S2W4Judg Flashcards
Judgement
Deciding the likelihood of various events using incomplete information (likelihood rather than choice).
Inductive Reasoning
Making a conclusion that is probably, but not definitely, true:
All of the swans I have seen are white
I expect that all swans are white
Factors contributing to strength of an inductive argument
Representativeness of observations:
o How well do observations of category represent all members of it
Number of observations
Quality of the evidence
Availability Heuristic
Events that are more easily remembered are judged to be more probable than events that are less easily remembered.
Availability Heuristic
Tversky & Kahneman (1973)
Which are more prevalent in English, words that begin with the letter r or words in which r is the third letter?
70% said more words begin with r
In reality 3x more words have r in the third position
Easier to think of words that start with r than words with r as third letter
Availability Heuristic
Lichtenstein et al (1978)
Participants asked to judge prevalence of causes of death
Given pairs of causes of death and asked to say which was more prevalent
Appendicitis twice as fatal as pregnancy but pregnancy receives more media attention so people got a lot of pairs wrong.
Affect Heurist
Basing a judgement on how much dread one feels (if you’re scared you think it’s more likely to happen).
Illusory Correlation
You believe a correlation is present between two events, but this is false or weaker than assumed.
May be a stereotypes:
Oversimplified generalisation about group
(often negative)
Every time you see someone doing something negative it reinforces your stereotype even though it’s the minority.
Representativeness Heuristic
The probability that A is a member of class B.
Determined by how well properties of A resemble properties usually associated with class B.
Representativeness Heuristic
Kahneman and Tversky, (1974)
Randomly pick one male from population.
Robert, wears glasses, speaks quietly, and reads a lot. Is it more likely that Robert is a librarian or a farmer?
Most people guess librarian
Far more farmers than librarians in population
Ten times more male farmers than librarians (base rate information)
When only base rate is available, people will use this information
When descriptive information is available, people disregard base rate information, causing errors.
Conjunction Rule
Probability of two events cannot be higher than the probability of the single constituents.
Which of the following is more probable?
Linda is a bank teller
Linda is a bank teller and a feminist.
85% chose 2
Participants influenced by repetitiveness heuristic
Ignored the conjunction rule
Law of Large Numbers
The larger the number of individuals randomly drawn from a population, the more representative the resulting group will be of the entire population.
The larger the sample the more representative it is of the population.
Law of Large Numbers
Tversky and Kahneman (1974)
In the large hospital 45 babies are born each day.
In the small one 15 babies are born each day.
Although the overall proportion of genders is 50/50
Which hospital will have more days where 60%+ were girls?
- the large
- the small
- the same
22% picked small
22% picked large
56% picked same
Correct answer: the small hospital
Smaller samples of numbers of individuals less likely to be representative of general population.
Myside Bias
Tendency for people to evaluate evidence and test hypotheses in a way that is biased toward own opinions
Errors occur when people let their own opinions and attitudes influence how they evaluate evidence needed to make decisions.
Myside Bias
Lord et al. (1979)
Gave questionnaire to two groups, one in favour of capital punishment and one against.
Presented them with descriptions of research studies into capital punishment.
Participants rated articles – responses reflected attitudes they had at the start of the study.
Confirmation Bias
Tendency to selectively look for information that conforms to our hypothesis and overlook information that argues against it.
Confirmation Bias
Wason (1960)
2, 4 & 6 conform to a rule.
Write down sets of numbers and reasons for your choice to discover my rule.
I shall tell you whether your numbers conform to the rule or not and then you can tell me the rule.
Most common hypothesis was increasing intervals of two
Actual rule was three numbers in increasing order of magnitude.
Secret is to find sequences that don’t satisfy your rule but do satisfy Wason’s.
Decision Making
Utility Approach
People make a decision resulting in the maximum expected utility (money).
Influenced by Expected Utility Theory
People ignore optimum way of responding e.g. carry on gambling.
Evaluation of utility approach
Advantages:
Specific procedures to determine the “best choice”
Problems:
People aren’t always motivated by money
Many decisions do not maximize the probability of the best outcome
Denes-Raj and Epstein’s (1994) utility approach
Participants received money if they picked a red bean.
Gave participants a choice between picking from a bowl with 1 red and 9 white beans OR a bowl with 7 red beans and 93 white ones.
Many participants chose less-optimal bowl with more beans.
Participants explained that they knew probabilities against them, but felt they had a better chance as more red beans.
Expected emotions
Kermer et al. (2006)
Compared people’s expected emotions with their actual emotions
Given $5 and told they would gain an additional $5 or lose $3 after a coin flip
Participants predicted how their happiness would change if they won or lost
People overestimated the negative effect of losing.
People only slightly overestimated the positive effect of wining.
People forget about coping mechanisms; leads to inefficient decision making.
Incidental emotions
Emotions that are not specifically related to decision-making (may be related to personality, recent experience, or environment).
Incidental emotions
Lerner et al. (2004)
Participants watched one of three film clips and asked to write how they would feel::
A person dying (sadness)
A dirty toilet (disgust)
Fish (neutral)
Then asked to determine price to sell or to buy items.
