corresponding number. Half of the participants were allowed to choose their ticket, and the other half were assigned tickets. On the day of the drawing, participants were approached by one of the experimenters and asked how much they would be willing to sell their ticket for. Those who were assigned the ticket were willing to sell for about $1.96, but those who had chosen would not part with the ticket unless given $8.67. This and similar experiments have shown that people believe that they might somehow control random events. For example, the illusion of control leads people to throw dice harder when they want to roll higher numbers when playing craps. Further, people are willing to bet more on the unknown outcome of a random event when they are told the event will happen in the future than when they are told it happened in the past. In many cases, people might convince themselves that they have the ability to influence things that are well beyond their own control.

Conservatism versus Representativeness

Representativeness, or learning too much from the given information, appears to be pervasive in its influence on learning and decision making. Nonetheless, people do not always jump to conclusions. In fact, Ward Edwards conducted a series of experiments in the 1960s that found what he termed conservatism. If representativeness is learning too fast, conservatism can be thought of as learning too slowly. He conducted a series of experiments that look very similar to the later work of David Grether except that there were hundreds of balls in each container and still only a small number of draws. In his experiments he finds that the base rate, or prior information about which ball is drawn, receives more weight than would be implied by Bayes’ rule. Thus, the subjects stick to their initial beliefs regardless of the information presented. What accounts for the difference?

The work of Robin Hogarth and Hillel Einhorn has tried to answer exactly this question by presenting participants various types and series of information to see how it influences beliefs. When the information is less complicated and easier to understand, people seem to update their beliefs too quickly, displaying a recency effect. Recency is consistent with the representativeness heuristic. When the information is very complicated and requires real cognitive effort to discern, they found that initial beliefs persist—a primacy effect. Primacy is consistent with Edwards’s conservatism. Thus, in Edwards’s experiment, it may be that drawing a small number of balls from distributions with hundreds of balls provides information that is too difficult to process, leading to conservatism. The implication is that simple messages are much more likely to change people’s minds. A well-reasoned, though complex, argument might not have a chance to succeed.

EXAMPLE 7.9 Diseases and Accidental Death

Among the decisions most closely associated with risk and risk perceptions are the precautions we take to prolong our lives. There are many different ways one might die and many different actions we might take to prevent each particular mode of death. For example, one who has a family history of stroke could alter one’s diet to reduce the possibility of a stroke. Which actions are worth taking depends heavily on how likely we believe a particular mode of death is. For example, the overwhelming majority (around

80 percent) feel that accidental death (e.g., car accident, accidental fall) is much more likely than death by stroke. This should lead people to worry more about their driving habits or their proximity to cliffs than they would about diet. However, in truth, you are about twice as likely to die from a stroke as you are from all accidental sources combined. In fact you are more than 15 times more likely to die from some disease than you are to die from some accident. Only 57 percent believe that death by disease is more likely than death by accident. About 70 percent believe that there are more victims of homicide than suicide. In actuality there are close to 1.5 suicides for every homicide.

Although there are many potential explanations for why people might so poorly predict the possible sources of death, Sarah Lichtenstein and a team of researchers propose that news coverage may be partially to blame. They compared the amount of newspaper coverage for 41 various causes of death to the estimates of 61 participants of the prevalence of the various causes, and they found a very high correlation. Participants believed that causes like homicide, which are covered much more frequently by the press than stroke, were much more prevalent. Importantly, the newspaper coverage was not very related to actual prevalence of the various causes of death.

Consistent with representativeness, Lichtenstein also found evidence that people ignore base rates in their data. For example, participants felt that death due to smallpox was much more likely than death due to complications arising from smallpox vaccination. In fact, partially owing to smallpox vaccinations, cases of smallpox are very rare and thus death by smallpox is very rare. Alternatively, nearly all school-age children have been vaccinated for smallpox. Although the probability death from being vaccinated for smallpox is very small, the sheer number of vaccinations leads to a much higher prevalence of death by vaccination than death by the disease itself.

