Heuristics and Biases

Behavioral EconomicsPatrick McAlvanah

HeuristicsOptimal decision-making is hard. As such, people often rely on

rules of thumb and heuristics.

Heuristics approximate a solution with little deliberation cost, but have the potential to make systematic errors.

For example, the apparent distance of an object is determined in part by its clarity. The more sharply the object appears, the closer it appears to be.

This is a good and valid rule, but it leads to systematic errors:Distance will be overestimated when visibility is poor, because

the object will be blurred.Distance will be underestimated when visibility is good.

RepresentativenessWhen people are judging conditional probabilities, they should

update according to Bayes rule:

Pr (Hypothesis | Evidence) =Pr (Evidence | Hypothesis)*Pr (Hypothesis) / Pr (Evidence)

Several probability judgments are of the form:What is the probability that object A belongs to class B?or What is the probability that event A is due to process B?

People typically rely on a representativeness heuristic by which probabilities are evaluated to the degree that A is representative and similar to B

RepresentativenessRepresentativeness is a useful heuristic precisely because

representative values are more common than unrepresentative ones.

Eagles are less representative of birds than are robins, and eagles are less common.

However, representativeness can lead to neglecting features that are normatively important to Bayes rule: the base rates Pr (Hypothesis) and Pr (Evidence)

Steve is very shy and withdrawn, invariably helpful, but with little interest in peope. He is meek and tidy, and has a need for order and structure, and a passion for detail.

What is the probability that Steve is a farmer? A salesman? An airline pilot? A librarian? A physician?

Base Rate NeglectBecause Steve is similar to the stereotype of a librarian, people

will be say that Steve is more likely to be a librarian than a salesman, despite the fact that there are many more salesmen than librarians.

This is known as base rate neglect overweighing the importance of information and underweighing the natural base rates.

Another example: Subjects were shown brief personality descriptions of several individuals, allegedly sampled at random from 100 professionals engineers and lawyers.

the subjects were told the group was 70 engineers and 30 lawyers.

the subjects were told the group was 30 engineers and 70 lawyers

Base Rate NeglectSubjects were asked to choose, for a given personality

description, whether a person was likely to be an engineer or lawyer.

Subjects in the 1st group should rate any given questions as more likely to be an engineer than in the 2nd group.

In fact, the two groups produced essentially identical answers: they basically ignored the prior likelihoods.

Base Rate NeglectMathematical Example:A cab was involved in a hit and run accident at night. Two cab

companies, Blue and Green, operate in the city.

- 85% of the cabs in the city are Green and 15% are Blue- A witness identified the cab as Blue

The conditions are replicated and finds that given the circumstances its possible to correctly identify each of the two colors correctly 80% of the time and incorrectly 20%

Given the eyewitness testimony, what is the probability that the cab was Blue?

Base Rate NeglectMedian and modal responses tend to be about 80%

However: Pr (Blue | Id Blue) = Pr (Id Blue | Blue)* Pr(Blue) / [Pr (Id Blue|Blue)*Pr (Blue) + Pr (id

Blue|green)*Pr (Green)

.80*.15 / [.8*.15 + .2*.85] = .41

Given the base rates, its still more likely than not that the cab was green.

Law of Small NumbersRepresentativeness also implies misperceptions of the

importance of sample size. (A small sample size can represent its process or population just as well as a large one)

People tend to believe that ALL samples will closely resemble the process that generated them. This is known as the law of small numbers

Ex) A certain town has two hospitals, a large hospital and a small one. In the large hospital, 45 babies are born each day, and in the smaller hospital, 15 babies are born each day.

For a period of 1 year, each hospital recorded the days on which more than 60% of the babies born were boys. Which hospital do you think recorded more days?

Law of Small NumbersLarger hospital (N = 21)Smaller hospital (N =21)About the same (N =53)

People expect both hospitals to represent the true population distribution.

The law of small numbers has a number of important economic implications: people will gather too little data and overgeneralize from small samples to distributions. People will search too little and learn too quickly.

Gamblers FallacyAnother consequence of the law of small numbers is the

misperception of chance and randomness people expect that a sequence of events should have the essential properties of its process, even when the sequence is short.

When flipping a coin, people think H T H T H T is more likely than the sequence H H H T T T.

People underestimate how many strings there can be in a sequence

People expect negative autocorrelation in a series thats random:After a long run of red on a roulette wheel, people believe that

black is now due so that the wheel can correct itself.Hot hand fallacy

Gamblers FallacyPeople are bad at generating random sequences if asked to

write the results of 100 imaginary coin flips, their generated sequences will have too few runs of HHHHH or TTTTT.