Those in S or D group willing to sell for less
Those in S group willing to pay more
D = need to expel things
S = need for change
Context
Redelmeier and Sahafir (1995)
Found that adding alternatives to be considered can influence decisions.
Given a patient that was experiencing the same symptoms for each condition:
Should doctor prescribe medication A?
o 72% opted yes
Should doctor prescribe medication A or B?
o 47% opted neither
Faced with more difficult decision lead to making no decision.
Choice Presentation
Decisions depend on how choices are presented.
Choice Presentation
Johnson & Goldstein (2003)
Opt-in/Opt-out procedure for organ donation:
Countries with opt-in procedure: low consent rate (people don’t bother applying)
Countries with opt-out procedure: high consent rate (people don’t bother changing)
Status quo bias: the tendency to do nothing when faced with making a decision.
Framing effect
Tversky and Kahnemann (1981)
Decisions are influenced by how a decision is stated.
Two alternative programs to combat a disease:
A: 200 people saved.
B: 1/3 probability that 600 people will be saved, 2/3 probability that no one will be saved.
C: 400 people will die.
D: 1/3 probability that nobody will die, 2/3 probability that 600 people will die.
A and B are the same as C and D but are worded in terms of life not death.
When situations are framed in terms of gains, people tend toward a risk-aversion strategy.
When situations are framed in terms of losses, people tend toward a risk-taking strategy.
Neuroeconomics
Investigates how brain activation is related to decisions involving gains or losses.
Areas of brain identified as people make decisions playing economic games.
Decisions influenced by emotions, and emotions are associated with activity in specific areas.
Sanfey et al. (2003) Neuroeconomics
Two players: one proposer, one responder.
Proposer given money; makes offer to responder as to how money is split.
If responder accepts, both get money; if rejects, neither get anything.
Utility theory predicts responder should always accept.
20 games:
• 10 with humans
• 10 with computer
Rejected unfair offers from human but not computer.
Less angry with computer
Sanfey et al. (2003) economics brain activity
Right anterior insula (emotional states) activated 3x more when participants rejected offers.
Participants with higher activation to unfair offers rejected more.
Prefrontal cortex (complex cognitive behaviours) also activated, but same for all conditions.
Suggests that:
Emotional goal of resenting unfairness handled by anterior insula
Cognitive goal of accumulating money handled by prefrontal cortex
Reasoning
The process of drawing conclusions.
Deductive Reasoning
Drawing conclusions that are definitely valid provided the assumptions are true.
Based on formal logic but most people don’t use formal logic to solve them.
Based on observation
Syllogisms
Determining whether a conclusion logically follows from premises (statements).
Basic form of deductive reasoning:
Two statements called premises
Third statement called conclusion
Categorical syllogisms
Premises and conclusion all begin with all, no, or some
Categorical syllogisms validity
Syllogism valid when form of syllogism follows logically from its two premises
Does not necessarily mean that the conclusion is true
If two premises of a valid syllogism are true, then the syllogism’s conclusion must be true
Categorical syllogisms belief bias
Tendency to think syllogism is valid if its conclusion is believable
Evan et al. (1983): invalid syllogisms with believable conclusions were judged as valid 71% of the time
Mental models
Specific situation represented in a person’s mind that can be used to determine validity of syllogisms in deductive reasoning.
Create an iconic model of a situation
Generate tentative conclusions about model
Look for exceptions to falsify model
Determine validity of syllogism
Conditional Syllogisms
If I study I’ll get a good grade.
I studied.
Therefore I’ll get a good grade.
vs.
If I study I’ll get a good grade.
I got a good grade.
Therefore I studied.
4 syllogisms with same first premise
P therefore Q (true 97% got correct)
Not Q therefore not P (true 60% got correct)
Q therefore not P (false: 40% got right)
Not P therefore not Q (false 40% got right)
Conditional reasoning
Wason (1966) Four-Card Problem
Each card has letter on one side and number on other
Rule applies to all cards
Select only cards that need to be turned over to decide whether or not the rule is correct
Results:
First card chosen:
o 53% turned over first card
Second card chosen:
o 46% turned over third card (does not test rule)
o 4% turned over fourth card (does test rule – correct)
Falsification principle
To test a rule, it is necessary to look for situations that would falsify the rule
Griggs and Cox (1982) falsification principle
Stated problem in real-world terms:
If a person is drinking beer, then he must be over 18
73% correct: Flip over beer & 16 years old (can test the rule)
People familiar with regulations
Permission schema
“if you are 18, then you are allowed to drink beer” – people fill in the gaps with existing knowledge.
Cheng and Holyoak (1985) permission schema
Tested for the involvement of permission schema in reasoning:
You are an immigration officer at the airport. Among the documents you have to check is a sheet.
It indicates (a list of tropical diseases) (inoculations travelers have received).
You have to make sure that if the form says ‘entering’ on one side, then the other side includes cholera among the list of diseases (to ensure protection).
This is to ensure that entering passengers are protected against the disease.
Which would you have to turn over?
List of diseases condition: 62% correct
Inoculation condition: 91% chose correct cards