EXAMPLE 7.10 Begins with R

If we were to take a Standard English dictionary and tabulate the number of words contained therein, do you believe we would find more words that begin with the letter R or that have R in the third position? Amos Tversky and Daniel Kahneman asked 152 participants this question, as well as identical questions for the letters K, L, N, and V. If you were to write down as many words as you know that begin with the letter R, you would likely fill the list rather easily. This is because we tend to classify words by their first letter. We often list words alphabetically. Further, these words would all have the same initial sound. Alternatively, if you were asked to construct a list of all the words you knew that had R as the third letter, the list would likely be considerably shorter. We tend not to classify words with the same third letter together. This is an unfamiliar task and so we might fail to produce much of a list. In fact, English has more words that have R in the third letter than in the first. For example, in this paragraph so far, 16 words have R in the third letter, but only one begins with R. Participants believed that words beginning with the letter R would be about twice as numerous as those with R as the third letter. Similar results were achieved for each of the other letters, though each appears more often as a third letter than as a first letter.

Availability Heuristic

As illustrated in the previous two examples, people tend to assess the probability of an event based on how easily an instance of the event may be recalled. This tendency to judge probability based on the difficulty one has in recalling an event has been termed the availability heuristic. The availability heuristic naturally leads people to exaggerate the probability of events that are easily recalled and underestimate the probability of events that are difficult to recall. Thus, newspaper coverage of violent deaths could lead one to believe that such deaths are common. At the same time, the lack of news coverage for deaths by disease could lead one to judge these deaths to be uncommon in comparison, despite their statistical prevalence. Such results could influence the actions of people worried about their eventual demise. The availability bias may be a useful tool for a policymaker or marketer. It is no surprise that smokers tend to view smoking as less of a threat of death than nonsmokers. However, it might surprise you that both groups significantly overestimate the probability of death from smoking. By highly publicizing the health risks from smoking, information campaigns have biased public opinion, causing people to believe that cigarettes are more lethal than they truly are. Nonetheless, such a view almost certainly reduces the risk to others from secondhand smoke as well as the true risks from smoking to the people who might have smoked otherwise.

The availability heuristic depends heavily on exposure to the possible events and on the cognitive process that is necessary to recall events. Newspapers and public information campaigns can affect exposure, making the events that are discussed or viewed seem more prevalent. Alternatively, the word-construction task illustrates how cognitive processes can bias probability judgment. Tasks that are unfamiliar, such as constructing words with a specific third letter, results in underestimating the probability of the associated outcome.

As another example, consider a bus route with 10 potential bus stops. How many possible routes could be constructed that stop at exactly two of the 10 bus stops? Is this more or fewer than the number of routes that could be constructed that stop at eight of the 10 bus stops? In fact the number is identical. Nonetheless, the first task seems easier given the apparent large number of potential stopping places relative to the number of stops. The task of producing the eight stop possibilities is more dif- ficult. Thus, most people intuitively predict that there are more possible two-stop routes than eight-stop routes. Similar results have been found in consumer evaluations of the risk of product failures. If the failures have occurred with brands that have interesting or distinctive names, people are much more likely to assess the probability of failure as being high. The distinctive name enables them to remember the failure.

One phenomenon related to the availability heuristic is the false consensus. People have the tendency to believe that others hold the same opinions and preferences that they themselves hold. Thus, people might exaggerate the extent to which their views and actions are normal or are similar to those of others in the general population.

EXAMPLE 7.11 Earthquake Insurance

The availability heuristic can lead to some strange behavior when dealing with risk and uncertainty. Roger Shelor, Dwight Anderson, and Mark Cross note a prominent example. On October 17, 1989, at 5:04 P.M. (just before game 3 of a World Series matchup between the Oakland Athletics and the San Francisco Giants) a major earthquake struck the San Francisco Bay area. The earthquake registered 7.1 on the Richter scale, causing buildings and highways to collapse in San Francisco and Oakland. The quake killed dozens of people and caused several billions of dollars in damage.

