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# Heuristics and Biases - PowerPoint PPT Presentation

Heuristics and Biases. Behavioral and Experimental Economics. Making Decisions under Uncertainty. Many decisions are based on beliefs concerning the likelihood of uncertain events … Who will win the election? How will a \$ trade for a € tomorrow? Is the defendant guilty?

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Heuristics and Biases

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## Heuristics and Biases

Behavioral and Experimental Economics

### Making Decisions under Uncertainty

• Many decisions are based on beliefs concerning the likelihood of uncertain events…

• Who will win the election? How will a \$ trade for a € tomorrow? Is the defendant guilty?

• What determines such beliefs?

• How do people assess the probability of an uncertain event or the value of an uncertain quantity?

### We Use Heuristics

We reduce the complex tasks of assessing probabilities and predicting values to simpler judgmental operations.

Resembles the subjective assessment of physical quantities such as distance or size.

Example of how we judge the distance of an object using clarity. The more sharply the object is seen, the closer it appears to be. This is mostly right but subject to systematic errors.

### Introduction

What is a heuristic?

Why do humans use them?

Do heuristics help or hurt human decision making?

### Definition from Wikipedia

Heuristic ( /hjʉˈrɪstɨk/; or heuristics; Greek: "Εὑρίσκω", "find" or "discover") refers to experience-based techniques for problem solving, learning, and discovery. Heuristic methods are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical. Examples of this method include using a "rule of thumb", an educated guess, an intuitive judgment, or common sense.

In more precise terms, heuristics are strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines.[1]

### Kahneman and Tversky

http://nobelprize.org/nobel_prizes/economics/laureates/2002/kahneman-autobio.html

http://www.dangoldstein.com/dsn/archives/2005/07/amos_tversky_1.html

"Judgment under Uncertainty: Heuristics and Biases," Science,1974.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=890563

### Source of Bias

• This seminal article by Tversky and Kahneman describes three Heuristics people rely on to assess probabilities and predict values:

• Representativeness

• Availability

### Representativeness

• Many of the probabilistic questions with which people are concerned belong to one of the following types:

• 1. What is the probability that object A belongs to class B

• OR

• 2. What is the probability that process B will generate event A

### To answer we rely on Representativeness Heuristic

Probabilities are evaluated by the degree to which A is representative of B…

In other words, the degree to which A resembles B

Is A similar to B?

### Example

Linda is 31 years old. She is single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and equality.

Which is more likely?

1) Linda works in a bank

2) Linda works in a bank and is active in the feminist movement

### Representativeness

It is more likely that Linda works in a bank.

To argue that (2) is more likely is to commit a conjunction fallacy.

Tversky & Kahneman (1983)

Bankers

85% of professional fund managers chose (1)

Feminist Bankers

Feminists

### Representativeness

Source of the error?

The Representative Heuristic (rule of thumb)

People base judgments on how things appear rather than how statistically likely they are.

People are driven by the narrative of the description rather than by the logic of the analysis.

### Representativeness

"The best explanation to date of the misperception of random sequences is offered by psychologists Daniel Kahneman and Amos Tversky, who attribute it to people’s tendency to be overly influenced by judgments of “representativeness.”

Representativeness can be thought of as the reflexive tendency to assess the similarity of outcomes, instances, and categories on relatively salient and even superficial features, and then to use these assessments of similarity as a basis of judgment.

We expect instances to look like the categories of which they are members; thus, we expect someone who is a librarian to resemble the prototypical librarian. We expect effects to look like their causes; thus we are more likely to attribute a case of heartburn to spicy rather than bland food, and we are more inclined to see jagged handwriting as a sign of a tense rather than a relaxed personality.“ Gilovich (1991), page 18

### Example: Steve

“Steve is very shy and withdrawn, invariably helpful, but with little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.”

