common knowledge.
The likelihood that a piece of information, or slot value, will be recalled by a group is also afunction of the number of group members who share that information^344 as is the likelihood
that it will be repeated after it is fi rst mentioned.^345 Why? Because shared information is easier to
remember, easier to understand, is seen as more accurate and trustworthy, and leads group members
to perceive themselves as more competent when they discuss it.^346
When, as in the study described previously, no single group member possesses all of the infor-mation relevant to making the decision, or the values fi lling all the schema slots, but the group as
a whole does—a situation called the hidden profi le condition —groups rarely discover the best alter-
native.^347 Instead, groups typically choose the alternative that is supported by the information all
group members hold in common. In one study only 18% of groups in a hidden profi le condition
chose the best alternative. In contrast, when all the relevant information was known by every group
member, 83% of groups selected the best alternative.^348
Information Integration–Related Heuristics and Biases
Heuristic Choice Rules
Audience members often have great diffi culty accurately combining or integrating the slot
values they acquire for their schemata. A computer given the same information will always do
as well or better than people at information integration tasks.^349 One reason for the difference
between computers and people is that people attend to the values of only a few of the impor-
tant attributes or decision criteria and fail to combine those attribute values with the values of
other attributes.^350
Because of the difficulties inherent in integrating information, audiences will usually pre-fer easy-to-choose options over difficult-to-choose ones.^351 Audience members who must
combine the pros and cons of many alternatives described by many attributes may even
make a decision contrary to the one they would have made had the information been easier
to integrate.^352 Because of the difficulties inherent in the information integration process,
consumers are willing to pay more for products if the effort required to choose among them
is reduced.^353
The use of heuristic choice rules enables audiences to simplify or avoid outright the informa-tion integration process.^354 We have already examined several heuristic choice rules in Chapter 3.
For example, the elimination-by-aspects rule and the lexicographic rule are heuristic choice rules because
they are noncompensatory, that is to say, they allow the audience to side-step making diffi cult
trade-offs.^355 The equal weight rule , although a compensatory choice rule, is a heuristic choice rule
because audiences who use it automatically assign an equal weight to all attributes. In contrast,
the weighted additive rule is a compensatory choice rule that is a normative or nonheuristic rule. It
demands that the audience weight every attribute or decision criteria independently and consider
every value of every alternative for every attribute.
Another way audiences can simplify the information integration process is by deciding onthe basis of a single “if-then,” or confi gural, heuristic choice rule. For example, a recruiter
might have the rule that if a job applicant does not give her a fi rm handshake, then she will
not hire them. Experts often use such “if-then” rules to make decisions.^356 For example,
magistrates in the United Kingdom tend to rely on “if-then” heuristic choice rules based on
the prosecutor’s recommendation or the age of the defendant when reaching an exonerative
decision.^357