One might think that such an event would have severe and negative implications for the insurance industry. After such an event, the insurance industry is responsible for restoring all damaged property that was covered by earthquake insurance. Unintuitively, stock prices for insurance companies rose significantly following the 1989 earthquake. Those who follow the market for earthquake insurance have noted that following a major earthquake, the demand for earthquake insurance spikes. The prevalence of news coverage and the visions of the damage leads people to temporarily consider earthquakes more probable and thus to seek insurance against the possible damage. Along the fault where the quake has occurred, the risk actually declines, with the earthquake itself relieving some of the pressure on the fault line. Thus, people insure just as the probability of an event decreases. In fact, the increase in demand for earthquake insurance observed following the 1989 earthquake more than offset the billions of dollars in losses the earthquake generated. Further, the increase in demand was not just limited to the San Francisco Bay area. Rather, earthquake insurance demand increased substantially all across the country—even in areas where earthquakes are truly rare.

Bias, Bigotry, and Availability

Hiring an employee often represents a substantial and risky investment. Once the person is hired, usually a substantial amount of time must be invested in training the new employee to fill that particular position. It could take several months to discover if the new hire is a poor fit, is undereducated for that particular job, or might have a less-than-desirable work ethic. Even once a poor fit is identified, it can cost substantial resources to fire an employee in a way that minimizes the chance of a lawsuit or other undesirable situations. Once a want ad is placed, interested parties often apply for the job by sending a résumé and perhaps filling out a form. At that point, a manager or human resources specialist examines the résumés and identifies the most-promising candidates. These candidates are called in for interviews, after which the winning candidate is identified. At each step of the process, the employer must evaluate candidates based on imperfect information and assess the probability that the candidate will be a good fit for the job. With so little information to work with, there is substantial room for behavioral heuristics to step in.

In the past few decades, government and private employers have been under tremendous pressure to eliminate racial bias in hiring practices. Many have argued that

competitive markets should eliminate racial discrimination. Intuitively, any firm willing to hire the most productive workers at the going wage will make a larger profit and drive discriminating firms out of business. Thus (absent any racial preference by customers) employers should be interested in hiring in a way that ignores race. Employers prominently advertise that they are equal opportunity employers, implying that they have systems in place to ensure a fair evaluation of people who belong to a race that has been historically discriminated against. Further, federal and other government offices must adhere to guidelines governing the evaluation of minority candidates. With the tremendous changes in the evaluation process, one would expect evaluation of résumés and candidates to be fair and evenhanded.

Marianne Bertrand and Sendhil Mullainathan sought to test just how fair the employment market was. They conducted an experiment in which they fabricated résumés for a large number of fictitious people. They randomly assigned names to résumés, with some names intentionally chosen to signal racial background. For example, some résumés bore the names Todd or Brad, and others bore the names Jamal or Darnell. This latter group was selected to suggest that the applicant was black. Because the names were randomly assigned to résumés, the quality of the résumés was held constant across racial soundingness of the names. After sending the résumés in response to job advertisements, the researchers recorded the number of callbacks they received for each resume. White-sounding names had about a 9.5 percent chance of receiving a callback for an interview. Alternatively, black-sounding names received callbacks only 6.5 percent of the time—about one third less. This pattern held true in government job openings as well as private-sector jobs. If employers are actively trying to even the playing field, why would they discriminate against minority applicants with identical credentials? In fact, many employers included in this study were surprised by the findings and contacted the authors of the study for information on how to improve. Although some employers purposely discriminate against minorities, there appears also to be a group that unintentionally discriminates. Why might this happen?

There are many candidate causes. However, the availability heuristic might provide a clue. Myron Rothbart and a team of researchers conducted several psychology experiments examining the formation of stereotypes. In one experiment, people were shown a series of statements about hypothetical people belonging to a hypothetical group, such as “John is lazy.” The same statement may be shown several times in the sequence. Rothbart found that when dealing with a smaller population of people (16), participants are able to correctly predict the proportion of people in a group displaying particular traits. However, when the number of people is larger (64), the predicted proportion displaying a trait depends on how often the phrases including that trait were repeated. Thus, if “John is lazy” was displayed four times in the sequence, the participant would begin to think that more of the people in the group were lazy. In other words, the frequency of a message might influence the availability of the message, leading to stereotyping. Thus, when large numbers of news articles talk about racial difference in academic performance and achievement, crime rates, or other potential indicators of the desirability of a potential job candidate, they might inadvertently alter the treatment of people belonging to a particular race.