Note that an important component of game theory is that mixed play equilibrium assumes that subjects can generate independent draws

Tests of plays in games with mixed strategy equilibria can yield that for a significant fraction of subjects, choices are NOT independent. Choices often depend on ones own previous plays and on the opponents previous play (and sometimes the interaction)

Misconception of RegressionIn the law of small numbers, when people know the underlying

population process, they expect regression to the mean when they should not.

When they do not know the underlying process, then regression to the mean surprises them.

Example: Consider two students who are identical, and have expected mean 85 for class tests.

Student 1 gets a 75 on the first one, student 2 gets a 90.

On test 2, who am I likely to be pleased with? Who am I likely to be disappointed with?

Misperception of RegressionExample Flight instructors training new pilots would praise

rookies for good landings and scold them for bad landings.

They noticed that, following a good landing and praise, performance on the next landing tended to be worse.

Likewise, following a poor landing and criticism, performance tended to improve on the next try.

The instructors concluded that punishment is beneficial for learning while praise is actually detrimental to learning.

Whats wrong with this?

Illusion of ControlAnother bias is known as the illusion of control the belief that

tasks that are only chance involve some element of skillPeople believe that they can have an influence outcomes over

events that they have no control over

When rolling dice for craps, people tend to throw harder for high numbers and softer for low numbers

Lottery bettors buy dream books telling them what numbers to bet after a certain dream

Langer (1975) people will wait longer or pay more for specific numbers in a lottery rather than random numbers

They will require much higher compensation to part with a chosen number ticket than a random ticket (an illusion of control endowment effect)

AvailabilityPeople often assess the probability of an event by the ease of

which instances of its occurrence springs to mind. This is known as the availability heuristic.

For example, one may assess the likelihood of a middle-aged heart attack by recalling such occurrences among ones acquaintances.

Availability is useful because large classes are usually recalled better and faster than instances of less frequent classes

However, it can also lead judgement astray.

AvailabilityBias due to familiarity / salience When the size of a class is

judged by the availability of its instances, classes whose instances are more easily retrieved will be over-estimated.

Im always stuck at red lights

Subjects were read a list of names and asked to judge whether the list contained more males or females.

For one group, there were several male celebrities included ; for the other, female celebrities.

Subjects erroneously judged that the gender with the more famous personalities was more numerous.

AvailabilitySeeing a house burning is more salient than just reading about it

in the paper.

Youre in the market for a new Honda. You check on epinions.com and of 500 reviews, 80% are positive. Youre going to purchase the car.

Then, someone tells you that their brother-in-law had a Honda, and it was a lemon and always breaking. You dont purchase the car.

Peoples estimates of the likelihood of traffic accidents will rise after seeing an overturned car on the side of the road.

AvailabilityEvents that are more newsworthy get rated as more likely:

people consistently overestimate the chance of death via plane crash, shark attack, terrorism, etc.

Bias due to effectiveness of search:

Is it more likely that a word starts with r or that r is the 3rd letter?

Looking for keys under lampost

Anchoring and AdjustmentThink of the last 3 digits of your phone number.Add 400 to it.

Now think of this 3 digit number as a date in years.

Was Attila the Hun defeated in Europe before or after that date?

In what year would you guess that Attila the Hun was actually defeated?

Anchoring and AdjustmentThink of the last 3 digits of your phone number.Add 400 to it.

Now think of this 3 digit number as a date in years.

Was Attila the Hun defeated in Europe before or after that date?

In what year would you guess that Attila the Hun was actually defeated?

The correct answer is 451.

Below is the mean guess for the year Attila the Hun was defeated as a function of a subjects anchor:

Anchoring and AdjustmentWhat year would you guess that George Washington was elected

President of the United States?

What would you guess the freezing point of vodka is?

Anchoring and AdjustmentWhat year would you guess that George Washington was elected

President of the United States?

Subjects average guess is 1779.Correct answer is 1789.

What would you guess the freezing point of vodka is?

Subjects average guess is 1 F.Correct answer is -20 F.

Adjustment and Anchoring

In many situations, people make estimates by starting from an initial value that is adjusted to yield the final answer.

The initial value starting point may be suggested by the problem, or it may be the result of a partial computation.

Adjustments are typically insufficient. That is, different starting points will yield different estimates. This is known as anchoring.

Anchoring and AdjustmentExternal anchor: when a subject fi rst compares whether the true

answer is greater or less than some externally provided value, and then provides a final estimate

This final estimate is biased in the direction of the anchor relative to the true anchor.