Farmer

Salesman

Airline pilot

Librarian

physician

### People order by probability and similarity in exactly the same way

Problem: similarity, or representativeness, is not influenced by several factors that SHOULD affect judgments of probability

### Our Insensitivities

• 1. Insensitivity to prior probability or outcomes: what is the base-rate frequency of the outcomes?

• The fact that there are many more farmers than librarians in the popn should enter into any reasonable estimate of the probability that Steve is a librarian rather than a farmer

### Example

Subjects shown brief personality descriptions of several individuals

Subjects asked to assess, for each description, the prob that it belonged to an engineer rather than a lawyer

In one experimental condition, subjects were told that the descriptions had been drawn from a sample of 70 engineers and 30 lawyers

In another condition, 30 engineers and 70 lawyers

### Bayes’ Rule

The odds that any particular description belongs to an engineer rather than to a lawyer should be higher in this condition, where there is a majority of engineers!

(.7/.3)^2 = 5.44

In violation of Bayes’ rule, the subjects in the two conditions produced essentially the same probability judgments.

People ignored the math and looked only at the representativeness!

### Evidence, No Evidence, Worthless Evidence

When given no other information, subjects used the prior probabilities (evidence)

Evidence ignored when given a description + evidence

Evidence ALSO ignored when given a useless description

### Useless Description

“Dick is a 30 year old man. He is married with no children. A man of high ability and high motivation, he promises to be quite successful in his field. He is well liked by his colleagues.”

### Insensitivity 2

2. Insensitivity to sample size

Example: What is the average height of the following group of men?

N = 1000

N = 100

N = 10

How do you make this assessment?

### Sample Size

People failed to appreciate the role of sample size.

As n increases, variability decreases

John Nunley and I both got decent student evaluation scores last year (him: 3.87 me: 3.88

I taught 3 classes and he taught 6

Who is the better teacher?

### Example:

• A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage varies from day to day.

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

The larger hospital (21)

The smaller hospital (210

About the same (that is, within 5 percent of each other) (53)

Discuss

### Representativeness

Posterior OddsUrn is 2/3 Red

Joe’s Sample

8-to-1 Red

Betty’s Sample

16-to-1 Red

Who should be more confident that the urn contains 2/3 red balls and 1/3 white?

Conclusion: People underestimate evidence

Don’t consider sample size.

### Explanation

Intuitive judgments are dominated by the sample proportion and are essentially unaffected by the size of the sample.

### Insensitivity 3

3. Misconceptions of chance (AKA the gambler’s fallacy)

People expect that a sequence of events generated by a random process will represent the essential characteristics of that process even when the sequence is short.

Example: People think that

HTHTTH is more likely than HHHTTT or HHHTH

### Chance

We think that “chance” will be displayed “globally” and “locally”

Imagine a roulette wheel at Vegas that has fallen on red for the last five spins…

The next spin MUST be black… Right?

RIGHT?

We think black is “due” because it will look more like a representative sequence than if the wheel spins red.

### More on Chance

We think of chance as a self-correcting process in which a deviation in one direction induces a deviation in the opposite direction to restore the equilibrium.

In fact, deviations are not “corrected”, rather as the chance process unfolds, they are “diluted.”

### Representativeness: Gambler’s Fallacy

Suppose an unbiased coin is flipped 3 times, and each time it lands on head. If you had to bet \$1,000 on the next toss, what side would you chose? Heads, Tails or no preference?

300 fund managers: no preference = 81%

Of the 29 2010 BEE students:

No preference = 17 of 23 (74%)

Tails = 4 (17%)

What is going on here?

### The Gambler’s Fallacy

Source of the fallacy?

The coin has no memory and each side is equally likely

You are betting on a SINGLE flip, not an ENTIRE sequence

### The Gambler’s Fallacy

The Gambler’s Fallacy and the Hot Hand: Empirical Data from Casinos

Rachel Croson and James Sundali

Journal of Risk and Uncertainty (2005)

Abstract

Research on decision making under uncertainty demonstrates that intuitive ideas of randomness depart systematically from the laws of chance. Two such departures involving random sequences of events have been documented in the laboratory, the gambler’s fallacy and the hot hand. This study presents results from the field, using videotapes of patrons gambling in a casino, to examine the existence and extent of these biases in naturalistic settings. We find small but significant biases in our population, consistent with those observed in the lab.

