Lecture 10- judgement bias 2

Question 1

what is a cognitive heuristic

Answer 1

Heuristics: mental strategies (‘rules of thumb’) used under ‘uncertain conditions’
*e.g., tasks on which outcome is unknown
when outcomes are uncertain- as heuristics are rules

stratergies to make judgements easier - helps simplify the cognitive load

Question 2

why are cognitive heuristics used

Answer 2

Used due to cognitive limitations
Working memory and attention

Used due to external constraints
e.g., limited time
Used due to motivational constraints

Lack of interest in or understanding of task

Question 3

what does using cognitive heuristics lead to

Answer 3

Using cognitive heuristics involves not taking (sufficient) account of relevant information, which leads to biased judgements or decisions

e.g., not taking sufficient account of colleague’s cultural background might lead to inaccurately assessing them as being unfriendly or aloof
Relying too much on information that’s easy to retrieve from memory (e.g., stereotype-consistent) rather than taking time to find out about colleague’s characteristics

Question 4

who created the first theory of CHB

Answer 4

Kahneman and Tversky (1970s and 1980s):

Challenged dominance of normative (rational) models of thinking

Question 5

WHAT IS A COGNITIIVE heuristic

Answer 5

Cognitive heuristics: reflexive ‘mental operations’ used to make complex tasks manageable
But, heuristic nature typically leads to bias

We rely on what we know already

Question 6

what did Kahneman and Tversky (1970s and 1980s) want to challenge with their CHB

Answer 6

Challenged dominance of normative (rational) models of thinking

e. g., subjective expected utility theory
e. g., making decisions using probabilistic reasoning
e. g., weighing up all ‘pros’ and ‘cons’ of task

Question 7

what were the 3 cog biases discovered by K+T

Answer 7

Identified three cognitive heuristics (K & T):
Availability:
Judging something based on ease of recall or mental simulation of consistent evidence/info

Representativeness:
Judgements based on similarity between seemingly-related things
e.g., personality characteristics and ‘star-sign’

Anchoring and adjustment:
Judgements based on numerical information
Bias due to insufficiently ‘adjusting’ from ‘anchor’

Question 8

WHAT DID K+T suggest

Answer 8

Notion that thinking under conditions of uncertainty due not to extensive information-processing but use of a few simplifying mental strategies (shortcuts) ‘came of age’
‘Conditions of uncertainty’: risk perception
Propose cognitive model of bias in judgement and decision-making (JDM)
Bias due to simplified cognitive information-processing

Question 9

why do we have biases

Answer 9

Propose cognitive model of bias in judgement and decision-making (JDM)
Bias due to simplified cognitive information-processing

when we dont know outcomes of decisions in advance - due to oversimplified cog processing

Question 10

what is availability

Answer 10

judging something based on the ease of recall or mental sity of consistent evidence / info
Memory retrieval or mental simulation of event-consistent information leads to bias

Question 11

explain the evidence leading to the heuristic if availability

Answer 11

Research (K & T, 1981; T & K, 1982):
Ps that read newspaper article about house fire, overestimated chance of involvement in incident
Fire-service data used as criterion against which judgement bias evaluated
Reading article served as ‘memory-prime’ for event-consistent information, which led to overestimation
Most Ps (wrongly) judged that more English words start with ‘R’ than have ‘r’ as third letter
3 times as many words with ‘r’ as third letter
Easier to think of examples of words starting with ‘R’

Greater cognitive ‘availability’ of such words because people tend to remember words based on their first letters not third letters

Question 12

what were the conclusions drawn up by K+Ts study on availability

Answer 12

Greater cognitive ‘availability’ of such words because people tend to remember words based on their first letters not third letters

