LANGUAGE AND THOUGHT

1
LANGUAGE AND THOUGHT

• PSYCHOLOGY OF PREDICTION 3

2
Representativeness Heuristic (Kahneman Tversky,
1972)

• Heuristic for estimating probability based on
  similarity judgements. Similarity is another
  basic cognitive process (like structure of
  memory).

3
Representativeness HeuristicDefinition

• A person using the representativeness heuristic
  evaluates the probability of an uncertain event,
  or a sample, by the degree to which it
• (i) is similar in essential properties to its
  parent population
• (ii) reflects the salient features of the process
  by which it is generated

4
Representativeness HeuristicJustification for
Use

• Similarity and probability are often highly
  related, so representativeness is a good
  heuristic most of time. But, like availability,
  it leads to systematic, predictable biases for
  certain tasks.

5
The Tom W Experiments Kahneman and Tversky
(1972)

• Subjects read a description of Tom W. Written
  by a psychologist when Tom was in high school.
• "Tom W. is of high intelligence although lacking
  in true creativity. He has a need for order and
  clarity, and for neat, tidy systems in which
  every detail fits in the appropriate place. His
  writing is rather dull and mechanical,
  occasionally enlivened by corny puns and flashes
  of the imagination of the sci-fi type. He has a
  strong drive for competence. He seems to have
  little feeling or sympathy for other people and
  does not enjoy interacting with others.

6
The Tom W Experiments (Cont.)

• Question
• How likely is it that Tom is a graduate student
  in
• Humanities
• Computer Science
• 95 say Computer Science more probable

7
The Tom W Experiments (Cont.)

• BUT
• there are 3 times as many graduate students in
  humanities as in CS (base rate)
• information is likely to be unreliable (because
  old, etc.)
• When info is unreliable, should not revise belief
  much away from base rate (normative model
  Bayes theorem).

8
The Tom W Experiments (Cont.)

• Subjects show general tendency to ignore base
  rates
• Subjects use representativeness (descriptive
  model).
• Tom W. is highly representative of CS graduate
  students (parent distribution 1) - not
  representative of Humanities graduate students
  (parent distribution 2).
• Thus believe Tom is a CS graduate student.
• Representativeness ignores base rates.

9
Representativeness HeuristicDefinition Revisited

• The second part of the definition of the
  representativeness heuristic refers to the
  process by which an event or a sample is generated

10
Representativeness Processes and Outcomes

• Problem
• On each round of a game, 20 1 coins are
  distributed at random between 5 students
• Will there be more rounds of Type 1 or Type 2
  after playing the game 100 times?
• Person Type 1 Type 2
• Jim 3 coins 4 coins
• Sue 4 coins 4 coins
• Mary 5 coins 4 coins
• Pat 4 coins 4 coins
• Chris 4 coins 4 coins

11
Representativeness Processes and Outcomes

• Type 2 is more probable, but Type 1 chosen much
  more often
• Reason We expect randomness to produce
  perturbations. Type 1 sample is more
  representative of this process than Type 2.

12
The Conjunction FallacyTversky Kahneman (1982)

• 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.
• Which of the following statements about Linda is
  more probable?
• She is a bank teller
• She is a bank teller who is active in the
  feminist movement.

13
The Conjunction FallacyWhy is it a Fallacy?

• Anyone who is a bank teller who is active in the
  feminist movement is also a bank teller.
• So, if Linda is a bank who is active in the
  feminist movement, she is also a bank teller.
• But, she could also be a bank teller but not
  active in the feminist movement.
• So, it is more likely that she is a bank teller
  than an bank teller who is active in the feminist
  movement

14
Maternity Hospital Problem

• A certain town is served by 2 hospitals. In the
  larger hospital about 45 babies are born each
  day. In the smaller hospital about 15 babies are
  born each day. As you know, about 50 of all
  babies are boys. The exact percentage of baby
  boys varies from day to day, however. Sometimes
  it will be higher than 50, sometimes lower. For
  a period of a year, each hospital recorded the
  days on which more than 60 of the babies born
  were boys.
• Which hospital do you think recorded more such
  days? Why?

15
Maternity Hospital Problem Results
16
Judgements by and of Representativeness

• Judgements by representativeness
• Tom W, Linda
• People are judged to be members of groups because
  they seem representative of them
• Judgements of representativeness
• Maternity hospital problem
• Small samples are taken to be representative of
  the population from which they are drawn.
• These two uses of representativeness are
  logically independent of one another

17
Representativeness and the Gamblers Fallacy

• Representativeness can also explain the Gambler's
  Fallacy (the belief that an event - e.g., red on
  a roulette table- is likely to come up now
  because it is due e.g., after a run of black).

18
Anchoring and Adjustment

• Final heuristic for estimating probabilities but
  also applies to any quantitative estimate
• Stage 1 Person starts with initial idea of
  answer (anchor) - ball park estimate.
• Anchor may be suggested by memory, or by
  something in environment.
• Stage 2 Person adjusts away from initial anchor
  to arrive at final judgement.

19
Why Anchoring and Adjustment might be a Bad Idea

• Problem Adjustments are generally inadequate.
  Final estimate is too closely tied to anchor
• Suggests that you can bias persons estimate if
  you provide the initial anchor

20
Anchoring and AdjustmentAn Experimental Study

• Kahneman Tversky 1974
• Task Suppose you randomly pick the name of one
  of the countries in the UN. What is the
  probability that this country will be an African
  country?

21
Anchoring and AdjustmentAn Experimental Study
(Cont.)

• Stage 1 A wheel-of-fortune is spun and yields a
  random number, 1 - 100.
• Stage 2 The subject is asked whether the actual
  percentage of African countries in UN is higher
  or lower than number in Stage 1 (Supplies anchor)
• Stage 3 The subject is asked to arrive at final
  estimate by moving up or down from Stage 1
  number.

22
Anchoring and AdjustmentAn Experimental Study
(Cont.)

• Results
• When Stage 1 number was 65, mean estimate was 45
• When Stage 1 number was 10, mean estimate was 25
• Subjects are inappropriately swayed by random
  anchor.

23
Anchoring and AdjustmentAn Everyday Example

• Car dealer attempts to anchor you to windscreen
  price on car
• Combat by anchoring on price dealership paid
• Problem with using anchoring and adjustment
  heuristic is sticking too close to bad anchor.

24
Curing Anchoring and Adjustment

• Be aware of the problem - try to choose different
  anchor and see effect on solution
• Anchor, or be anchored!!
• Get good feedback constantly modify your
  predictions with feedback from environment. Will
  help eliminate effect of bad anchor.

25
Conclusions

• There is a large literature on hypothesis testing
  and prediction showing subjects are errorful
  (deviations from normative model (e.g. logic,
  probability theory
• Errors are not random - they all show the same
  biases - suggests common causes (heuristics).
• Does use of heuristics mean subjects are
  stupid??? NO!

26
Conclusions

• We use heuristics because they are generally
  useful, but they predictably get us into trouble
  on certain (well-studied) tasks
• Heuristics help us avoid
• information processing limitations (e.g., lack of
  STM capacity, lack of processing power)
• (some) information processing biases (e.g., bias
  in recall, storage), but they produce other
  biases
• time limitations
• Some errors can be avoided by education,
  feedback, seeking multiple perspectives. It is
  worth avoiding these biases when correct results
  are important.