### Insensitivity 4

4. Insensitivity to predictability:

if predictability is nil, the same prediction should be made in all cases.

Examples: which company will be profitable? What is the future value of a stock? Who will win the football game?

What info do you have to make this assessment?

### Predictions are often made by Representativeness

Three descriptions of a company: favorable, mediocre, poor.

If mediocre description, mediocre prediction.

The degree to which the description is favorable is unaffected by the reliability of that description or by the degree to which it permits accurate description.

Predictions insensitive to reliability of evidence.

### Insensitivity 5

5. Illusion of validity: The unwarranted confidence which is produced by a good fit between the predicted outcome and the input information

People are more confident in their predictions if they perceive that the inputs look more like the outputs-no regard for limits of predictability

### Example: Accuracy versus Confidence

People express more confidence in predicting the final GPA of a student whose first-year record consists entirely of B’s than in predicting the GPA of a student whose first year record includes many A’s and C’s.

Basic statistics tells us we can make better predictions if we have independent inputs rather than redundant or correlated inputs

### Insensitivity 6

6. Misconception of regression: people don’t understand regression toward the mean.

Consider two variables X and Y which have the same distribution.

If one selects individuals whose average X score deviates from the mean of X by k units, then the average of their Y scores will usually deviate from the mean of Y by less than k units.

Note: this was first documented by Galton more than 100 years ago.

### Examples throughout life

Comparison of height of fathers and sons

Intelligence of husbands and wives

Performance of individuals on consecutive examinations

1. They do not expect regression in many contexts

2. When they do recognize it they invent spurious causal explanations.

### Pilot Training Example(Tversky and Kahneman (1974))

Pilot instructors note that…

Praise for an exceptionally smooth landing is typically followed by a poor landing on next try.

Criticism for a rough landing is typically followed by an improvement on next try.

Conclusion: Verbal rewards are detrimental to learning, while verbal punishments are beneficial.

### Misconception of Regression

Probability

What’s more likely? Improvement or an even worse performance?

Mean

Performance

### Availability

People assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind

Example: one may assess the risk of a heart attack among middle-aged people by recalling such occurrences among one’s acquaintances

### Predictable Biases from reliance on Availability

• 1. Biases due to retrievability of the data: a class whose instances are easily retrieved will seem bigger than a similarly-sized class whose instances are less retrievable

• Car crashes versus airplane crashes

• Familiarity: example lists of famous people

• Salience: seeing a house burning

• Recent-ness

### Bias of Availability 2

2. Biases due to the effectiveness of a search set: searching for words by first letter

Example: Suppose one samples a word (of three letters or more) at random from an English text. Is it more likely that the word starts with r or that r is the third letter?

### Bias of Availability 3

3. Biases of Imaginability

### Bias of Availability 4

4. Illusory Correlation:

Example mental patients with clinical diagnosis and drawings.

1. What are the last four digits of your telephone number?

2. Is the number of physicians in Wisconsin higher or lower than that number?

3. What is your best guess as to the number of physicians in Wisconsin?

### Anchoring

Mean guesses of number of physicians

Last 4 digits of telephone #

Sources: “Behaving Badly” (2006) and B.E. Class Survey

### What is going on?

Anchoring – the tendency to grab hold of irrelevant and subliminal inputs in the face of uncertainty.

If people are “rational,” there should be no difference between those who happen to have high telephone numbers and those who have low ones

### Subsets of Anchoring

Adjustments to initial estimates are typically insufficient:

Example: subjects were asked to estimate various quantities, stated in percentage (for example, the percentage of African countries in the UN).

For each quantity, a number between 0 and 100 was determined by spinning a wheel in the subject’s presence.

