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Flashcards in Judgement Deck (47)
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1

What theories do subjective probability judgements form part of?

Subjective Expected Utility theories of normative decision-making

2

People make judgements on the basis of...

...heuristics

3

What are heuristics?

Mental rules of thumb, shortcuts
Don't always work
Help us to get to a reasonable estimate of the likelihood of something happening

4

What is the availability heuristic?

Judging probability by ease with which things come to mind

5

What are the pros of the availability heuristic?

- frequency of events happening is a good tally for how often things come to mind

6

Frequency isn't the only thing that can affect the availability of info. What can this lead to?

Biased judgements

7

Which research highlights the limitations of the availability heuristic?

Tversky & Kahneman (1974) – asked students “does ‘r’ appear more in 1st/3rd position?” --> most pps said 1st position because it is easier to recall words from memory by their 1st letter than their 3rd letter

Slovic, Fischhoff & Lichtenstein (1979) – pps were given a list of causes of death & had to estimate the frequencies of each cause --> pps overestimated ones that were heavily reported in newspapers (murder, fires) & underestimated ones that were rarely reported (emphysema, stomach cancer)

8

Why is Slovic, Fischhoff & Lichtenstein's (1979) study good?

It uses source material - we know the correct answers (of the causes of death) & can compare them to pps' answers

9

What is the representativeness heuristic?

An assessment of the degree of correspondence between an instance & a category

10

What helps to drive our likelihood judgement?

How much a set of circumstances looks like another set that has been previously experienced

11

What are limitations of the representativeness heuristic?

X other things can influence representativeness – it doesn’t always work, is tied to stereotypes

X it doesn’t rely on all the info out there, only a small sub-set --> this can affect our judgements

X can lead to the conjunction fallacy

12

What is the conjunction fallacy?

When we assume that specific conditions are more probable than a single general one

The conjunction of 2 events can’t be more probable that either of its constituent events

13

Which researcher/s studied the conjunction fallacy?

Tversky & Kahneman (1983) - the Linda problem

14

What did Tversky & Kahneman's (1983) study involve?

Pps were shown a description about 'Linda'...
“Linda is 31 years old, single, outspoken & very bright. She majored in philosophy. As a student, she was deeply concerned about issues of discrimination & social justice, & also participated in anti-nuclear demonstrations. Put these statements about Linda in order of probability…”
a) Linda is active in the feminist movement
b) Linda is a bank teller
c) Linda is a bank teller & active in the feminist movement

15

What did Tversky & Kahneman (1983) find?

Pps rated Linda as a feminist quite highly, a bank teller quite low, & a feminist + bank teller in the middle
This breaks the rules of probability because feminist bank tellers are a sub-set of ‘bank tellers’ – it must be more probable that she is a bank teller than a feminist bank teller

16

What is 'anchoring'?

A cognitive bias

When we rely too heavily on the first piece of info we are given (the 'anchor') when making decisions

17

What researchers studied anchoring? (x2)

Tversky & Kahneman (1974)

Lichtenstein et al. (1978)

18

What did Tversky & Kahneman's (1974) study involve?

Pps had to estimate the answer to sums under time pressure, either...
a) 1x2x3x4x5x6x7x8
b) 8x7x6x5x4x3x2x1

19

What did Tversky & Kahneman (1974) find?

Mean estimates:
Group a) = 512
Group b) 2,250

Pps anchored on the first values they were given & didn't adjust their estimates appropriately

20

What did Lichtenstein et al.'s (1978) study involve?

Pps estimated the frequencies of 40 causes of death; they were given an answer, either...
a) 50,000 deaths by motor vehicle accidents
b) 1,000 deaths by electrocution

21

What did Lichtenstein et al. (1978) find?

Group a)'s estimates were higher - they had adjusted down from the high value they were given

Group b) had adjusted upwards, starting with lower frequency estimates

22

What are Bayesian reasoning problems?

Reasoning problems that we solve using base theorem

Involves updating probabilities on the basis of additional info

23

What can Bayesian reasoning be influenced by?

Representativeness

24

What is the base rate fallacy?

If we are presented with base rate info (generic info) & specific info, we tend to ignore the base rate info & focus on the specific info

25

Kahneman & Tversky (1973) told pps that there were 30 engineers & 70 lawyers in a sample. Pps were given a personality description of a person and asked what the probability of the person being an engineer was.
What did pps do?

The description was stereotypical of an engineer.

Pps focused on the stereotypical/specific info & not on the base rate info (the actual number of engineers in the sample)

--> said that there was a higher chance of the description being about an engineer

26

Kahneman & Tversky (1973) found that even if pps were given a neutral description (30/70)...

...pps still tended to say there was a 50% chance of the description being about an engineer (ignored the base rate info)

27

What type of info do we tend to get over-influenced by?

Stereotypical info
Info that gets representativeness

28

What is an example of a 'probability format' problem?

"The probability of breast cancer is 1% for women at age 40 who have routine screenings. If she has breast cancer, there is an 80% probability that she will get a positive mammography. If she doesn’t have breast cancer, there is a 9.6% probability that she will get a positive mammography. A woman in this age group had a positive mammography in routine screening, what is the probability that she actually has breast cancer?”

29

How do pps normally respond to this probability format problem?

"The probability of breast cancer is 1% for women at age 40 who have routine screenings. If she has breast cancer, there is an 80% probability that she will get a positive mammography. If she doesn’t have breast cancer, there is a 9.6% probability that she will get a positive mammography. A woman in this age group had a positive mammography in routine screening, what is the probability that she actually has breast cancer?”

Most common answer = 70-80%

Normative Bayesian answer = 7.8%

30

Why do pps respond like this?

"The probability of breast cancer is 1% for women at age 40 who have routine screenings. If she has breast cancer, there is an 80% probability that she will get a positive mammography. If she doesn’t have breast cancer, there is a 9.6% probability that she will get a positive mammography. A woman in this age group had a positive mammography in routine screening, what is the probability that she actually has breast cancer?”

Pps focus on the hit rate (80%) at the expense of the base rate info (1%)