Question 13

what is the cognitive heuristic representitiveness

Answer 13

Violate normative principles (e.g., Bayes’ theorem) when reasoning probabilistically

alot about % and chance of things happening

Question 14

describe Taxi cab’ study (K & T, 1972): of representitiveness

Answer 14

Ps given scenario about hit-and-run incident involving blue or green company taxi
Ps also told proportion of taxis run by blue (15%) and green (85%) companies in city
Ps also told of correctness of eye-witness’ identification of colour of taxi involved (80%)
Ps overestimated probability that blue or green taxi involved in incident
Judging on basis of information in scenario about colour of taxi rather than on proportion of taxis in city
Prior probabilities or base-rate data
Ps ignored or discounted proportion of blue and green taxis in city when judging
Colour of taxi in scenario was taxi most Ps said was involved in incident

ignores % of taxis in city and relys on EWT

Question 15

what is conjunction fallacy for representitiveness

Answer 15

Linked to conjunction fallacy (T & K, 1984)
Conjunction fallacy: thinking error (non-logical) occurring with probabilities
2 (or more) not perfectly correlated things judged as being more likely to occur together (in conjunction) than each thing occurring separately
- a thinking bias
- mathematical probability

Violates ‘extension rule’ of probability theory
Mathematically, probability of 2 (or more) events occurring together has to be less likely than each of these events occurring on their own

Question 16

what does the taxi cab study violate

Answer 16

Violates ‘extension rule’ of probability theory
Mathematically, probability of 2 (or more) events occurring together has to be less likely than each of these events occurring on their own

Question 17

give an example of conjunction fallacy in BU

Answer 17

Only 2% of BU students speak Icelandic
Likelihood of person being a BU student who doesn’t speak Icelandic greater than likelihood of one that does speak it (98% vs. 2%)
Chance of meeting person who’s a BU student who also speaks Icelandic is slim (2 in 100)
So, can’t be more probable that person you meet is BU student that speaks Icelandic than one that doesn’t speak it

Question 18

describe a study abot Representativeness and conjunction fallacy (Linda problem study - K+T ()

Answer 18

Ps given information about characteristics, job and interests of ‘Linda’
e.g., outspoken, CND member, political activist
Ps judged likelihood of information about ‘Linda’
e.g., ‘active in feminist movement’
Most Ps judged 2 pieces of info together as more likely to occur than each piece would separately
e.g., more likely to be a ‘feminist bank teller’ than a ‘feminist’ or a ‘bank teller’

Question 19

Describe the wheel of fortune study

Answer 19

‘Wheel of fortune’ study (T & K, 1974):
Judgements of percentage of African nations in UN affected by ‘random’ number derived from spinning ‘wheel of fortune’ beforehand
Numbers: 1 (‘low anchor’); 64 (‘high anchor’)
Judgements higher when wheel stopped at high anchor value; lower with low anchor value
Overestimation in high-anchor condition
Underestimation in low-anchor condition
Judgements biased in direction of anchor because of insufficient adjustment from it

Question 20

what are the 2 major buases

Answer 20

Overconfidence:
e.g., overestimating ability to do well on test
Typically evident in probabilistic reasoning (thinking you now more than you do )
e.g., confidence in occurrence of future events

2) Over-optimism:
e.g., underestimating time needed to write essay
Both explained by neglect of base-rate data
e.g., actual % of blue and green taxis in city
Both linked with case-specific reasoning
e.g., judging based on single instance/example

Question 21

what is plannning fallacy bias

Answer 21

Underestimate future task duration despite knowing that previous tasks overran
Over-optimism regarding future task duration judgements
Due to: i) neglect of base-rate data, and ii) focusing on aspects of current task
i) e.g., previous task performance
ii) e.g., what’s unique/different about current task

Focus on the task to much - can justtify this

due to priror experience

Question 22

what causes planning fallacy

Answer 22

Due to use of anchoring and adjustment heuristic (Thomas & Handley, 2008)
Previous task duration used as anchor for predictions
Bias due to insufficient adjustment from anchor value
Underestimation with ‘anchor’ of shorter duration
Overestimation with ‘anchor’ of longer duration

Question 23

explain gamblers fallacy

Answer 23

Operational when judging repeated stimuli
e.g., because roulette ball landed on red last 20 times, doesn’t mean it’ll land on black next time
Same probability of ball landing on black or red (50/50) for each spin of wheel
Tendency to look for order or sequences
Misconception of ‘randomness’ or ‘chance’
Based on representativeness heuristic
Don’t consider probability of stimulus occurring
Violation of ‘law of averages’