Subjects first indicated whether that number was higher or lower than the value and then to estimate the value by moving upward or downward from the given number.

Arbitrary starts had marked effect on estimates

### Anchoring occurs also when the subject bases his estimate on some incomplete computation

• Example:

• estimating 8x7x6x5x4x3x2x1 versus 1x2x3x4x5x6x7x8

• To do this fast, take a few steps and extrapolate, but because adjustments are insufficient, the procedure leads to underestimation that is predictable in each sequence

• median for ascending: 512

• Median for descending: 2,250

### Subsets of Anchoring 2

• Biases in the evaluation of conjunctive and disjunctive events: example:

• Simple events: draw a red marble from a bag with 50 percent red and 50 percent white

• Conjunctive: draw a red marble 7 times in succession with replacement from a bag containing 90% red

• Disjunctive: draw a red marble at least once in 7 successive tries with replacement from a bag containing 10 percent red.

### Subset Anchoring 3

Biases in the evaluation of conjunctive and disjunctive events

Study where subjects were given the opportunity to bet on one of two events.

Three types of events were offered:

### Types of Events

1. Simple events: drawing a red marble from a bag that is 50% red and 50% white

2. Conjunctive events: drawing a red marble 7 times in succession with replacement from a bag containing 90% red and 10% white

3. Disjunctive events: drawing a red marble at least once in 7 successive tries with replacement from a bag containing 10% red and 90% white

### What people did

Majority preferred to bet on the conjunctive event (prob is .48) rather than the simple event (prob is .50)

Subjects preferred to bet on simple event rather than the disjunctive event (prob .50 versus prob .52)

Most bet on the less likely event!

### Implications

• People use the simple probability to start and adjust from there, but they don’t adjust enough

• Note that :

• Pr(disjunctive) > Pr(simple)>Pr(conjunctive)

• Due to anchoring: pr of conjunctive is overestimated and pr of disjunctive is underestimated

### Other Heuristics

Framing

Hindsight bias

Black Swans

The Affect Heuristic

Scope Neglect

Overconfidence

Bystander Apathy

### Framing

Framing refers to a situation whereby we fail to see through the way in which information is provided to us.

In our pretest, I presented one question twice – two different frames.

### Framing

The U.S. is preparing for the outbreak of an unusual disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. The scientific estimates of the consequences of the programs are as follows:

If program A is adopted 200 people will be saved.

If program B is adopted there is a 1/3 probability that 600 people will be saved, and a 2/3 probability that no one will be saved.

Which program do you choose?

### Framing

The U.S. is preparing for the outbreak of an unusual disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. The scientific estimates of the consequences of the programs are as follows:

If program C is adopted 400 people will die.

If program D is adopted there is a 1/3 probability that nobody will die, and a 2/3 probability that 600 people will die.

Which program do you choose?

### Framing

Live prob. Die prob.

Program A200 14001

Program B600 1/36002/3

Program C200 14001

Program D600 1/36002/3

Consistent Preferences imply:

If you pick A over B, you should pick C over D

Question #9

Question #12

### Framing at UCD

Over-Optimism (Over Confidence) Bias

### Do you expect to perform above or below average in this course? 79% say above

AboveAverageBelow

20136067

### Results for over 300 fund managers

Over-Optimism Bias

Source: Global Equity Strategy, “Behaving Badly” (2006)

### Sources of Over-Optimism Bias?

Illusion of Control (people think they can influence the outcome)

Illusion of Knowledge (people think they know more than everyone else)

Evolutionary Explanations of Overconfidence?

### Confirmatory Bias

Tendency to look for information that agrees with our prior beliefs.

Examples: - Iraq had weapons of mass destruction- God Exists (or God does not exist)- Corporations are bad- raising taxes lowers growth

### Evolutionary Explanations of Confirmatory Bias

Failures of logic is an effective ploy to win arguments and winning arguments is an adaptive problem

“Arguing, after all, is less about seeking truth than about overcoming opposing views…. So while confirmation bias, for instance, may mislead us about what’s true and real, by letting examples that support our view monopolize our memory and perception, it maximizes the artillery we wield when trying to convince someone… Confirmation bias “has a straightforward explanation,” argues Mercier. “It contributes to effective argumentation.”

