One strategy for making a risky decision is to calculate the expected value of the decision. People calculate the expected value by adding the value of a win times the probability of a win to the value of a loss times the probability of a loss.
Example: For Eric, the value of a win is +$495 ($500 prize – $5 cost), and the value of a loss is –$5. The probability of winning is 1/1000 and the probability of losing is 999/1000. Therefore the expected value is –3.5. That means Eric can expect to lose $3.50 for every raffle ticket he buys.
Even when decisions have negative expected values, people still make such decisions. Some researchers believe that this occurs because people make some decisions by estimating subjective utility, or the personal value of a decision’s outcome.
Example: Eric may still buy the raffle ticket because having the ticket lets him dream about buying a stereo he’s always wanted.
People often use heuristics to estimate probabilities. One heuristic people frequently use is the availability heuristic. When people use this rule-of-thumb strategy, they estimate probability based on how readily they can remember relevant instances of an event. If people can quickly remember instances of some event, then they will estimate that event as being quite likely.
Example: If Eric can think of several friends who have won raffles, he will judge that he is likely to win the raffle.
People also use the representativeness heuristic to estimate probability. The representativeness heuristic is a rule-of-thumb strategy that estimates the probability of an event based on how typical that event is. For example, if Eric the raffle ticket buyer lives in the United States, has several tattoos, and often wears dark sunglasses and a leather jacket, is it more likely that he owns a motorcycle or a car? If people use the representativeness heuristic, they may judge that Eric is more likely to own a motorcycle. This happens because the description of Eric is more representative of motorcycle owners.
People often make flawed decisions. There are many biases that account for bad decision-making.
When using the representativeness heuristic, people frequently ignore the base rate, or the total number of events.
Example: If people judged that Eric is more likely to be a motorcycle owner than a car owner because he has tattoos, they were subject to the tendency to ignore base rates. The total number of car owners in the United States far exceeds the number of motorcycle owners, so it is really more likely that Eric owns a car.
The representativeness heuristic can also make people susceptible to the gambler’s fallacy. The gambler’s fallacy is the false belief that a chance event is more likely if it hasn’t happened recently. This belief is false because the laws of probability don’t apply to individual independent events.
Example: Mindy tosses a coin and get heads. Because of this, she believes that on her second toss, she’ll get tails, since 50 percent of her tosses should yield tails. This belief is incorrect. Over a series of tosses, she can estimate that the probability of tails will be about 50 percent, but this logic can’t be correctly applied to a single toss.
Using the availability heuristic can cause people to overestimate improbable events. This happens because rare but memorable events come to mind easily.
Example: Recalling a few dramatic TV reports of plane crashes could make people overestimate the likelihood of a plane crash.
Using the availability heuristic can also cause people to underestimate likely events. This can happen when events are hard to visualize and don’t easily come to mind.
Example: Beth may have unprotected sex because she doesn’t think anyone she knows has a sexually transmitted disease (STD), and she doesn’t know what the symptoms of an STD might be. In reality, the majority of the adult American population has contracted one or more STDs, and Beth has a very high chance of contracting one herself through unprotected sex.
People sometimes make irrational decisions in an effort to minimize risk. An event is more likely to be chosen if it’s framed in terms of winning rather than losing.
Example: People are more likely to buy a raffle ticket if they hear they have a 1 in 1000 chance of winning than if they hear they have a 999 in 1000 chance of losing.
Confirmation bias is the tendency for people to look for and accept evidence that supports what they want to believe and to ignore or reject evidence that refutes their beliefs. When people reject evidence that refutes their beliefs, it can also be called belief perseverance, because rejecting contradicting evidence makes it easy for people to hold on to their beliefs.
Example: If Carl is a believer in herbal nutritional supplements, he may willingly accept research that supports their benefits while ignoring or rejecting research that disproves their benefits.
The overconfidence effect is the tendency for people to be too certain that their beliefs, decisions, and estimates are correct. People can minimize the effects of overconfidence by collecting a lot of information and evaluating it carefully before making a decision.
Example: At the outset of the Civil War, young Southern men eagerly enlisted in the Confederate Army, believing their superior gallantry would help them make speedy work of the Union soldiers.