Internal anchors: The nature of the question suggests a natural starting point (1776 for George Washington or 32F for waters freezing point) which is then adjusted, insufficiently.

An important distinction is that internal anchors do not involve any explicit more or less comparison, as subjects already know that the starting value is incorrect and must require some adjustment

A 2003 study sold valuable consumer products (a wireless keyboard, bottles of wine, boxes of chocolate) to MBA students. Students were asked whether they would buy it for a price equal to the last two digits of their own Social Security number.

They were then asked to state the most they would pay for it. Despite the fact that Social Security numbers are essentially random, those with high numbers were willing to pay more for the products.

For example, subjects with numbers in the bottom half of the distribution priced a bottle of wine at $11.62, while those with numbers in the top half priced the same bottle at $19.95.

Application: Lawsuit damage requests

Anchoring ExamplesBookies explicitly set odds on horses, presumably to profit-

maximize. In a small percentage of races, a horse scratches and is removed from the race at the last second.

The quoted odds on the remaining field are now too generous, since the remaining horses win probability increased, and bookies profit margins are lower (and maybe even negative).

For example, suppose Horse A will win 25% of the time, and the bookie lists the payout odds of $3. The bookie keeps (1- p*O) = 1 - .25*3 = 25% of all bets wagered.

Now suppose Horse B scratches so Horse A will now win 33% of the time. Unless the bookie adjusts his odds, hes now keeping 0% of the bets wagered.

Anchoring ExamplesBookies scramble to re-adjust odds after a scratch to maintain

their previous profit margins.

McAlvanah and Moul (2013) demonstrate that the original (pre-scratch) odds serve as an internal anchor, and that re-adjustments are insufficient and sticky towards the original odds.

For example, a horse that was listed at 10:1 that should have been adjusted to 8:1 was only adjusted to 9:1.

Bookies re-adjustments recover 80% of lost profit margin but forgo the remaining 20% due to systematic under-adjustment.

Self-Serving BiasWhy is there so much impasse in negotiations? Enormous

private and public resources expended:-Civil litigation, labor disputes, domestic strife

Standard explanation: Imperfect info, people use costly delays to signal their reservation value

Alternative explanation: Self serving bias the tendency to view overestimate what is fair in a direction that serves oneself

Assessments of fairness are endogenous

Self-Serving BiasThe self-serving bias impedes negotiations in 3 ways:

1) If people estimate alternatives in self-serving ways, this eliminates some of the mutual benefit zone

2) If people believe that their notion of fairness is impartial and shared by both sides, then they will interpret the other sides bargaining not as an attempt to get what they see as fair, but as ultra-aggressive.

People care not just about outcomes, but intentions.

3) Loss aversion around a fairness point implies negotiators are strongly averse to settling even slightly below the fair point

Where does the self-serving bias come from?

Above-average effect / fundamental attribution error the tendency to overemphasize ones own personality as responsible for ones successes

-Well more than of respondents rate themselves in the top of driving ability, ethics, managerial prowess, teaching ability

-When married couples estimate the fraction of chore contributions they make, the numbers typically add to more than 100 %

Holds for groups as well asked students from each college to watch a football game between the two schools and count the number of penalties committed huge discrepancy - like they each saw a different game

Self Serving BiasExperiment: Based on a tort case of an injured motorcyclist suing

an automobile driver.

Subjects on both sides read the same original info.

Defendant subjects were paid an extra $10 and negotiated out of that. If subjects fail to reach an agreement, they each had to give up $2.50 in court costs

Before negotiating, subjects were asked to guess what they thought the judge actually awarded in the real case

Self Serving BiasSubjects clearly had self-serving biases plaintiffs predictions

about what the judge awarded were higher than the defendants

Moreover, the size of the discrepancy between plaintiff and defendant assessments was highly related to Pr (no settlement)

However, this doesnt get at causation. In another condition, subjects learned their role (whether they were plaintiff or defendant) AFTER they read they case and guessed what the judge awarded

Self Serving BiasWhen subjects knew their roles initially, and then read the info,

28% of negotiations broke down and failed to settle

When subjects learned their roles after reading the info, only 6% of negotiations broke down

This suggests that the bias results from selective information processing plaintiffs overrate the importance of evidence favoring the plaintiff and vice versa

De-biasing had no effect subjects told ahead of time of the bias behaved similarly

Self Serving BiasReal world example public school teacher contract negotiations

Commonplace for both sides to make references to agreements in comparable communities

Average salary in communities listed by the union was significantly higher than salary in districts listed by the school board

Districts where the average salary listed by the union was $1000 greater than ones listed by the school board were 49% more likely to strike

Experience of either the union or board was did not eliminate the bias