Source: http://www.newsweek.com/2010/08/05/the-limits-of-reason.html

Frederick, Shane “Cognitive Reflection and Decision Making,” Journal of Economic Perspectives 19(4), 2005.

Three Questions:

- Bat and Ball - Machines and Widgets - Lilly pads

### First CRT Question

A bat and a ball together cost \$1.10. The bat costs \$1.00 more than the ball. How much does the ball cost?

Bat + Ball = 1.10

Ball = Bat -1.00

Bat - Ball = 1.00

Bat + (Bat -1.00) = 1.10

2 Bat = 2.10

Ball = \$.05

Bat = \$1.05

### Second CRT Question

If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?

5 minutes

### Third CRT Question

In a lake, there is a patch of lily pads. Everyday, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half the lake?

47 days !

Sources: Frederick (2006) and B.E. Class Survey

### CRT Scores

Mean CRT Score

Group

0(%)

1(%)

2(%)

3(%)

MIT 2.18 7 1630 48

Princeton 1.63 18 2728 26

Harvard 1.43 20 3724 20

Bostonfireworks

1.53 24 24 26 26

1.99 10 21 29 40

Fund Mngrs

ucd(’13)1.24 (41%) 43% 15% 22% 23%

Two parts of the brain:

X-System (the default) – Reflexive part in charge of perception. It is an effortless, fast parallel-processing system

C-System – Reflective, in charge of thought. It requires deliberate effort to use and is slow, but logical

The CRT measures how easy it is for people to interrupt their X-System style automatic response

### CRT Interpretation

What explains variation in scores across participants?

• People with high CRT scores are more patient

• The CRT was positively correlated with people’s attitude toward risk

### Risk Attitudes and the CRT

% choosing risky option

Gamble

Low CRT

High CRT

\$100 for sure or a 75% chance of \$250

19%28%

\$100 for sure or a 3% chance of \$7,000

8% 21%

Lose \$100 for sure or a 75% chance to lose \$250

54%31%

Lose \$100 for sure or a 3% chance to lose \$7,000

63%28%

Source: Frederick (2005)

### UCD Students

Amounted need to win on toss of a fair die to balance 50% chance of \$100 loss

2011

CRT score of 0 or 1

\$103.14

CRT score of 2 or 3

\$152

### Keynes’ Beauty Contest

You are going to play a game against the others sitting in this room. The game is simply this. Pick a number between 0 and 100. The winner of the game will be the person who guesses the number closest to two thirds of the average number picked. Your guess is:

### Keynes’ Beauty Contest

Under the assumptions of rationality and common knowledge, this is the only Nash equilibrium

x = 2/3*x

x = 0 is only solution

### UCDStudents 2013

0 1 2 15 17 20 21 22 24 27 34 35 36 40 45 47 50 67 68

27 40 50

2/3 average = 2/3(33) = 22

### NPR’s Version of the Beauty Contest on Planet Money

http://www.npr.org/blogs/money/2011/01/14/132906135/ranking-cute-animals-a-stock-market-experiment

### The Monty Hall Problem

You are on a game show. You are offered a choice of one of three doors. Behind two of the doors there is a goat. Behind one of the doors there is a car. Upon you announcing what door you chose, the host of the show opens one of the two doors not selected by you, and reveals a goat. After he has done this, he offers you the opportunity to switch your choice. What should you do, stick or switch?

### Percent Choosing each Option in the Monty Hall Problem

Fund Managers

Mac ‘08

Mac ‘10

Stick

48% 18% 34%

Switch

42% 65% 66%

No Preference

10% 18% 0%

Sources: “Behaving Badly” (2006) and B.E. Class Survey