Heuristics &amp; Biases

1 / 19

# Heuristics & Biases - PowerPoint PPT Presentation

Heuristics &amp; Biases. Bayes Rule. Prior Beliefs. Posterior Probability. Evidence. Medical Test. In the 1980’s in the US, a HIV test was used that had the following properties: There were 4% false positives There were 100% true positives About 0.4% of the male population was HIV positive

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Heuristics & Biases

Bayes Rule

Prior Beliefs

Posterior Probability

Evidence

Medical Test
• In the 1980’s in the US, a HIV test was used that had the following properties:There were 4% false positivesThere were 100% true positives
• About 0.4% of the male population was HIV positive
• If a man tested HIV positive, what is the probability he is actually HIV positive?
Representation
• P( positive | no HIV ) = .04 (4% false positives)
• P( positive | HIV ) = 1 (100% true positives)
• P( HIV ) = .004 (0.4% HIV positive rate)
• want: P( HIV | positive ) = ?

HIV no HIV

Positive

Negative

P( positive | HIV)P( HIV ) P(positive | noHIV )P( noHIV )

P( negative | HIV)P( HIV ) P(negative | noHIV )P( noHIV)

Representation
• P( positive | no HIV ) = .04 (4% false positives)
• P( positive | HIV ) = 1 (100% true positives)
• P( HIV ) = .004 (0.4% HIV positive rate)
• want: P( HIV | positive ) = ?

HIV no HIV

P( positive | HIV)P( HIV )= P(positive | noHIV )P( noHIV )=

(1)(.004) = .004 (.04)(.996) = .03984

P( negative | HIV)P( HIV )= P(negative | noHIV )P( noHIV)=

(0)(.004) = 0 (.96)(.996) = .95616

Positive

Negative

Solution
• P( HIV | positive ) = .004 / ( .004 + .03984 ) = .091

HIV no HIV

P( positive | HIV)P( HIV )= P(positive | noHIV )P( noHIV )=

(1)(.004) = .004 (.04)(.996) = .03984

P( negative | HIV)P( HIV )= P(negative | noHIV )P( noHIV)=

(0)(.004) = 0 (.96)(.996) = .95616

Positive

Negative

The Taxi Problem: version 1
• A witness sees a crime involving a taxi in Carborough. The witness says that the taxi is blue. It is known from previous research that witnesses are correct 80% of the time when making such statements.
• What is the probability that a blue taxi was involved in the crime?
The Taxi Problem: version 2
• A witness sees a crime involving a taxi in Carborough. The witness says that the taxi is blue. It is known from previous research that witnesses are correct 80% of the time when making such statements.
• The police also know that 15% of the taxis in Carborough are blue, the other 85% being green.
• What is the probability that a blue taxi was involved in the crime?
Normative Model
• Bayes rule tells you how you should reason with probabilities – it is a prescriptive (i.e., normative) model
• But do people reason like Bayes?

(Tversky & Kahneman)

• Bayes rate neglect
• Conservatism
Base Rate Neglect (2)
• Kahneman & Tversky (1973).

group A: 70 engineers and 30 lawyers

group B: 30 engineers and 70 lawyers

• What is probability of picking an engineer in group A and B? Subjects can do this …
“Jack is a 45 year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues and spends most of his free time on his many hobbies, which include home carpentry, sailing, and mathematical puzzles”

What now is probability Jack is an engineer?

Estimates for both group A and group B was P = .9

Provide some evidence …
Tversky & Kahneman

Much of decision making may be based on:

Biases and heuristics (mental short-cuts)

Lowers cognitive load, but more errors

 Representativeness heuristic

 Availability heuristic

All the families having exactly six children in a particular city were surveyed. In 72 of the families, the exact order of the births of boys and girls was:

G B G B B G

What is your estimate of the number of families surveyed in which the exact order of births was:

B G B B B B

Answer: a) < 72 b) 72 c) >72

Representativeness Heuristic

The sequence “G B G B B G” is seen as

A) more representative of all possible birth sequences.

B) better reflecting the random process of B/G

A coin is flipped. What is a more likely sequence?

A) H T H T T H

B) H H H H H H

A) #H = 3 and #T = 3 (in some order)

B) #H = 6

Gambler’s fallacy: wins are perceived to be more likely after a string of losses

Does the “hot hand” phenomenon exist?

Most basketball coaches/players/fans refer to players having a “Hot hand” or being in a “Hot zone” and show “Streaky shooting”

However, there is little statistical evidence that basketball players switch between a state of “hot hand” and “cold hand”

People often see structure in sequences that are statistically purely random (and nonchanging)

(Gilovich, Vallone, & Tversky, 1985)

Availability Heuristic
• Are there more words in the English language that begin with the letter V or that have V as their third letter?
• What about the letter R, K, L, and N?

(Tversky & Kahneman, 1973)

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Rate the likelihood that the following statements about Linda are true:

a) Linda is active in the feminist movement

b) Linda is a bank teller

c) Linda is a bank teller and is active in the feminist movement

CONJUNCTION FALLACY

Are heuristics wrong?

No, we use mental shortcuts because they are often right.

Availability and representativeness are often ecologically valid cues.